README.md

---

license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-7B/blob/main/LICENSE
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
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>