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Model: tiiuae/Falcon3-10B-Instruct Source: Original Platform
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---
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base_model: tiiuae/Falcon3-10B-Base
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library_name: transformers
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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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tags:
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- falcon3
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model-index:
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- name: Falcon3-10B-Instruct
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 78.17
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 44.82
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 25.91
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 10.51
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name: acc_norm
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source:
|
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 13.61
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 38.1
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=tiiuae/Falcon3-10B-Instruct
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name: Open LLM Leaderboard
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---
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<div align="center">
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<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/general/falco3-logo.png" alt="drawing" width="500"/>
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</div>
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# Falcon3-10B-Instruct
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**Falcon3** family of Open Foundation Models is a set of pretrained and instruct LLMs ranging from 1B to 10B parameters.
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This repository contains the **Falcon3-10B-Instruct**. It achieves state-of-the-art results (at the time of release) on reasoning, language understanding, instruction following, code and mathematics tasks.
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Falcon3-10B-Instruct supports 4 languages (English, French, Spanish, Portuguese) and a context length of up to 32K.
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## Model Details
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- Architecture
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- Transformer-based causal decoder-only architecture
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- 40 decoder blocks
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- Grouped Query Attention (GQA) for faster inference: 12 query heads and 4 key-value heads
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- Wider head dimension: 256
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- High RoPE value to support long context understanding: 1000042
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- Uses SwiGLu and RMSNorm
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- 32K context length
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- 131K vocab size
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- Depth up-scaled from **Falcon3-7B-Base** with 2 Teratokens of datasets comprising of web, code, STEM, high quality and mutlilingual data using 1024 H100 GPU chips
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- Posttrained on 1.2 million samples of STEM, conversational, code, safety and function call data
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- Supports EN, FR, ES, PT
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- Developed by [Technology Innovation Institute](https://www.tii.ae)
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- License: TII Falcon-LLM License 2.0
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- Model Release Date: December 2024
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## Getting started
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<details>
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<summary> Click to expand </summary>
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "tiiuae/Falcon3-10B-Instruct"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "How many hours in one day?"
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messages = [
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{"role": "system", "content": "You are a helpful friendly assistant Falcon3 from TII, try to follow instructions as much as possible."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=1024
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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</details>
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<br>
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## Benchmarks
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We report the official HuggingFace leaderboard normalized evaluations [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) in the following table.
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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: 7%;">
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<col style="width: 7%;">
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<col style="width: 7%;">
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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>Yi-1.5-9B-Chat</th>
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<th>Mistral-Nemo-Instruct-2407 (12B)</th>
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<th>Gemma-2-9b-it</th>
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<th>Falcon3-10B-Instruct</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>60.46</td>
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||||
<td>63.80</td>
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<td>74.36</td>
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<td><b>78.17</b></td>
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</tr>
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<tr>
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<td>BBH (3-shot)</td>
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<td>36.95</td>
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<td>29.68</td>
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<td>42.14</td>
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<td><b>44.82</b></td>
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</tr>
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<tr>
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<td>MATH Lvl-5 (4-shot)</td>
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<td>12.76</td>
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<td>6.50</td>
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<td>0.23</td>
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<td><b>25.91</b></td>
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</tr>
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<tr>
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<td>GPQA (0-shot)</td>
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<td>11.30</td>
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<td>5.37</td>
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<td><b>14.77</b></td>
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<td>10.51</td>
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</tr>
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<tr>
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<td>MUSR (0-shot)</td>
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<td>12.84</td>
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<td>8.48</td>
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<td>9.74</td>
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<td><b>13.61</b></td>
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</tr>
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<tr>
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<td>MMLU-PRO (5-shot)</td>
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<td>33.06</td>
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<td>27.97</td>
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<td>31.95</td>
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<td><b>38.10</b></td>
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</tr>
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</tbody>
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</table>
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Also, we report in the following table our internal pipeline benchmarks.
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- We use [lm-evaluation harness](https://github.com/EleutherAI/lm-evaluation-harness).
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- We report **raw scores** obtained by applying chat template and fewshot_as_multiturn.
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- We use same batch-size across all models.
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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="width: 7%;">
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<col style="width: 7%;">
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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>Category</th>
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<th>Benchmark</th>
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<th>Yi-1.5-9B-Chat</th>
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<th>Mistral-Nemo-Instruct-2407 (12B)</th>
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<th>Falcon3-10B-Instruct</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 rowspan="3">General</td>
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<td>MMLU (5-shot)</td>
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<td>68.8</td>
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<td>66.0</td>
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<td><b>73.9</b></td>
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</tr>
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<tr>
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<td>MMLU-PRO (5-shot)</td>
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<td>38.8</td>
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<td>34.3</td>
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<td><b>44</b></td>
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</tr>
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<tr>
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<td>IFEval</td>
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<td>57.8</td>
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<td>63.4</td>
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<td><b>78</b></td>
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</tr>
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<tr>
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<td rowspan="3">Math</td>
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<td>GSM8K (5-shot)</td>
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<td>77.1</td>
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<td>77.6</td>
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<td><b>84.9</b></td>
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</tr>
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<tr>
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<td>GSM8K (8-shot, COT)</td>
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<td>76</td>
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<td>80.4</td>
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<td><b>84.6</b></td>
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</tr>
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<tr>
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<td>MATH Lvl-5 (4-shot)</td>
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<td>3.3</td>
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<td>5.9</td>
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<td><b>22.1</b></td>
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</tr>
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<tr>
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<td rowspan="5">Reasoning</td>
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<td>Arc Challenge (25-shot)</td>
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<td>58.3</td>
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<td>63.4</td>
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<td><b>66.2</b></td>
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</tr>
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<tr>
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<td>GPQA (0-shot)</td>
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<td><b>35.6</b></td>
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<td>33.2</td>
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<td>33.5</td>
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</tr>
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<tr>
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<td>GPQA (0-shot, COT)</td>
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<td>16</td>
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<td>12.7</td>
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<td><b>32.6</b></td>
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</tr>
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<tr>
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<td>MUSR (0-shot)</td>
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<td><b>41.9</b></td>
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<td>38.1</td>
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<td>41.1</td>
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</tr>
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<tr>
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<td>BBH (3-shot)</td>
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<td>50.6</td>
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<td>47.5</td>
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<td><b>58.4</b></td>
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</tr>
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<tr>
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<td rowspan="4">CommonSense Understanding</td>
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<td>PIQA (0-shot)</td>
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<td>76.4</td>
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<td>78.2</td>
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<td><b>78.4</b></td>
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</tr>
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<tr>
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<td>SciQ (0-shot)</td>
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<td>61.7</td>
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<td>76.4</td>
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<td><b>90.4</b></td>
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</tr>
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<tr>
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<td>Winogrande (0-shot)</td>
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<td>-</td>
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<td>-</td>
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||||
<td>71</td>
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</tr>
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<tr>
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<td>OpenbookQA (0-shot)</td>
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<td>43.2</td>
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<td>47.4</td>
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<td><b>48.2</b></td>
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</tr>
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<tr>
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<td rowspan="2">Instructions following</td>
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<td>MT-Bench (avg)</td>
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<td>8.3</td>
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<td><b>8.6</b></td>
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<td>8.2</td>
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</tr>
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<tr>
|
||||
<td>Alpaca (WC)</td>
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||||
<td>25.8</td>
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||||
<td><b>45.4</b></td>
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||||
<td>24.7</td>
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||||
</tr>
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||||
<tr>
|
||||
<td>Tool use</td>
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||||
<td>BFCL AST (avg)</td>
|
||||
<td>48.4</td>
|
||||
<td>74.2</td>
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||||
<td><b>90.5</b></td>
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||||
</tr>
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||||
<tr>
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||||
<td rowspan="2">Code</td>
|
||||
<td>EvalPlus (0-shot) (avg)</td>
|
||||
<td>69.4</td>
|
||||
<td>58.9</td>
|
||||
<td><b>74.7</b></td>
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||||
</tr>
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||||
<tr>
|
||||
<td>Multipl-E (0-shot) (avg)</td>
|
||||
<td>-</td>
|
||||
<td>34.5</td>
|
||||
<td><b>45.8</b></td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
|
||||
## Useful links
|
||||
- View our [release blogpost](https://huggingface.co/blog/falcon3).
|
||||
- Feel free to join [our discord server](https://discord.gg/fwXpMyGc) if you have any questions or to interact with our researchers and developers.
|
||||
|
||||
## Technical Report
|
||||
|
||||
Coming soon....
|
||||
|
||||
## Citation
|
||||
If Falcon3 family were helpful in your work, feel free to give us a cite.
|
||||
|
||||
```
|
||||
@misc{Falcon3,
|
||||
title = {The Falcon 3 family of Open Models},
|
||||
author = {TII Team},
|
||||
month = {December},
|
||||
year = {2024}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
|
||||
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/tiiuae__Falcon3-10B-Instruct-details)
|
||||
|
||||
| Metric |Value|
|
||||
|-------------------|----:|
|
||||
|Avg. |35.19|
|
||||
|IFEval (0-Shot) |78.17|
|
||||
|BBH (3-Shot) |44.82|
|
||||
|MATH Lvl 5 (4-Shot)|25.91|
|
||||
|GPQA (0-shot) |10.51|
|
||||
|MuSR (0-shot) |13.61|
|
||||
|MMLU-PRO (5-shot) |38.10|
|
||||
|
||||
28
config.json
Normal file
28
config.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
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"attention_bias": false,
|
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|
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|
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|
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|
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|
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|
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"intermediate_size": 23040,
|
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"max_position_embeddings": 32768,
|
||||
"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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"pretraining_tp": 1,
|
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"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000042,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.46.1",
|
||||
"use_cache": true,
|
||||
"vocab_size": 131072
|
||||
}
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 11,
|
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"eos_token_id": 11,
|
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"transformers_version": "4.46.1"
|
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}
|
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3
model-00001-of-00005.safetensors
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"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00005.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00005.safetensors",
|
||||
"model.norm.weight": "model-00005-of-00005.safetensors"
|
||||
}
|
||||
}
|
||||
41
special_tokens_map.json
Normal file
41
special_tokens_map.json
Normal file
@@ -0,0 +1,41 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
">>TITLE<<",
|
||||
">>ABSTRACT<<",
|
||||
">>INTRODUCTION<<",
|
||||
">>SUMMARY<<",
|
||||
">>COMMENT<<",
|
||||
">>ANSWER<<",
|
||||
">>QUESTION<<",
|
||||
">>DOMAIN<<",
|
||||
">>EMAIL_ADDRESS<<",
|
||||
">>IP_ADDRESS<<",
|
||||
"<|startoftext|>",
|
||||
">>IP_ADDRESS_0<<",
|
||||
">>IP_ADDRESS_1<<",
|
||||
">>IP_ADDRESS_2<<",
|
||||
">>IP_ADDRESS_3<<",
|
||||
">>IP_ADDRESS_4<<",
|
||||
">>IP_ADDRESS_5<<",
|
||||
">>IP_ADDRESS_6<<",
|
||||
">>IP_ADDRESS_7<<",
|
||||
">>IP_ADDRESS_8<<",
|
||||
">>IP_ADDRESS_9<<",
|
||||
">>PASSWORD<<",
|
||||
">>KEY<<"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
664576
tokenizer.json
Normal file
664576
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
16233
tokenizer_config.json
Normal file
16233
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user