Create README.md

README.md

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+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen3-0.6B
+tags:
+- neuralmagic
+- redhat
+- llmcompressor
+- quantized
+- INT4
+---
+
+# Qwen3-0.6B-quantized.w4a16
+
+## Model Overview
+- **Model Architecture:** Qwen3ForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** INT4
+- **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 the weights of [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) to INT4 data type.
+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 of the linear operators within transformers blocks are quantized.
+Weights are quantized using a symmetric per-group scheme, with group size 128.
+The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
+
+
+## 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/Qwen3-0.6B-quantized.w4a16"
+number_gpus = 1
+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 GPTQModifier
+  from llmcompressor.transformers import oneshot
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  
+  # Load model
+  model_stub = "Qwen/Qwen3-0.6B"
+  model_name = model_stub.split("/")[-1]
+
+  num_samples = 1024
+  max_seq_len = 8192
+
+  model = AutoModelForCausalLM.from_pretrained(model_stub)
+
+  tokenizer = AutoTokenizer.from_pretrained(model_stub)
+
+  def preprocess_fn(example):
+    return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)}
+  
+  ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train")
+  ds = ds.map(preprocess_fn)
+
+  # Configure the quantization algorithm and scheme
+  recipe = GPTQModifier(
+      ignore=["lm_head"],
+      sequential_targets=["Qwen3DecoderLayer"],
+      targets="Linear",
+      scheme="W4A16",
+      dampening_frac=0.1,
+  )
+
+  # Apply quantization
+  oneshot(
+      model=model,
+      dataset=ds, 
+      recipe=recipe,
+      max_seq_length=max_seq_len,
+      num_calibration_samples=num_samples,
+  )
+  
+  # Save to disk in compressed-tensors format
+  save_path = model_name + "-quantized.w4a16"
+  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/Qwen3-0.6B-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.5,max_model_len=8192,enable_chunk_prefill=True,tensor_parallel_size=1 \
+    --tasks openllm \
+    --apply_chat_template\
+    --fewshot_as_multiturn \
+    --batch_size auto
+  ```
+</details>
+
+### Accuracy
+
+<table>
+  <tr>
+   <th>Category
+   </th>
+   <th>Benchmark
+   </th>
+   <th>Qwen3-0.6B
+   </th>
+   <th>Qwen3-0.6B-quantized.w4a16<br>(this model)
+   </th>
+   <th>Recovery
+   </th>
+  </tr>
+  <tr>
+   <td rowspan="7" ><strong>OpenLLM v1</strong>
+   </td>
+   <td>MMLU (5-shot)
+   </td>
+   <td>80.96
+   </td>
+   <td>80.36
+   </td>
+   <td>99.3%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>69.03
+   </td>
+   <td>68.69
+   </td>
+   <td>99.5%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>87.64
+   </td>
+   <td>85.97
+   </td>
+   <td>98.1%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>71.10
+   </td>
+   <td>71.18
+   </td>
+   <td>100.1%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>69.77
+   </td>
+   <td>70.90
+   </td>
+   <td>100.5%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>58.63
+   </td>
+   <td>58.86
+   </td>
+   <td>100.4%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>72.86</strong>
+   </td>
+   <td><strong>72.52</strong>
+   </td>
+   <td><strong>99.6%</strong>
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="7" ><strong>OpenLLM v2</strong>
+   </td>
+   <td>MMLU-Pro (5-shot)
+   </td>
+   <td>17.25
+   </td>
+   <td>14.27
+   </td>
+   <td>---
+   </td>
+  </tr>
+  <tr>
+   <td>IFEval (0-shot)
+   </td>
+   <td>62.83
+   </td>
+   <td>55.81
+   </td>
+   <td>88.8%
+   </td>
+  </tr>
+  <tr>
+   <td>BBH (3-shot)
+   </td>
+   <td>4.23
+   </td>
+   <td>1.63
+   </td>
+   <td>---
+   </td>
+  </tr>
+  <tr>
+   <td>Math-lvl-5 (4-shot)
+   </td>
+   <td>18.26
+   </td>
+   <td>10.26
+   </td>
+   <td>---
+   </td>
+  </tr>
+  <tr>
+   <td>GPQA (0-shot)
+   </td>
+   <td>0.00
+   </td>
+   <td>0.00
+   </td>
+   <td>---
+   </td>
+  </tr>
+  <tr>
+   <td>MuSR (0-shot)
+   </td>
+   <td>0.00
+   </td>
+   <td>0.00
+   </td>
+   <td>---
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>17.10</strong>
+   </td>
+   <td><strong>13.66</strong>
+   </td>
+   <td><strong>---</strong>
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Multilingual</strong>
+   </td>
+   <td>MGSM (0-shot)
+   </td>
+   <td>19.70
+   </td>
+   <td>19.90
+   </td>
+   <td>---
+   </td>
+  </tr>
+  <tr>
+   <td rowspan="6" ><strong>Reasoning<br>(generation)</strong>
+   </td>
+   <td>AIME 2024
+   </td>
+   <td>9.69
+   </td>
+   <td>3.44
+   </td>
+   <td>---
+   </td>
+  </tr>
+  <tr>
+   <td>AIME 2025
+   </td>
+   <td>13.13
+   </td>
+   <td>6.98
+   </td>
+   <td>---
+   </td>
+  </tr>
+  <tr>
+   <td>GPQA diamond
+   </td>
+   <td>29.29
+   </td>
+   <td>27.78
+   </td>
+   <td>94.8%
+   </td>
+  </tr>
+  <tr>
+   <td>Math-lvl-5
+   </td>
+   <td>71.60
+   </td>
+   <td>70.60
+   </td>
+   <td>98.6%
+   </td>
+  </tr>
+  <tr>
+   <td>LiveCodeBench
+   </td>
+   <td>12.83
+   </td>
+   <td>8.35
+   </td>
+   <td>---
+   </td>
+  </tr>
+</table>