commit f03ee46538af805e179987c1b8641f0bd698c811 Author: ModelHub XC Date: Thu Sep 17 02:10:24 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: unsloth/granite-4.1-8b-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..7c36646 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,57 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-UD-IQ2_M.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-UD-IQ3_XXS.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-UD-Q2_K_XL.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-UD-Q3_K_XL.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-UD-Q4_K_XL.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-UD-Q5_K_XL.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-IQ4_NL.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q4_1.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-UD-Q6_K_XL.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-UD-Q8_K_XL.gguf filter=lfs diff=lfs merge=lfs -text +imatrix_unsloth.gguf_file filter=lfs diff=lfs merge=lfs -text +granite-4.1-8b-BF16.gguf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..ee1d467 --- /dev/null +++ b/README.md @@ -0,0 +1,620 @@ +--- +base_model: +- ibm-granite/granite-4.1-8b +license: apache-2.0 +library_name: transformers +tags: +- language +- unsloth +- granite-4.1 +--- +> [!NOTE] +> Includes Unsloth **chat template fixes**!
For `llama.cpp`, use `--jinja` +> + +
+

+ Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. +

+
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+
+ + +[![mof-class3-qualified](https://mot.isitopen.ai/modules/mof/assets/badge_class3_qualified.png)](https://mot.isitopen.ai/model/1160) + +# Granite-4.1-8B + + +**Model Summary:** +Granite-4.1-8B is a 8B parameter long-context instruct model finetuned from *Granite-4.1-8B-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. + +- **Developers:** Granite Team, IBM +- **HF Collection:** [Granite 4.1 Language Models HF Collection](https://huggingface.co/collections/ibm-granite/granite-41-language-models) +- **Technical Blog:** [Granite-4.1 Blog](https://huggingface.co/blog/ibm-granite/granite-4-1) +- **GitHub Repository:** [ibm-granite/granite-4.1-language-models](https://github.com/ibm-granite/granite-4.1-language-models) +- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/) +- **Release Date**: April 29th, 2026 +- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) + +**Supported Languages:** +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. + +**Intended use:** +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. + +*Capabilities* +* Summarization +* Text classification +* Text extraction +* Question-answering +* Retrieval Augmented Generation (RAG) +* Code related tasks +* Function-calling tasks +* Multilingual dialog use cases +* Fill-In-the-Middle (FIM) code completions + + + +**Generation:** +This is a simple example of how to use Granite-4.1-8B model. + +Install the following libraries: + +```shell +pip install torch torchvision torchaudio +pip install accelerate +pip install transformers +``` +Then, copy the snippet from the section that is relevant for your use case. + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +device = "cuda" +model_path = "ibm-granite/granite-4.1-8b" +tokenizer = AutoTokenizer.from_pretrained(model_path) +# drop device_map if running on CPU +model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device) +model.eval() +# change input text as desired +chat = [ + { "role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location." }, +] +chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) +# tokenize the text +input_tokens = tokenizer(chat, return_tensors="pt").to(device) +# generate output tokens +output = model.generate(**input_tokens, + max_new_tokens=100) +# decode output tokens into text +output = tokenizer.batch_decode(output) +# print output +print(output[0]) +``` + +Expected output: +```shell +<|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|> +<|start_of_role|>assistant<|end_of_role|>IBM Almaden Research Laboratory, San Jose, California, United States.<|end_of_text|> +``` + + +**Tool-calling:** +Granite-4.1-8B 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). + +This is an example of how to use Granite-4.1-8B model tool-calling ability: + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +device = "cuda" +model_path = "ibm-granite/granite-4.1-8b" +tokenizer = AutoTokenizer.from_pretrained(model_path) +# drop device_map if running on CPU +model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device) +model.eval() + +tools = [ + { + "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"] + } + } + } +] + +# change input text as desired +chat = [ + { "role": "user", "content": "What's the weather like in Boston right now?" }, +] +chat = tokenizer.apply_chat_template(chat, \ + tokenize=False, \ + tools=tools, \ + add_generation_prompt=True) +# tokenize the text +input_tokens = tokenizer(chat, return_tensors="pt").to(device) +# generate output tokens +output = model.generate(**input_tokens, + max_new_tokens=100) +# decode output tokens into text +output = tokenizer.batch_decode(output) +# print output +print(output[0]) +``` + +Expected output: +```shell +<|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. +You are provided with function signatures within XML tags: +- +- unsloth +{"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"]}}} + +For each tool call, return a json object with function name and arguments within XML tags: +- +- unsloth +{"name": , "arguments": } +. 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|> +<|start_of_role|>user<|end_of_role|>What's the weather like in Boston right now?<|end_of_text|> +<|start_of_role|>assistant<|end_of_role|> +{"name": "get_current_weather", "arguments": {"city": "Boston"}} +<|end_of_text|> +``` + + + +**Evaluation Results:** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
BenchmarksMetric3B Dense8B Dense30B Dense
+ General Tasks +
MMLU5-shot67.0273.8480.16
MMLU-Pro5-shot, CoT49.8355.9964.09
BBH3-shot, CoT75.8380.5183.74
AGI EVAL0-shot, CoT65.1672.4377.80
GPQA0-shot, CoT31.7041.9645.76
SimpleQA3.684.826.81
+ Alignment Tasks +
AlpacaEval 2.038.5750.0856.16
IFEval Avg82.3087.0689.65
ArenaHard37.8068.9871.02
MTBench Avg7.578.618.61
+ Math Tasks +
GSM8K8-shot86.8892.4994.16
GSM Symbolic8-shot81.3283.7075.70
Minerva Math0-shot, CoT67.9480.1081.32
DeepMind Math0-shot, CoT64.6480.0781.93
+ Code Tasks +
HumanEvalpass@181.7185.3788.41
HumanEval+pass@176.8379.8885.37
MBPPpass@171.1687.3085.45
MBPP+pass@162.1773.8173.54
CRUXEval-Opass@140.7547.6355.75
BigCodeBenchpass@132.1935.0038.77
MULTIPLEpass@152.5460.2662.31
Eval+ Avgpass@167.0580.2182.66
+ Tool Calling Tasks +
BFCL v360.8068.2773.68
+ Multilingual Tasks +
MMMLU5-shot57.6164.8473.71
INCLUDE5-shot52.0558.8967.26
MGSM8-shot70.0082.3271.12
+ Safety +
SALAD-Bench93.9595.8096.41
AttaQ81.8881.1985.76
Tulu3 Safety Eval Avg66.8475.5778.19
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Multilingual Benchmarks and the included languages:
Benchmarks# LangsLanguages
MMMLU11ar, de, en, es, fr, ja, ko, pt, zh, bn, hi
INCLUDE14hi, bn, ta, te, ar, de, es, fr, it, ja, ko, nl, pt, zh
MGSM5en, es, fr, ja, zh
+ +**Model Architecture:** + +Granite-4.1-8B 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. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model3B Dense8B Dense30B Dense
Embedding size256040964096
Number of layers404064
Attention head size64128128
Number of attention heads403232
Number of KV heads888
MLP / Shared expert hidden size81921280032768
MLP activationSwiGLUSwiGLUSwiGLU
Sequence length131072131072131072
Position embeddingRoPERoPERoPE
# Parameters3B8B30B
+ + + +**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 + + \ No newline at end of file diff --git a/granite-4.1-8b-BF16.gguf b/granite-4.1-8b-BF16.gguf new file mode 100644 index 0000000..d060709 --- /dev/null +++ b/granite-4.1-8b-BF16.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9089b3ff9e675f4da976c7b7d0e5418776f6950476894a9b9d4d43a672dfc01 +size 17587418240 diff --git a/granite-4.1-8b-IQ4_NL.gguf 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