Create README.md

README.md

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+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+- chat
+- neuralmagic
+- llmcompressor
+- int8
+---
+
+# Qwen2.5-7B-Instruct-quantized.w8a8
+
+## Model Overview
+- **Model Architecture:** Qwen2
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Activation quantization:** INT8
+  - **Weight quantization:** INT8
+- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B), this models is intended for assistant-like chat.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws).
+- **Release Date:** 10/09/2024
+- **Version:** 1.0
+- **License(s):** [apache-2.0](https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE)
+- **Model Developers:** Neural Magic
+
+### Model Optimizations
+
+This model was obtained by quantizing activations and weights of [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) to INT8 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.
+A combination of the [SmoothQuant](https://arxiv.org/abs/2211.10438) and [GPTQ](https://arxiv.org/abs/2210.17323) algorithms 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/Qwen2.5-7B-Instruct-quantized.w8a8"
+number_gpus = 1
+max_model_len = 8192
+
+sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)
+
+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, tokenize=False)
+
+llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
+
+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 transformers import AutoModelForCausalLM, AutoTokenizer
+  from llmcompressor.modifiers.quantization import GPTQModifier
+  from llmcompressor.modifiers.smoothquant import SmoothQuantModifier  
+  from llmcompressor.transformers import oneshot
+  from datasets import load_dataset
+  
+  # Load model
+  model_stub = "Qwen/Qwen2.5-7B-Instruct"
+  model_name = model_stub.split("/")[-1]
+  
+  num_samples = 512
+  max_seq_len = 8192
+  
+  tokenizer = AutoTokenizer.from_pretrained(model_stub)
+  
+  model = AutoModelForCausalLM.from_pretrained(
+      model_stub,
+      device_map="auto",
+      torch_dtype="auto",
+  )
+  
+  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 = [
+      SmoothQuantModifier(
+        smoothing_strength=0.8,
+        mappings=[
+            [["re:.*q_proj", "re:.*k_proj", "re:.*v_proj"], "re:.*input_layernorm"],
+            [["re:.*gate_proj", "re:.*up_proj"], "re:.*post_attention_layernorm"],
+            [["re:.*down_proj"], "re:.*up_proj"],
+        ],
+      ),
+      GPTQModifier(
+          ignore=["lm_head"],
+          sequential_targets=["Qwen2DecoderLayer"],
+          dampening_frac=0.01,
+          targets="Linear",
+          scheme="W8A8",
+      ),
+  ]
+  
+  # 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.w8a8"
+  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](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) leaderboard tasks (version 1) with the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/tree/387Bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 387Bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command:
+```
+lm_eval \
+  --model vllm \
+  --model_args pretrained="neuralmagic/Qwen2.5-7B-Instruct-quantized.w8a8",dtype=auto,gpu_memory_utilization=0.5,max_model_len=4096,enable_chunk_prefill=True,tensor_parallel_size=1 \
+  --apply_chat_template \
+  --fewshot_as_multiturn \
+  --tasks openllm \
+  --batch_size auto
+```
+
+### Accuracy
+
+#### Open LLM Leaderboard evaluation scores
+<table>
+  <tr>
+   <th>Benchmark
+   </th>
+   <th>Qwen2.5-7B-Instruct
+   </th>
+   <th>Qwen2.5-7B-Instruct-quantized.w8a8<br>(this model)
+   </th>
+   <th>Recovery
+   </th>
+  </tr>
+  <tr>
+   <td>MMLU (5-shot)
+   </td>
+   <td>74.24
+   </td>
+   <td>73.87
+   </td>
+   <td>99.5%
+   </td>
+  </tr>
+  <tr>
+   <td>ARC Challenge (25-shot)
+   </td>
+   <td>63.40
+   </td>
+   <td>63.23
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td>GSM-8K (5-shot, strict-match)
+   </td>
+   <td>80.36
+   </td>
+   <td>80.74
+   </td>
+   <td>100.5%
+   </td>
+  </tr>
+  <tr>
+   <td>Hellaswag (10-shot)
+   </td>
+   <td>81.52
+   </td>
+   <td>81.06
+   </td>
+   <td>99.4%
+   </td>
+  </tr>
+  <tr>
+   <td>Winogrande (5-shot)
+   </td>
+   <td>74.66
+   </td>
+   <td>74.82
+   </td>
+   <td>100.2%
+   </td>
+  </tr>
+  <tr>
+   <td>TruthfulQA (0-shot, mc2)
+   </td>
+   <td>64.76
+   </td>
+   <td>64.58
+   </td>
+   <td>99.7%
+   </td>
+  </tr>
+  <tr>
+   <td><strong>Average</strong>
+   </td>
+   <td><strong>73.16</strong>
+   </td>
+   <td><strong>73.05</strong>
+   </td>
+   <td><strong>99.4%</strong>
+   </td>
+  </tr>
+</table>
+