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Model: ibm-granite/granite-4.1-3b Source: Original Platform
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---
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license: apache-2.0
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library_name: transformers
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tags:
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- language
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- granite-4.1
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---
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[](https://mot.isitopen.ai/model/1160)
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# Granite-4.1-3B
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<!-- 📣 **Update [10-07-2025]:** Added a *default system prompt* to the chat template to guide the model towards more *professional, accurate, and safe* responses. -->
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**Model Summary:**
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Granite-4.1-3B is a 3B parameter long-context instruct model finetuned from *Granite-4.1-3B-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. Granite 4.1 models have gone through an improved post-training pipeline, including supervised finetuning and reinforcement learning alignment, resulting in enhanced tool calling, instruction following, and chat capabilities.
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- **Developers:** Granite Team, IBM
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- **HF Collection:** [Granite 4.1 Language Models HF Collection](https://huggingface.co/collections/ibm-granite/granite-41-language-models)
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- **Technical Blog:** [Granite-4.1 Blog](https://huggingface.co/blog/ibm-granite/granite-4-1)
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- **GitHub Repository:** [ibm-granite/granite-4.1-language-models](https://github.com/ibm-granite/granite-4.1-language-models)
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- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
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- **Release Date**: April 29th, 2026
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)
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**Supported Languages:**
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English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages.
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**Intended use:**
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The model is designed to follow general instructions and can serve as the foundation for AI assistants across diverse domains, including business applications, as well as for LLM agents equipped with tool-use capabilities.
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*Capabilities*
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* Summarization
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* Text classification
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* Text extraction
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* Question-answering
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* Retrieval Augmented Generation (RAG)
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* Code related tasks
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* Function-calling tasks
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* Multilingual dialog use cases
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* Fill-In-the-Middle (FIM) code completions
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<!-- <todo>Need to test the examples. (especially the tool calling and RAG ones)</todo>
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-->
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**Generation:**
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This is a simple example of how to use Granite-4.1-3B model.
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Install the following libraries:
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```shell
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pip install torch torchvision torchaudio
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pip install accelerate
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pip install transformers
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```
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Then, copy the snippet from the section that is relevant for your use case.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda"
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model_path = "ibm-granite/granite-4.1-3b"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# drop device_map if running on CPU
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
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model.eval()
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# change input text as desired
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chat = [
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{ "role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location." },
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]
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chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
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# tokenize the text
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input_tokens = tokenizer(chat, return_tensors="pt").to(device)
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# generate output tokens
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output = model.generate(**input_tokens,
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max_new_tokens=100)
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# decode output tokens into text
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output = tokenizer.batch_decode(output)
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# print output
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print(output[0])
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```
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Expected output:
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```shell
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<|start_of_role|>user<|end_of_role|>Please list one IBM Research laboratory located in the United States. You should only output its name and location.<|end_of_text|>
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<|start_of_role|>assistant<|end_of_role|>Almaden Research Center, San Jose, California<|end_of_text|>
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```
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<!-- 📣 **Update [2025-10-07]:** Added a *default system prompt* to the chat template to guide the model towards more *professional, accurate, and safe* responses. -->
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**Tool-calling:**
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Granite-4.1-3B comes with enhanced tool calling capabilities, enabling seamless integration with external functions and APIs. To define a list of tools please follow OpenAI's function [definition schema](https://platform.openai.com/docs/guides/function-calling?api-mode=responses#defining-functions).
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This is an example of how to use Granite-4.1-3B model tool-calling ability:
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda"
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model_path = "ibm-granite/granite-4.1-3b"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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# drop device_map if running on CPU
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
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model.eval()
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tools = [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather for a specified city.",
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"parameters": {
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"type": "object",
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"properties": {
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"city": {
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"type": "string",
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"description": "Name of the city"
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}
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},
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"required": ["city"]
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}
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}
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}
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]
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# change input text as desired
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chat = [
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{ "role": "user", "content": "What's the weather like in Boston right now?" },
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]
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chat = tokenizer.apply_chat_template(chat, \
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tokenize=False, \
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tools=tools, \
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add_generation_prompt=True)
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# tokenize the text
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input_tokens = tokenizer(chat, return_tensors="pt").to(device)
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# generate output tokens
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output = model.generate(**input_tokens,
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max_new_tokens=100)
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# decode output tokens into text
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output = tokenizer.batch_decode(output)
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# print output
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print(output[0])
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```
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Expected output:
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```shell
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<|start_of_role|>system<|end_of_role|>You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.
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You are provided with function signatures within <tools></tools> XML tags:
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<tools>
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{"type": "function", "function": {"name": "get_current_weather", "description": "Get the current weather for a specified city.", "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "Name of the city"}}, "required": ["city"]}}}
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</tools>
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For each tool call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
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<tool_call>
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{"name": <function-name>, "arguments": <args-json-object>}
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</tool_call>. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.<|end_of_text|>
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<|start_of_role|>user<|end_of_role|>What's the weather like in Boston right now?<|end_of_text|>
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<|start_of_role|>assistant<|end_of_role|><tool_call>
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{"name": "get_current_weather", "arguments": {"city": "Boston"}}
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</tool_call><|end_of_text|>
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```
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<!-- **Retrieval Augmented Generation:**
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*Coming soon* -->
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**Evaluation Results:**
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<table>
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<!-- <caption><b> All Results</b></caption> -->
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<thead>
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<tr>
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<th style="text-align:left; background-color: #001d6c; color: white;">Benchmarks</th>
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<th style="text-align:left; background-color: #001d6c; color: white;">Metric</th>
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<th style="text-align:center; background-color: #001d6c; color: white;">3B Dense</th>
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<th style="text-align:center; background-color: #001d6c; color: white;">8B Dense</th>
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<th style="text-align:center; background-color: #001d6c; color: white;">30B Dense</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td colspan="5" style="text-align:center; background-color: #FFFFFF; color: #2D2D2D; font-style:italic;">
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General Tasks
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</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MMLU</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">5-shot</td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">67.02</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">73.84</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">80.16</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MMLU-Pro</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">5-shot, CoT</td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">49.83</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">55.99</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">64.09</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">BBH</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">3-shot, CoT</td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">75.83</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">80.51</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">83.74</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">AGI EVAL</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">0-shot, CoT</td>
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<td style="text-align:right; background-color:#DAE8FF; color: #2D2D2D;">65.16</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">72.43</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">77.80</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">GPQA</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">0-shot, CoT</td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">31.70</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">41.96</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">45.76</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">SimpleQA</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">3.68</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">4.82</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">6.81</td>
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</tr>
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<tr>
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<td colspan="5" style="text-align:center; background-color: #FFFFFF; color: #2D2D2D; font-style:italic;">
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Alignment Tasks
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</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">AlpacaEval 2.0</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;"></td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">38.57</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">50.08</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">56.16</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">IFEval Avg</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">82.30</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">87.06</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">89.65</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">ArenaHard</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">37.80</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">68.98</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">71.02</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MTBench Avg</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">7.57</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">8.61</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">8.61</td>
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</tr>
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<tr>
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<td colspan="5" style="text-align:center; background-color: #FFFFFF; color: #2D2D2D; font-style:italic;">
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Math Tasks
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</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">GSM8K</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">8-shot</td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">86.88</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">92.49</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">94.16</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">GSM Symbolic</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">8-shot</td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">81.32</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">83.70</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">75.70</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Minerva Math</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">0-shot, CoT</td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">67.94</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">80.10</td>
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<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">81.32</td>
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</tr>
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<tr>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">DeepMind Math</td>
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<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">0-shot, CoT</td>
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<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">64.64</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">80.07</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">81.93</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td colspan="5" style="text-align:center; background-color: #FFFFFF; color: #2D2D2D; font-style:italic;">
|
||||
Code Tasks
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">HumanEval</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">81.71</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">85.37</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">88.41</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">HumanEval+</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">76.83</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">79.88</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">85.37</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MBPP</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">71.16</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">87.30</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">85.45</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MBPP+</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">62.17</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">73.81</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">73.54</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">CRUXEval-O</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">40.75</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">47.63</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">55.75</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">BigCodeBench</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">32.19</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">35.00</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">38.77</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MULTIPLE</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">52.54</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">60.26</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">62.31</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Eval+ Avg</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">pass@1</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">67.05</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">80.21</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">82.66</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td colspan="5" style="text-align:center; background-color: #FFFFFF; color: #2D2D2D; font-style:italic;">
|
||||
Tool Calling Tasks
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">BFCL v3</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">60.80</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">68.27</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">73.68</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td colspan="5" style="text-align:center; background-color: #FFFFFF; color: #2D2D2D; font-style:italic;">
|
||||
Multilingual Tasks
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MMMLU</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">5-shot</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">57.61</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">64.84</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">73.71</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">INCLUDE</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">5-shot</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">52.05</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">58.89</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">67.26</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MGSM</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">8-shot</td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">70.00</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">82.32</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">71.12</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td colspan="6" style="text-align:center; background-color: #FFFFFF; color: #2D2D2D; font-style:italic;">
|
||||
Safety
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">SALAD-Bench</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">93.95</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">95.80</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">96.41</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">AttaQ</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">81.88</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">81.19</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">85.76</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">Tulu3 Safety Eval Avg</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;"></td>
|
||||
<td style="text-align:right; background-color: #DAE8FF; color: #2D2D2D;">66.84</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">75.57</td>
|
||||
<td style="text-align:right; background-color: #FFFFFF; color: #2D2D2D;">78.19</td>
|
||||
</tr>
|
||||
</tbody></table>
|
||||
|
||||
|
||||
<table>
|
||||
<caption><b>Multilingual Benchmarks and the included languages:</b></caption>
|
||||
<thead>
|
||||
<tr>
|
||||
<th style="text-align:left; background-color: #001d6c; color: white;">Benchmarks</th>
|
||||
<th style="text-align:left; background-color: #001d6c; color: white;"># Langs</th>
|
||||
<th style="text-align:center; background-color: #001d6c; color: white;">Languages</th>
|
||||
</tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MMMLU</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">11</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">ar, de, en, es, fr, ja, ko, pt, zh, bn, hi</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">INCLUDE</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">14</td>
|
||||
<!-- <td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">hindi, bengali, tamil, telugu, arabic, german, spanish, french, italian, japanese, korean, dutch, portuguese, chinese</td> -->
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">hi, bn, ta, te, ar, de, es, fr, it, ja, ko, nl, pt, zh</td>
|
||||
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">MGSM</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: #2D2D2D;">5</td>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: #2D2D2D;">en, es, fr, ja, zh</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
**Model Architecture:**
|
||||
|
||||
Granite-4.1-3B baseline is built on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
|
||||
|
||||
<table>
|
||||
<thead>
|
||||
<tr>
|
||||
<th style="text-align:left; background-color: #001d6c; color: white;">Model</th>
|
||||
<th style="text-align:center; background-color: #001d6c; color: white;">3B Dense</th>
|
||||
<th style="text-align:center; background-color: #001d6c; color: white;">8B Dense</th>
|
||||
<th style="text-align:center; background-color: #001d6c; color: white;">30B Dense</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Embedding size</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">2560</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">4096</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">4096</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Number of layers</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">40</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">40</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">64</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Attention head size</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">64</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">128</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Number of attention heads</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">40</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">32</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">32</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Number of KV heads</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">8</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">8</td>
|
||||
</tr>
|
||||
<!--<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Mamba2 state size</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">-</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Number of Mamba2 heads</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
</tr>-->
|
||||
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">MLP / Shared expert hidden size</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">8192</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">12800</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">32768</td>
|
||||
</tr>
|
||||
<!--<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Num. Experts</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Num. active Experts</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Expert hidden size</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
</tr>-->
|
||||
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">MLP activation</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">SwiGLU</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">SwiGLU</td>
|
||||
</tr>
|
||||
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Sequence length</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">131072</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">131072</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">131072</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;">Position embedding</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">RoPE</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">RoPE</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">RoPE</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;"># Parameters</td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;">3B</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">8B</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;">30B</td>
|
||||
</tr>
|
||||
<!-- <tr>
|
||||
<td style="text-align:left; background-color: #FFFFFF; color: black;"># Active parameters</td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #DAE8FF; color: black;"></td>
|
||||
<td style="text-align:center; background-color: #FFFFFF; color: black;"></td>
|
||||
</tr>-->
|
||||
</tbody></table>
|
||||
|
||||
|
||||
|
||||
**Training Data:**
|
||||
Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities, and (3) a select set of human-curated data.
|
||||
|
||||
**Supervised Fine-Tuning and Reinforcement Learning:**
|
||||
Instruct model has been fine tuned with significantly improved SFT-pipeline and Reinforcement learning pipelines with high quality mix of various datasets as mentioned above. With rigorous SFT-RL cycles we have improved Granite-4.1 model's tool calling, instruction following and chat capabilities. For further details please check our [Granite-4.1 Blog]((https://huggingface.co/blog/ibm-granite/granite-4-1)).
|
||||
|
||||
**Infrastructure:**
|
||||
We trained the Granite 4.1 Language Models utilizing an NVIDIA GB200 NVL72 cluster hosted in CoreWeave. Intra-rack communication occurs via the 72-GPU NVLink domain, and a non-blocking, full Fat-Tree NDR 400 Gb/s InfiniBand network provides inter-rack communication. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
|
||||
|
||||
**Ethical Considerations and Limitations:**
|
||||
Granite 4.1 Instruction Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering multiple languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such cases, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. We urge the community to use this model with proper safety testing and tuning tailored for their specific tasks. To enhance safety in enterprise deployments, we recommend using Granite 4.1 Language models alongside [Granite Guardian](https://huggingface.co/ibm-granite/granite-guardian-4.1-8b), a model designed to detect and flag risks in inputs and outputs across key dimensions outlined in the IBM AI Risk Atlas.
|
||||
|
||||
**Resources**
|
||||
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
|
||||
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
|
||||
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources
|
||||
|
||||
<!-- ## Citation
|
||||
```
|
||||
@misc{granite-models,
|
||||
author = {author 1, author2, ...},
|
||||
title = {},
|
||||
journal = {},
|
||||
volume = {},
|
||||
year = {2024},
|
||||
url = {https://arxiv.org/abs/0000.00000},
|
||||
}
|
||||
``` -->
|
||||
114
chat_template.jinja
Normal file
114
chat_template.jinja
Normal file
@@ -0,0 +1,114 @@
|
||||
{%- set tools_system_message_prefix = 'You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>' %}
|
||||
{%- set tools_system_message_suffix = '\n</tools>\n\nFor each tool call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call>. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.' %}
|
||||
{%- set documents_system_message_prefix = 'You are a helpful assistant with access to the following documents. You may use one or more documents to assist with the user query.\n\nYou are given a list of documents within <documents></documents> XML tags:\n<documents>' %}
|
||||
{%- set documents_system_message_suffix = '\n</documents>\n\nWrite the response to the user\'s input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.' %}
|
||||
{%- if available_tools is defined and available_tools %}
|
||||
{%- set tools = available_tools %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(tools_system_message=tools_system_message_prefix,
|
||||
documents_system_message=documents_system_message_prefix,
|
||||
system_message=''
|
||||
) %}
|
||||
{%- if tools %}
|
||||
{%- for tool in tools %}
|
||||
{%- set ns.tools_system_message = ns.tools_system_message + '\n' + (tool | tojson) %}
|
||||
{%- endfor %}
|
||||
{%- set ns.tools_system_message = ns.tools_system_message + tools_system_message_suffix %}
|
||||
{%- else %}
|
||||
{%- set ns.tools_system_message = '' %}
|
||||
{%- endif %}
|
||||
{%- if documents %}
|
||||
{%- for document in documents %}
|
||||
{%- set ns.documents_system_message = ns.documents_system_message + '\n' + (document | tojson) %}
|
||||
{%- endfor %}
|
||||
{%- set ns.documents_system_message = ns.documents_system_message + documents_system_message_suffix %}
|
||||
{%- else %}
|
||||
{%- set ns.documents_system_message = '' %}
|
||||
{%- endif %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{%- if messages[0].content is string %}
|
||||
{%- set ns.system_message = messages[0].content %}
|
||||
{%- elif messages[0].content is iterable %}
|
||||
{%- for entry in messages[0].content %}
|
||||
{%- if entry.type== 'text' %}
|
||||
{%- if ns.system_message != '' %}
|
||||
{%- set ns.system_message = ns.system_message + '\n' %}
|
||||
{%- endif %}
|
||||
{%- set ns.system_message = ns.system_message + entry.text %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{%- if tools and documents %}
|
||||
{%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message + '\n\n' + ns.documents_system_message %}
|
||||
{%- elif tools %}
|
||||
{%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message %}
|
||||
{%- elif documents %}
|
||||
{%- set ns.system_message = ns.system_message + '\n\n' + ns.documents_system_message %}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{%- if tools and documents %}
|
||||
{%- set ns.system_message = ns.tools_system_message + '\n\n' + ns.documents_system_message %}
|
||||
{%- elif tools %}
|
||||
{%- set ns.system_message = ns.tools_system_message %}
|
||||
{%- elif documents %}
|
||||
{%- set ns.system_message = ns.documents_system_message %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if ns.system_message %}
|
||||
{{- '<|start_of_role|>system<|end_of_role|>' + ns.system_message + '<|end_of_text|>\n' }}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- set content = namespace(val='') %}
|
||||
{%- if message.content is string %}
|
||||
{%- set content.val = message.content %}
|
||||
{%- else %}
|
||||
{%- if message.content is iterable %}
|
||||
{%- for entry in message.content %}
|
||||
{%- if entry.type== 'text' %}
|
||||
{%- if content.val != '' %}
|
||||
{%- set content.val = content.val + '\n' %}
|
||||
{%- endif %}
|
||||
{%- set content.val = content.val + entry.text %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if (message.role == 'user') or (message.role == 'system' and not loop.first) %}
|
||||
{{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val + '<|end_of_text|>\n' }}
|
||||
{%- elif message.role == 'assistant' %}
|
||||
{{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val }}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content.val) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|end_of_text|>\n' }}
|
||||
{%- elif message.role == 'tool' %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != 'tool') %}
|
||||
{{- '<|start_of_role|>user<|end_of_role|>' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- content.val }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != 'tool') %}
|
||||
{{- '<|end_of_text|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|start_of_role|>assistant<|end_of_role|>' }}
|
||||
{%- endif %}
|
||||
32
config.json
Normal file
32
config.json
Normal file
@@ -0,0 +1,32 @@
|
||||
{
|
||||
"architectures": [
|
||||
"GraniteForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"attention_multiplier": 0.015625,
|
||||
"bos_token_id": 100257,
|
||||
"embedding_multiplier": 12.0,
|
||||
"eos_token_id": 100257,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.1,
|
||||
"intermediate_size": 8192,
|
||||
"logits_scaling": 10.0,
|
||||
"max_position_embeddings": 131072,
|
||||
"mlp_bias": false,
|
||||
"model_type": "granite",
|
||||
"num_attention_heads": 40,
|
||||
"num_hidden_layers": 40,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 100256,
|
||||
"residual_multiplier": 0.22,
|
||||
"rms_norm_eps": 1e-05,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 10000000,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.53.3",
|
||||
"use_cache": true,
|
||||
"vocab_size": 100352
|
||||
}
|
||||
7
generation_config.json
Normal file
7
generation_config.json
Normal file
@@ -0,0 +1,7 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 100257,
|
||||
"eos_token_id": 100257,
|
||||
"pad_token_id": 100256,
|
||||
"transformers_version": "4.53.3"
|
||||
}
|
||||
100001
merges.txt
Normal file
100001
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:895bf5f2d7c8b06ca902499567d3c3d9ed30061e4c5ad94bf8216286ca67e2fd
|
||||
size 4991538344
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:de8c9efdaa6f669d595bda8b949213cba92ea69689fd2a27fbceed3d1ebeb2f7
|
||||
size 1814176448
|
||||
370
model.safetensors.index.json
Normal file
370
model.safetensors.index.json
Normal file
@@ -0,0 +1,370 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 3402836480,
|
||||
"total_size": 6805672960
|
||||
},
|
||||
"weight_map": {
|
||||
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
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|
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|
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|
||||
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}
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||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
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||||
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}
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}
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||||
501264
tokenizer.json
Normal file
501264
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
783
tokenizer_config.json
Normal file
783
tokenizer_config.json
Normal file
@@ -0,0 +1,783 @@
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||||
{
|
||||
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||||
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},
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||||
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||||
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||||
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||||
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|
||||
},
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||||
"100263": {
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
},
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100265": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100266": {
|
||||
"content": "<|unused_1|>",
|
||||
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|
||||
"normalized": false,
|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"100267": {
|
||||
"content": "<|start_of_plugin|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100269": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100270": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100271": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100272": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100273": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100274": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100275": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100276": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"100277": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100280": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"100281": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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||||
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||||
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||||
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|
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||||
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||||
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||||
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|
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||||
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||||
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|
||||
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|
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"100350": {
|
||||
"content": "<|unused_81|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"100351": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
},
|
||||
"bos_token": "<|end_of_text|>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|end_of_text|>",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<|pad|>",
|
||||
"padding_side": "left",
|
||||
"tokenizer_class": "GPT2Tokenizer",
|
||||
"unk_token": "<|unk|>"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user