Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-235B-A22B
+tags:
+- neuralmagic
+- redhat
+- llmcompressor
+- quantized
+- FP8
+---
+
+# Qwen3-235B-A22B-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** Qwen3MoeForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** FP8
+  - **Weight quantization:** FP8
+- **Intended Use Cases:**
+  - Reasoning.
+  - Function calling.
+  - Subject matter experts via fine-tuning.
+  - Multilingual instruction following.
+  - Translation.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 05/05/2025
+- **Version:** 1.0
+- **Model Developers:** RedHat (Neural Magic)
+
+### Model Optimizations
+
+This model was obtained by quantizing activations and weights of [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) to FP8 data type.
+This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x).
+Weight quantization also reduces disk size requirements by approximately 50%.
+
+Only weights and activations of the linear operators within transformers blocks are quantized.
+Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
+The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization.
+
+
+## Deployment
+
+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/Qwen3-235B-A22B-FP8-dynamic"
+number_gpus = 4
+sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]
+
+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
+
+<details>
+  <summary>Creation details</summary>
+  This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+
+  ```python
+  from llmcompressor.modifiers.quantization import QuantizationModifier
+  from llmcompressor.transformers import oneshot
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  
+  # Load model
+  model_stub = "Qwen/Qwen3-235B-A22B"
+  model_name = model_stub.split("/")[-1]
+
+  model = AutoModelForCausalLM.from_pretrained(model_stub)
+
+  tokenizer = AutoTokenizer.from_pretrained(model_stub)
+
+  # Configure the quantization algorithm and scheme
+  recipe = QuantizationModifier(
+      ignore=["lm_head"],
+      targets="Linear",
+      scheme="FP8_dynamic",
+  )
+
+  # Apply quantization
+  oneshot(
+      model=model,
+      recipe=recipe,
+  )
+  
+  # Save to disk in compressed-tensors format
+  save_path = model_name + "-FP8-dynamic"
+  model.save_pretrained(save_path)
+  tokenizer.save_pretrained(save_path)
+  print(f"Model and tokenizer saved to: {save_path}")
+  ```
+</details>
+ 
+
+
+## Evaluation
+
+The model was evaluated on the OpenLLM leaderboard tasks (version 1), using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) and [vLLM](https://docs.vllm.ai/en/stable/).
+
+<details>
+  <summary>Evaluation details</summary>
+
+  ```
+  lm_eval \
+    --model vllm \
+    --model_args pretrained="RedHatAI/Qwen3-235B-A22B-FP8-dynamic",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=4 \
+    --tasks openllm \
+    --apply_chat_template\
+    --fewshot_as_multiturn \
+    --batch_size auto
+  ```
+</details>
+
+### Accuracy
+
+<table>
+  <tr>
+   <th>Category
+   </th>
+   <th>Benchmark
+   </th>
+   <th>Qwen3-235B-A22B
+   </th>
+   <th>Qwen3-235B-A22B-FP8-dynamic<br>(this model)
+   </th>
+   <th>Recovery
+   </th>
+  </tr>
+  <tr>
+   <td rowspan="7" ><strong>OpenLLM v1</strong>
+   </td>
+   <td>MMLU (5-shot)
+   </td>
+   <td>84.77
+   </td>
+   <td>84.61
+   </td>
+   <td>99.8%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>71.84
+   </td>
+   <td>70.90
+   </td>
+   <td>98.7%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>74.22
+   </td>
+   <td>74.98
+   </td>
+   <td>101.0%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>76.56
+   </td>
+   <td>76.10
+   </td>
+   <td>99.4%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>73.95
+   </td>
+   <td>75.06
+   </td>
+   <td>101.5%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>61.18
+   </td>
+   <td>60.93
+   </td>
+   <td>99.6%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>73.75</strong>
+   </td>
+   <td><strong>73.76</strong>
+   </td>
+   <td><strong>100.0%</strong>
+   </td>
+  </tr>
+</table>