| --- |
| tags: |
| - fp4 |
| - vllm |
| language: |
| - en |
| - de |
| - fr |
| - it |
| - pt |
| - hi |
| - es |
| - th |
| pipeline_tag: text-generation |
| license: apache-2.0 |
| base_model: unsloth/Mistral-Small-3.2-24B-Instruct-2506 |
| --- |
| |
| # Mistral-Small-3.2-24B-Instruct-2506-NVFP4 |
|
|
| ## Model Overview |
| - **Model Architecture:** unsloth/Mistral-Small-3.2-24B-Instruct-2506 |
| - **Input:** Text |
| - **Output:** Text |
| - **Model Optimizations:** |
| - **Weight quantization:** FP4 |
| - **Activation quantization:** FP4 |
| - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. |
| - **Release Date:** 10/29/2025 |
| - **Version:** 1.0 |
| - **Model Developers:** RedHatAI |
|
|
| This model is a quantized version of [unsloth/Mistral-Small-3.2-24B-Instruct-2506](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506). |
| It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. |
|
|
| ### Model Optimizations |
|
|
| This model was obtained by quantizing the weights and activations of [unsloth/Mistral-Small-3.2-24B-Instruct-2506](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506) to FP4 data type, ready for inference with vLLM>=0.9.1 |
| This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. |
|
|
| Only the weights and activations of the linear operators within transformers blocks are quantized using [LLM Compressor](https://github.com/vllm-project/llm-compressor). |
|
|
| ## Deployment |
|
|
| ### Use with vLLM |
|
|
| 1. Initialize vLLM server: |
| ``` |
| vllm serve RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4 --tensor_parallel_size 1 --tokenizer_mode mistral |
| ``` |
|
|
| 2. Send requests to the server: |
|
|
| ```python |
| from openai import OpenAI |
| |
| # Modify OpenAI's API key and API base to use vLLM's API server. |
| openai_api_key = "EMPTY" |
| openai_api_base = "http://<your-server-host>:8000/v1" |
| |
| client = OpenAI( |
| api_key=openai_api_key, |
| base_url=openai_api_base, |
| ) |
| |
| model = "RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4" |
| |
| |
| messages = [ |
| {"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, |
| ] |
| |
| outputs = client.chat.completions.create( |
| model=model, |
| messages=messages, |
| ) |
| |
| generated_text = outputs.choices[0].message.content |
| print(generated_text) |
| ``` |
|
|
| ## Creation |
|
|
| This model was created by applying [LLM Compressor with calibration samples from UltraChat](https://github.com/vllm-project/llm-compressor/blob/main/examples/quantization_w4a4_fp4/llama3_example.py), as presented in the code snipet below. |
|
|
| <details> |
| |
| ```python |
| from datasets import load_dataset |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| from llmcompressor import oneshot |
| from llmcompressor.modifiers.quantization import QuantizationModifier |
| from llmcompressor.modifiers.smoothquant import SmoothQuantModifier |
| from llmcompressor.utils import dispatch_for_generation |
| |
| MODEL_ID = "unsloth/Mistral-Small-3.2-24B-Instruct-2506" |
| |
| # Load model. |
| model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto") |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_ID) |
| |
| DATASET_ID = "HuggingFaceH4/ultrachat_200k" |
| DATASET_SPLIT = "train_sft" |
| |
| # Select number of samples. 512 samples is a good place to start. |
| # Increasing the number of samples can improve accuracy. |
| NUM_CALIBRATION_SAMPLES = 512 |
| MAX_SEQUENCE_LENGTH = 2048 |
| |
| # Load dataset and preprocess. |
| ds = load_dataset(DATASET_ID, split=f"{DATASET_SPLIT}[:{NUM_CALIBRATION_SAMPLES}]") |
| ds = ds.shuffle(seed=42) |
| |
| def preprocess(example): |
| return { |
| "text": tokenizer.apply_chat_template( |
| example["messages"], |
| tokenize=False, |
| ) |
| } |
| |
| ds = ds.map(preprocess) |
| |
| # Tokenize inputs. |
| def tokenize(sample): |
| return tokenizer( |
| sample["text"], |
| padding=False, |
| max_length=MAX_SEQUENCE_LENGTH, |
| truncation=True, |
| add_special_tokens=False, |
| ) |
| |
| ds = ds.map(tokenize, remove_columns=ds.column_names) |
| |
| # Configure the quantization algorithm and scheme. |
| # In this case, we: |
| # * quantize the weights to fp4 with per group 16 via ptq |
| # * calibrate a global_scale for activations, which will be used to |
| # quantize activations to fp4 on the fly |
| smoothing_strength = 0.9 |
| recipe = [ |
| SmoothQuantModifier(smoothing_strength=smoothing_strength), |
| QuantizationModifier( |
| ignore=["re:.*lm_head.*"], |
| config_groups={ |
| "group_0": { |
| "targets": ["Linear"], |
| "weights": { |
| "num_bits": 4, |
| "type": "float", |
| "strategy": "tensor_group", |
| "group_size": 16, |
| "symmetric": True, |
| "observer": "mse", |
| }, |
| "input_activations": { |
| "num_bits": 4, |
| "type": "float", |
| "strategy": "tensor_group", |
| "group_size": 16, |
| "symmetric": True, |
| "dynamic": "local", |
| "observer": "minmax", |
| }, |
| } |
| }, |
| ) |
| ] |
| |
| # Save to disk in compressed-tensors format. |
| SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-NVFP4" |
| |
| # Apply quantization. |
| oneshot( |
| model=model, |
| dataset=ds, |
| recipe=recipe, |
| max_seq_length=MAX_SEQUENCE_LENGTH, |
| num_calibration_samples=NUM_CALIBRATION_SAMPLES, |
| output_dir=SAVE_DIR, |
| ) |
| |
| print("\n\n") |
| print("========== SAMPLE GENERATION ==============") |
| dispatch_for_generation(model) |
| input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda") |
| output = model.generate(input_ids, max_new_tokens=100) |
| print(tokenizer.decode(output[0])) |
| print("==========================================\n\n") |
| |
| model.save_pretrained(SAVE_DIR, save_compressed=True) |
| tokenizer.save_pretrained(SAVE_DIR) |
| ``` |
| </details> |
|
|
| ## Evaluation |
|
|
| This model was evaluated on the well-known OpenLLM v1, OpenLLM v2 and HumanEval_64 benchmarks using [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness). |
| |
| ### Accuracy |
| <table> |
| <thead> |
| <tr> |
| <th>Category</th> |
| <th>Metric</th> |
| <th>unsloth/Mistral-Small-3.2-24B-Instruct-2506</th> |
| <th>RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4</th> |
| <th>Recovery</th> |
| </tr> |
| </thead> |
| <tbody> |
| <!-- OpenLLM V1 --> |
| <tr> |
| <td rowspan="7"><b>OpenLLM V1</b></td> |
| <td>arc_challenge</td> |
| <td>68.52</td> |
| <td>66.98</td> |
| <td>97.75</td> |
| </tr> |
| <tr> |
| <td>gsm8k</td> |
| <td>89.61</td> |
| <td>87.11</td> |
| <td>97.21</td> |
| </tr> |
| <tr> |
| <td>hellaswag</td> |
| <td>85.70</td> |
| <td>85.11</td> |
| <td>99.31</td> |
| </tr> |
| <tr> |
| <td>mmlu</td> |
| <td>81.06</td> |
| <td>79.43</td> |
| <td>97.99</td> |
| </tr> |
| <tr> |
| <td>truthfulqa_mc2</td> |
| <td>61.35</td> |
| <td>60.34</td> |
| <td>98.35</td> |
| </tr> |
| <tr> |
| <td>winogrande</td> |
| <td>83.27</td> |
| <td>81.61</td> |
| <td>98.01</td> |
| </tr> |
| <tr> |
| <td><b>Average</b></td> |
| <td><b>78.25</b></td> |
| <td><b>76.76</b></td> |
| <td><b>98.10</b></td> |
| </tr> |
| <tr> |
| <td rowspan="7"><b>OpenLLM V2</b></td> |
| <td>BBH (3-shot)</td> |
| <td>65.86</td> |
| <td>64.05</td> |
| <td>97.25</td> |
| </tr> |
| <tr> |
| <td>MMLU-Pro (5-shot)</td> |
| <td>50.84</td> |
| <td>48.45</td> |
| <td>95.30</td> |
| </tr> |
| <tr> |
| <td>MuSR (0-shot)</td> |
| <td>39.15</td> |
| <td>40.21</td> |
| <td>102.71</td> |
| </tr> |
| <tr> |
| <td>IFEval (0-shot)</td> |
| <td>84.05</td> |
| <td>84.41</td> |
| <td>100.43</td> |
| </tr> |
| <tr> |
| <td>GPQA (0-shot)</td> |
| <td>33.14</td> |
| <td>32.55</td> |
| <td>98.22</td> |
| </tr> |
| <tr> |
| <td>Math-|v|-5 (4-shot)</td> |
| <td>41.69</td> |
| <td>37.76</td> |
| <td>90.57</td> |
| </tr> |
| <tr> |
| <td><b>Average</b></td> |
| <td><b>52.46</b></td> |
| <td><b>51.24</b></td> |
| <td><b>97.68</b></td> |
| </tr> |
| <tr> |
| <td rowspan="2"><b>Coding</b></td> |
| <td>HumanEval_64 pass@2</td> |
| <td>88.88</td> |
| <td>88.84</td> |
| <td>99.95</td> |
| </tr> |
| </tbody> |
| </table> |
| |
|
|
|
|
|
|
| ### Reproduction |
|
|
| The results were obtained using the following commands: |
|
|
| <details> |
|
|
| ``` |
| lm_eval \ |
| --model vllm \ |
| --model_args pretrained="RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\ |
| --apply_chat_template \ |
| --fewshot_as_multiturn \ |
| --tasks openllm \ |
| --batch_size auto |
| ``` |
|
|
|
|
| #### OpenLLM v2 |
| ``` |
| lm_eval \ |
| --model vllm \ |
| --model_args pretrained="RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\ |
| --apply_chat_template \ |
| --fewshot_as_multiturn \ |
| --tasks leaderboard \ |
| --batch_size auto |
| ``` |
|
|
| #### HumanEval_64 |
| ``` |
| lm_eval \ |
| --model vllm \ |
| --model_args pretrained="RedHatAI/Mistral-Small-3.2-24B-Instruct-2506-NVFP4",dtype=auto,max_model_len=4096,tensor_parallel_size=2,enable_chunked_prefill=True,enforce_eager=True\ |
| --apply_chat_template \ |
| --fewshot_as_multiturn \ |
| --tasks humaneval_64_instruct \ |
| --batch_size auto |
| |
| ``` |
| </details> |
