Update README.md

README.md

@@ -9,4 +9,142 @@ tags:
 - mistral-small
 - fp8
 - vllm
----
+---
+
+# Mistral-Small-24B-Instruct-2501-FP8-Dynamic
+
+## Model Overview
+- **Model Architecture:** Mistral-Small-24B-Instruct-2501
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Release Date:** 3/1/2025
+- **Version:** 1.0
+- **Model Developers:** Neural Magic
+
+Quantized version of [Mistral-Small-24B-Instruct-2501](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501).
+It achieves a flexible-extract filter score of 0.9030 on the evaluated on [GSM8k](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks/gsm8k) task, where as the unquantized model achieves a flexible-extract filter score of 0.9060.
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations to FP8 data type, ready for inference with vLLM >= 0.5.2.
+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. 
+
+## Deployment
+
+### Use with vLLM
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from transformers import AutoTokenizer
+from vllm import LLM, SamplingParams
+
+max_model_len, tp_size = 4096, 1
+model_name = "nm-testing/Mistral-Small-24B-Instruct-2501-FP8-Dynamic
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True)
+sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
+
+messages_list = [
+    [{"role": "user", "content": "Who are you? Please respond in pirate speak!"}],
+]
+
+prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
+
+outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
+
+generated_text = [output.outputs[0].text for output in outputs]
+print(generated_text)
+```
+
+vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+## Creation
+
+This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+
+```python
+import argparse
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import oneshot
+import os
+
+def main():
+    parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8')
+    parser.add_argument('--model_id', type=str, required=True,
+                        help='The model ID from HuggingFace (e.g., "mistralai/Mistral-Small-24B-Instruct-2501")')
+    parser.add_argument('--save_path', type=str, default='.',
+                        help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic')
+    args = parser.parse_args()
+
+    # Load model
+    model = AutoModelForCausalLM.from_pretrained(
+        args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(args.model_id)
+
+    # Configure the quantization algorithm and scheme
+    recipe = QuantizationModifier(
+        targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
+    )
+
+    # Apply quantization
+    oneshot(model=model, recipe=recipe)
+
+    save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic")
+    os.makedirs(save_path, exist_ok=True)
+
+    # Save to disk in compressed-tensors format
+    model.save_pretrained(save_path)
+    tokenizer.save_pretrained(save_path)
+    print(f"Model and tokenizer saved to: {save_path}")
+
+if __name__ == "__main__":
+    main()
+```
+
+## Evaluation
+
+The optimized model was evaluated on GSM8k task with the flexible-extract filter score of 0.9030 ± 0.0082, and strict-match filter score of 0.8976 ± 0.0083, where as the unquantized model with the flexible-extract filter score of 0.9060 ± .0080, and strict-match filter score of 0.8992 ± 0.0083.
+
+Evaluations were carried out using the following commands.
+
+For the quantized model:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="nm-testing/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",add_bos_token=True \
+  --tasks gsm8k \
+  --batch_size auto
+```
+
+For the unquantized model
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="mistralai/Mistral-Small-24B-Instruct-2501",add_bos_token=True \
+  --tasks gsm8k \
+  --batch_size auto
+
+```
+
+### Accuracy
+
+#### GSM8k evaluation scores for the optimized model
+
+|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
+|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
+|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9030|±  |0.0082|
+|     |       |strict-match    |     5|exact_match|↑  |0.8976|±  |0.0083|
+
+#### GSM8k evaluation scores for the unquantized model
+
+|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
+|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
+|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9060|±  |0.0080|
+|     |       |strict-match    |     5|exact_match|↑  |0.8992|±  |0.0083|