commit 3e3a9945f05278325972eabf18a8944b1b298a3b Author: ModelHub XC Date: Wed Aug 12 12:35:12 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: RedHatAI/Qwen2-0.5B-Instruct-quantized.w4a16 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..4aa8f3a --- /dev/null +++ b/README.md @@ -0,0 +1,260 @@ +--- +language: +- en +pipeline_tag: text-generation +license: apache-2.0 +license_link: https://www.apache.org/licenses/LICENSE-2.0 +--- + +# Qwen2-0.5B-Instruct-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 in English. Similarly to [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct), 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). Use in languages other than English. +- **Release Date:** 7/11/2024 +- **Version:** 1.0 +- **License(s):** [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) +- **Model Developers:** Neural Magic + +Quantized version of [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct). +It achieves an average score of 40.88 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 43.05. + +### Model Optimizations + +This model was obtained by quantizing the weights of [Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) 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 25%. + +Only the weights of the linear operators within transformers blocks are quantized. Symmetric group-wise quantization is applied, in which a linear scaling per group maps the INT4 and floating point representations of the quantized weights. +[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization with 1% damping factor, group-size as 128 and 512 sequences sampled from [Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus). + + +## Deployment + +### Use with vLLM + +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 = "neuralmagic/Qwen2-0.5B-Instruct-quantized.w4a16" + +sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256) + +tokenizer = AutoTokenizer.from_pretrained(model_id) + +messages = [ + {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, + {"role": "user", "content": "Who are you?"}, +] + +prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) + +llm = LLM(model=model_id, tensor_parallel_size=1) + +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. + +### Use with transformers + +This model is supported by Transformers leveraging the integration with the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) data format. +The following example contemplates how the model can be used using the `generate()` function. + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM + +model_id = "neuralmagic/Qwen2-0.5B-Instruct-quantized.w4a16" + +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype="auto", + device_map="auto", +) + +messages = [ + {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, + {"role": "user", "content": "Who are you?"}, +] + +input_ids = tokenizer.apply_chat_template( + messages, + add_generation_prompt=True, + return_tensors="pt" +).to(model.device) + +terminators = [ + tokenizer.eos_token_id, + tokenizer.convert_tokens_to_ids("<|eot_id|>") +] + +outputs = model.generate( + input_ids, + max_new_tokens=256, + eos_token_id=terminators, + do_sample=True, + temperature=0.6, + top_p=0.9, +) +response = outputs[0][input_ids.shape[-1]:] +print(tokenizer.decode(response, skip_special_tokens=True)) +``` + +## Creation + +This model was created by applying the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library as presented in the code snipet below. +Although AutoGPTQ was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoGPTQ. + +```python +from transformers import AutoTokenizer +from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig +from datasets import load_dataset +import random + +model_id = "Qwen/Qwen2-0.5B-Instruct" + +num_samples = 512 +max_seq_len = 4096 + +tokenizer = AutoTokenizer.from_pretrained(model_id) + +preprocess_fn = lambda example: {"text": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n{text}".format_map(example)} + +dataset_name = "neuralmagic/LLM_compression_calibration" +dataset = load_dataset(dataset_name, split="train") +ds = dataset.shuffle().select(range(num_samples)) +ds = ds.map(preprocess_fn) + +examples = [ + tokenizer( + example["text"], padding=False, max_length=max_seq_len, truncation=True, + ) for example in ds +] + +quantize_config = BaseQuantizeConfig( + bits=4, + group_size=128, + desc_act=True, + model_file_base_name="model", + damp_percent=0.01, +) + +model = AutoGPTQForCausalLM.from_pretrained( + model_id, + quantize_config, + device_map="auto", +) + +model.quantize(examples) +model.save_pretrained("Qwen2-0.5B-Instruct-quantized.w4a16") +``` + + + +## 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/383bbd54bc621086e05aa1b030d8d4d5635b25e6) (commit 383bbd54bc621086e05aa1b030d8d4d5635b25e6) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command: +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Qwen2-0.5B-Instruct-quantized.w4a16",dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.4,add_bos_token=True,max_model_len=4096 \ + --tasks openllm \ + --batch_size auto +``` + +### Accuracy + +#### Open LLM Leaderboard evaluation scores + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Benchmark + Qwen2-0.5B-Instruct + Qwen2-0.5B-Instruct-quantized.w4a16(this model) + Recovery +
MMLU (5-shot) + 43.71 + 41.41 + 94.75% +
ARC Challenge (25-shot) + 31.82 + 30.20 + 94.92% +
GSM-8K (5-shot, strict-match) + 37.68 + 30.09 + 79.87% +
Hellaswag (10-shot) + 49.5 + 47.23 + 95.41% +
Winogrande (5-shot) + 56.27 + 56.27 + 100.00% +
TruthfulQA (0-shot) + 39.37 + 40.07 + 101.78% +
Average + 49.94 + 40.88 + 81.85% +
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