117 lines
3.5 KiB
Markdown
117 lines
3.5 KiB
Markdown
---
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen2.5-Coder-3B-Instruct
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tags:
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- llmcompressor
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- quantized
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- FP8
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---
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# Qwen2.5-Coder-3B-Instruct-FP8-dynamic
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## Model Overview
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- **Model Architecture:** Qwen2ForCausalLM
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- **Input:** Text
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- **Output:** Text
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- **Model Optimizations:**
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- **Activation quantization:** FP8
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- **Weight quantization:** FP8
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- **Release Date:** 09/06/2025
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- **Version:** 1.0
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- **Model Developers:** duydq12 (enhance by RedHatAI)
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### Model Optimizations
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This model was obtained by quantizing activations and weights of [Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) to FP8 data type.
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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).
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Weight quantization also reduces disk size requirements by approximately 50%.
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Only weights and activations of the linear operators within transformers blocks are quantized.
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Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme.
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The [llm-compressor](https://github.com/vllm-project/llm-compressor) library is used for quantization.
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## Deployment
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
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```python
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from vllm import LLM, SamplingParams
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from transformers import AutoTokenizer
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model_id = "duydq12/Qwen2.5-Coder-3B-Instruct-FP8-dynamic"
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number_gpus = 1
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sampling_params = SamplingParams(temperature=0.6, top_p=0.95, top_k=20, min_p=0, max_tokens=256)
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messages = [
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{"role": "user", "content": prompt}
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]
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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messages = [{"role": "user", "content": "Give me a short introduction to large language model."}]
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prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus)
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outputs = llm.generate(prompts, sampling_params)
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generated_text = outputs[0].outputs[0].text
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print(generated_text)
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```
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vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
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## Creation
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<details>
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<summary>Creation details</summary>
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This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below.
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```python
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from llmcompressor.modifiers.quantization import QuantizationModifier
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from llmcompressor.transformers import oneshot
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load model
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model_stub = "Qwen/Qwen2.5-Coder-3B-Instruct"
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model_name = model_stub.split("/")[-1]
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model = AutoModelForCausalLM.from_pretrained(model_stub, torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_stub, torch_dtype="auto", device_map="auto")
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# Configure the quantization algorithm and scheme
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recipe = QuantizationModifier(
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ignore=["lm_head"],
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targets="Linear",
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scheme="FP8_dynamic",
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)
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# Apply quantization
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oneshot(
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model=model,
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recipe=recipe,
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)
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# Save to disk in compressed-tensors format
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save_path = model_name + "-FP8-dynamic"
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model.save_pretrained(save_path)
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tokenizer.save_pretrained(save_path)
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print(f"Model and tokenizer saved to: {save_path}")
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```
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</details>
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## Evaluation
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private
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### Accuracy
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private
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