初始化项目，由ModelHub XC社区提供模型
Model: RedHatAI/Qwen2.5-7B-quantized.w4a16 Source: Original Platform
This commit is contained in:
ModelHub XC
223
README.md
Normal file
223
README.md
Normal file
@@ -0,0 +1,223 @@
|
||||
---
|
||||
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
|
||||
tags:
|
||||
- chat
|
||||
- neuralmagic
|
||||
- llmcompressor
|
||||
- int4
|
||||
---
|
||||
|
||||
# Qwen2.5-7B-quantized.w4a16
|
||||
|
||||
## Model Overview
|
||||
- **Model Architecture:** Qwen2
|
||||
- **Input:** Text
|
||||
- **Output:** Text
|
||||
- **Model Optimizations:**
|
||||
- **Weight quantization:** INT4
|
||||
- **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:** 10/18/2024
|
||||
- **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 the weights of [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) 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/Qwen2.5-7B-quantized.w4a16"
|
||||
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)
|
||||
|
||||
prompt = "Give me a short introduction to large language model."
|
||||
|
||||
llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len)
|
||||
|
||||
outputs = llm.generate(prompt, 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.transformers import oneshot
|
||||
from datasets import load_dataset
|
||||
|
||||
# Load model
|
||||
model_stub = "Qwen/Qwen2.5-7B"
|
||||
model_name = model_stub.split("/")[-1]
|
||||
|
||||
num_samples = 3072
|
||||
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 = GPTQModifier(
|
||||
targets="Linear",
|
||||
scheme="W4A16",
|
||||
ignore=["lm_head"],
|
||||
sequential_targets=["Qwen2DecoderLayer"],
|
||||
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](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-quantized.w4a16",dtype=auto,gpu_memory_utilization=0.9,add_bos_token=True,max_model_len=4096,enable_chunk_prefill=True,tensor_parallel_size=1 \
|
||||
--tasks openllm \
|
||||
--batch_size auto
|
||||
```
|
||||
|
||||
### Accuracy
|
||||
|
||||
#### Open LLM Leaderboard evaluation scores
|
||||
<table>
|
||||
<tr>
|
||||
<th>Benchmark
|
||||
</th>
|
||||
<th>Qwen2.5-7B
|
||||
</th>
|
||||
<th>Qwen2.5-7B-quantized.w4a16<br>(this model)
|
||||
</th>
|
||||
<th>Recovery
|
||||
</th>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>MMLU (5-shot)
|
||||
</td>
|
||||
<td>74.15
|
||||
</td>
|
||||
<td>73.47
|
||||
</td>
|
||||
<td>99.1%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>ARC Challenge (25-shot)
|
||||
</td>
|
||||
<td>59.39
|
||||
</td>
|
||||
<td>58.70
|
||||
</td>
|
||||
<td>98.9%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>GSM-8K (5-shot, strict-match)
|
||||
</td>
|
||||
<td>79.76
|
||||
</td>
|
||||
<td>79.08
|
||||
</td>
|
||||
<td>99.1%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Hellaswag (10-shot)
|
||||
</td>
|
||||
<td>80.17
|
||||
</td>
|
||||
<td>79.39
|
||||
</td>
|
||||
<td>99.0%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>Winogrande (5-shot)
|
||||
</td>
|
||||
<td>75.69
|
||||
</td>
|
||||
<td>76.01
|
||||
</td>
|
||||
<td>100.4%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td>TruthfulQA (0-shot, mc2)
|
||||
</td>
|
||||
<td>56.38
|
||||
</td>
|
||||
<td>55.48
|
||||
</td>
|
||||
<td>98.4%
|
||||
</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<td><strong>Average</strong>
|
||||
</td>
|
||||
<td><strong>70.92</strong>
|
||||
</td>
|
||||
<td><strong>70.35</strong>
|
||||
</td>
|
||||
<td><strong>99.2%</strong>
|
||||
</td>
|
||||
</tr>
|
||||
</table>
|
||||
|
||||
Reference in New Issue
Block a user