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Model: tiiuae/Falcon3-10B-Base-1.58bit-prequantized Source: Original Platform
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README.md
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---
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library_name: transformers
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tags:
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- bitnet
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- falcon3
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base_model: tiiuae/Falcon3-10B-Base
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license: other
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license_name: falcon-llm-license
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license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
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---
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# Table of Contents
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0. [TL;DR](#TL;DR)
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1. [Model Details](#model-details)
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2. [Training Details](#training-details)
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3. [Usage](#usage)
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4. [Evaluation](#evaluation)
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5. [Citation](#citation)
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# TL;DR
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# Model Details
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This is a 'hacked' version of `tiiuae/Falcon3-10B-Base-1.58bit` where model weight scales have been injected into ternary model weights in order to make the model compatible with fine-tuning
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## Model Description
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- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
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- **Model type:** Causal decoder-only
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- **Architecture:** Pure-transformer - 1.58bit version
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- **Language(s) (NLP):** Mainly English
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- **License:** TII Falcon License 2.0
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# Training details
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The model has been trained following the training strategies from the recent [1-bit LLM HF blogpost](https://huggingface.co/blog/1_58_llm_extreme_quantization) and [1-bit LLM paper](https://huggingface.co/papers/2402.17764).
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For more details about the training protocol of this model, please refer to the Falcon-3 technical report, section *Compression*.
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# Usage
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Currently to use this model you can either rely on Hugging Face transformers library or [BitNet](https://github.com/microsoft/BitNet) library. You can also play with the model using the [falcon-1.58bit playground](https://huggingface.co/spaces/tiiuae/falcon3-1.58bit-playground) (only for the 7B instruct version).
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## 🤗 transformers
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "tiiuae/Falcon3-10B-Base-1.58bit"
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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).to("cuda")
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# Perform text generation
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```
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## BitNet
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```
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git clone https://github.com/microsoft/BitNet && cd BitNet
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pip install -r requirements.txt
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python setup_env.py --hf-repo tiiuae/Falcon3-10B-Base-1.58bit -q i2_s
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python run_inference.py -m models/Falcon3-10B-1.58bit/ggml-model-i2_s.gguf -p "You are a helpful assistant" -cnv
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```
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# Evaluation
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We report in the following table our internal pipeline benchmarks:
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**Note evaluation results are normalized score from v2 leaderboard tasks - reported results of original models in the blogpost are raw scores**
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<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
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<colgroup>
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<col style="width: 10%;">
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<col style="width: 10%;">
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<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
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</colgroup>
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<thead>
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<tr>
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<th>Benchmark</th>
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<th>Llama3-8B-1.58-100B-tokens</th>
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<th>Falcon3-10B-Base-1.58bit</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>IFEval</td>
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<td>17.91</td>
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<td><b>24.89</b></td>
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</tr>
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<tr>
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<td>MUSR</td>
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<td><b>4.87</b></td>
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<td>4.6</td>
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</tr>
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<tr>
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<td>GPQA</td>
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<td>1.83</td>
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<td>1.83</td>
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</tr>
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<tr>
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<td>BBH</td>
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<td><b>5.36</b></td>
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<td>4.44</td>
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</tr>
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<tr>
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<td>MMLU-PRO</td>
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<td><b>2.78</b></td>
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<td>1.36</td>
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</tr>
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<tr>
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<td>MATH</td>
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<td>0.26</td>
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<td><b>0.48</b></td>
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</tr>
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<tr>
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<td>Average</td>
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<td>5.5</td>
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<td><b>6.27</b></td>
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</tr>
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</tbody>
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</table>
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# Citation
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Coming soon ..
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chat_template.jinja
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{# ================= SYSTEM PROMPT ================= #}
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{%- if messages and messages[0]['role'] == 'system' %}
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{%- set system_msg = messages[0]['content'] %}
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{%- set remaining_messages = messages[1:] %}
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{%- elif messages and messages[0]['role'] == 'developer' %}
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{%- set system_msg = messages[0]['content'] %}
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{%- set remaining_messages = messages[1:] %}
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{%- else %}
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{%- set system_msg = "You are Falcon, a helpful AI assistant created by Technology Innovation Institute (TII)." %}
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{%- set remaining_messages = messages %}
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{%- endif %}
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{%- if tools %}
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<|im_start|>system
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{{ system_msg }}
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# Tools
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You may call one or more functions to assist with the user query.
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<tools>
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{%- for tool in tools %}
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{{ tool | tojson }}
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{%- endfor %}
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</tools>
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<|im_end|>
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{%- else %}
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<|im_start|>system
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{{ system_msg }}
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<|im_end|>
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{%- endif %}
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{# ================= FIND LAST USER QUERY ================= #}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=remaining_messages|length - 1) %}
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{%- for message in remaining_messages %}
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{%- if message.role == "user" %}
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{%- set ns.last_query_index = loop.index0 %}
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{%- endif %}
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{%- endfor %}
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{# ================= RENDER MESSAGES ================= #}
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{%- for message in remaining_messages %}
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{# ---- Normalize content to string ---- #}
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{%- set content = message.get('content', '') %}
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{%- set reasoning_content = message.get('reasoning_content', none) %}
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{%- if content is string %}
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{%- set content_str = content %}
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{%- elif content is not none %}
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{%- set ns_content = namespace(text='') %}
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{%- for item in content if item.type == 'text' %}
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{%- set ns_content.text = ns_content.text ~ item.text %}
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{%- endfor %}
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{%- set content_str = ns_content.text %}
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{%- endif %}
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{# ================= USER ================= #}
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{%- if message.role in ['user', 'human'] %}
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<|im_start|>user
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{{ content_str }}<|im_end|>
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{%- elif message.role in ['assistant', 'gpt', 'function_call'] %}
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<|im_start|>assistant
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{%- if reasoning_content is string %}
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{{ "\n<think>" ~ reasoning_content ~ "</think>" }}
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{%- endif %}
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{{ content_str }}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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<tool_call>
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{"name": "{{ tool_call.name }}", "arguments": {{ tool_call.arguments if tool_call.arguments is string else (tool_call.arguments | tojson) }}}
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</tool_call>
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{%- endfor %}
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{%- endif %}
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<|im_end|>
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{%- elif message.role == 'tool' %}
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<|im_start|>user
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<tool_response>
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{{ content_str }}
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</tool_response><|im_end|>
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{%- endif %}
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{%- endfor %}
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{# ================= GENERATION PROMPT ================= #}
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{%- if add_generation_prompt %}
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<|im_start|>assistant
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- "\n<think></think>" }}
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{%- endif %}
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{%- endif %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 11,
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"head_dim": 256,
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 23040,
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"is_bitnet_config": true,
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"max_position_embeddings": 32768,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 12,
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"num_hidden_layers": 40,
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"num_key_value_heads": 4,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000042,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.5.0",
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"use_cache": true,
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"vocab_size": 131072
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 11,
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"output_attentions": false,
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"output_hidden_states": false,
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"transformers_version": "5.5.0",
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"use_cache": true
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bdc5e0d178f149b5e4ee0209eb2f627af1f93f2ea8cddb087729371f548cdc89
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size 20611350200
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:3aba27c4298f23f82157a1ab646db95bec23f6aebcb4056ee323229064fc5591
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size 9781129
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|endoftext|>",
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"is_local": true,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 131072,
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"pad_token": "<|endoftext|>",
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"tokenizer_class": "TokenizersBackend"
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}
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