Update README.md

README.md

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----
-license: apache-2.0
----
+---
+tags:
+- fp4
+- vllm
+language:
+- en
+- de
+- fr
+- it
+- pt
+- hi
+- es
+- th
+pipeline_tag: text-generation
+license: apache-2.0
+base_model: Qwen/Qwen3-8B
+---
+
+# Qwen3-8B-NVFP4
+
+## Model Overview
+- **Model Architecture:** Qwen/Qwen3-8B
+  - **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:** 6/25/2025
+- **Version:** 1.0
+- **Model Developers:** RedHatAI
+
+This model is a quantized version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
+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 [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) 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 25%.
+
+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
+
+This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
+
+```python
+from vllm import LLM, SamplingParams
+from transformers import AutoTokenizer
+
+model_id = "RedHatAI/Qwen3-8B-NVFP4"
+number_gpus = 1
+
+sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+
+prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
+
+outputs = llm.generate(prompts, sampling_params)
+
+generated_text = outputs[0].outputs[0].text
+print(generated_text)
+```
+
+vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
+
+## 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.utils import dispatch_for_generation
+
+MODEL_ID = "Qwen/Qwen3-8B"
+
+# 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 = 0.8
+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": "mse",
+                },
+            }
+        },
+    )
+]
+
+# 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). The Reasoning evals were done using [ligheval](https://github.com/neuralmagic/lighteval).
+
+### Accuracy
+<table>
+  <thead>
+    <tr>
+      <th>Category</th>
+      <th>Metric</th>
+      <th>Qwen/Qwen3-8B</th>
+      <th>Qwen3-8B-NVFP4 (this model)</th>
+      <th>Recovery</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V1</b></td>
+      <td>arc_challenge</td>
+      <td>64.76</td>
+      <td>63.91</td>
+      <td>98.69</td>
+    </tr>
+    <tr>
+      <td>gsm8k</td>
+      <td>87.26</td>
+      <td>86.73</td>
+      <td>99.39</td>
+    </tr>
+    <tr>
+      <td>hellaswag</td>
+      <td>76.68</td>
+      <td>75.34</td>
+      <td>98.25</td>
+    </tr>
+    <tr>
+      <td>mmlu</td>
+      <td>74.97</td>
+      <td>73.07</td>
+      <td>97.47</td>
+    </tr>
+    <tr>
+      <td>truthfulqa_mc2</td>
+      <td>54.42</td>
+      <td>55.07</td>
+      <td>101.19</td>
+    </tr>
+    <tr>
+      <td>winogrande</td>
+      <td>71.43</td>
+      <td>68.43</td>
+      <td>95.80</td>
+    </tr>
+    <tr>
+      <td><b>Average</b></td>
+      <td><b>71.59</b></td>
+      <td><b>70.43</b></td>
+      <td><b>98.38</b></td>
+    </tr>
+    <tr>
+      <td rowspan="7"><b>OpenLLM V2</b></td>
+      <td>BBH (3-shot)</td>
+      <td>47.46</td>
+      <td>49.33</td>
+      <td>103.94</td>
+    </tr>
+    <tr>
+      <td>MMLU-Pro (5-shot)</td>
+      <td>34.64</td>
+      <td>27.49</td>
+      <td>79.36</td>
+    </tr>
+    <tr>
+      <td>MuSR (0-shot)</td>
+      <td>40.61</td>
+      <td>42.86</td>
+      <td>105.54</td>
+    </tr>
+    <tr>
+      <td>IFEval (0-shot)</td>
+      <td>87.89</td>
+      <td>87.65</td>
+      <td>99.73</td>
+    </tr>
+    <tr>
+      <td>GPQA (0-shot)</td>
+      <td>25.17</td>
+      <td>26.34</td>
+      <td>104.65</td>
+    </tr>
+    <tr>
+      <td>Math-|v|-5 (4-shot)</td>
+      <td>53.55</td>
+      <td>50.83</td>
+      <td>94.92</td>
+    </tr>
+    <tr>
+      <td><b>Average</b></td>
+      <td><b>48.22</b></td>
+      <td><b>47.42</b></td>
+      <td><b>98.33</b></td>
+    </tr>
+    <tr>
+      <td rowspan="1"><b>Coding</b></td>
+      <td>HumanEval_64 pass@2</td>
+      <td>86.51</td>
+      <td>85.32</td>
+      <td>98.62</td>
+    </tr>
+      <tr>
+      <td rowspan="4"><b>Reasoning</b></td>
+      <td>AIME24 (0-shot)</td>
+      <td>75.86</td>
+      <td>62.07</td>
+      <td>81.82</td>
+    </tr>
+    <tr>
+      <td>AIME25 (0-shot)</td>
+      <td>65.52</td>
+      <td>62.07</td>
+      <td>94.74</td>
+    </tr>
+    <tr>
+      <td>GPQA (Diamond, 0-shot)</td>
+      <td>59.90</td>
+      <td>54.82</td>
+      <td>91.51</td>
+    </tr>
+    <tr>
+      <td><b>Average</b></td>
+      <td><b>67.09</b></td>
+      <td><b>59.65</b></td>
+      <td><b>89.36</b></td>
+    </tr>
+  </tbody>
+</table>
+
+
+
+
+### Reproduction
+
+The results were obtained using the following commands:
+
+<details>
+
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="RedHatAI/Qwen3-8B-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/Qwen3-8B-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/Qwen3-8B-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
+```
+
+#### LightEval 
+```
+# --- model_args.yaml ---
+cat > model_args.yaml <<'YAML'
+model_parameters:
+  model_name: "RedHatAI/Qwen3-8B-NVFP4"
+  dtype: auto
+  gpu_memory_utilization: 0.9
+  tensor_parallel_size: 2
+  max_model_length: 40960
+  generation_parameters:
+    seed: 42
+    temperature: 0.6
+    top_k: 20
+    top_p: 0.95
+    min_p: 0.0
+    max_new_tokens: 32768
+YAML
+
+lighteval vllm model_args.yaml \
+  "lighteval|aime24|0,lighteval|aime25|0,lighteval|gpqa:diamond|0" \
+  --max-samples -1 \
+  --output-dir out_dir
+
+```
+</details>