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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- mistral |
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- mistral-small |
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- fp8 |
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- vllm |
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base_model: mistralai/Mistral-Small-24B-Instruct-2501 |
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library_name: transformers |
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--- |
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# Mistral-Small-24B-Instruct-2501-FP8-Dynamic |
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## Model Overview |
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- **Model Architecture:** Mistral-Small-24B-Instruct-2501 |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** FP8 |
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- **Activation quantization:** FP8 |
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- **Release Date:** 3/1/2025 |
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- **Version:** 1.0 |
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- **Model Developers:** Neural Magic |
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Quantized version of [Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501). |
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It achieves an average score of 78.88 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 79.45. |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. |
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## Deployment |
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### Use with vLLM |
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. |
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```python |
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from transformers import AutoTokenizer |
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from vllm import LLM, SamplingParams |
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max_model_len, tp_size = 4096, 1 |
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model_name = "neuralmagic/Mistral-Small-24B-Instruct-2501-FP8-Dynamic" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True) |
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sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id]) |
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messages_list = [ |
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[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}], |
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] |
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prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] |
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outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) |
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generated_text = [output.outputs[0].text for output in outputs] |
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print(generated_text) |
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``` |
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vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. |
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## Creation |
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. |
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```python |
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import argparse |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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from llmcompressor.transformers import oneshot |
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import os |
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def main(): |
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parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8') |
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parser.add_argument('--model_id', type=str, required=True, |
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help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-8B-Instruct")') |
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parser.add_argument('--save_path', type=str, default='.', |
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help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic') |
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args = parser.parse_args() |
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# Load model |
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model = AutoModelForCausalLM.from_pretrained( |
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args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(args.model_id) |
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# Configure the quantization algorithm and scheme |
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recipe = QuantizationModifier( |
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targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"] |
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) |
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# Apply quantization |
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oneshot(model=model, recipe=recipe) |
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save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic") |
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os.makedirs(save_path, exist_ok=True) |
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# Save to disk in compressed-tensors format |
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model.save_pretrained(save_path) |
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tokenizer.save_pretrained(save_path) |
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print(f"Model and tokenizer saved to: {save_path}") |
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if __name__ == "__main__": |
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main() |
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``` |
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## Evaluation |
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The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard) and [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/), using the following commands: |
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OpenLLM Leaderboard V1: |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks openllm \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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OpenLLM Leaderboard V2: |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",dtype=auto,add_bos_token=False,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \ |
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--apply_chat_template \ |
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--fewshot_as_multiturn \ |
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--tasks leaderboard \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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### Accuracy |
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#### OpenLLM Leaderboard V1 evaluation scores |
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| Metric | mistralai/Mistral-Small-24B-Instruct-2501 | nm-testing/Mistral-Small-24B-Instruct-2501-FP8-dynamic | |
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|-----------------------------------------|:---------------------------------:|:-------------------------------------------:| |
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| ARC-Challenge (Acc-Norm, 25-shot) | 72.18 | 71.76 | |
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| GSM8K (Strict-Match, 5-shot) | 90.14 | 89.01 | |
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| HellaSwag (Acc-Norm, 10-shot) | 85.05 | 84.65 | |
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| MMLU (Acc, 5-shot) | 80.69 | 80.55 | |
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| TruthfulQA (MC2, 0-shot) | 65.55 | 64.85 | |
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| Winogrande (Acc, 5-shot) | 83.11 | 82.48 | |
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| **Average Score** | **79.45** | **78.88** | |
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| **Recovery (%)** | **100.00** | **99.28** | |
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#### OpenLLM Leaderboard V2 evaluation scores |
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| Metric | mistralai/Mistral-Small-24B-Instruct-2501 | nm-testing/Mistral-Small-24B-Instruct-2501-FP8-dynamic | |
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|---------------------------------------------------------|:---------------------------------:|:-------------------------------------------:| |
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| IFEval (Inst-and-Prompt Level Strict Acc, 0-shot) | 73.27 | 73.53 | |
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| BBH (Acc-Norm, 3-shot) | 45.18 | 44.39 | |
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| MMLU-Pro (Acc, 5-shot) | 38.83 | 37.28 | |
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| **Average Score** | **52.42** | **51.73** | |
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| **Recovery (%)** | **100.00** | **98.68** | |
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| Math-Hard (Exact-Match, 4-shot) | 6.35 | 2.99 | |
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| GPQA (Acc-Norm, 0-shot) | 8.29 | 6.97 | |
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| MUSR (Acc-Norm, 0-shot) | 7.84 | 8.04 | |
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Results on Math-Hard, GPQA, and MUSR are not considred for accuracy recovery calculation because the unquantized model has close to random prediction accuracy (6.35, 8.29, 7.84) which doesn't provide a reliable baseline for recovery calculation. |
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