--- tags: - fp8 - vllm license: other license_name: deepseek-license license_link: https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL --- # DeepSeek-Coder-V2-Lite-Instruct-FP8 ## Model Overview - **Model Architecture:** DeepSeek-Coder-V2-Lite-Instruct - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Meta-Llama-3-7B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-7B-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:** 7/18/2024 - **Version:** 1.0 - **License(s):** [deepseek-license](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL) - **Model Developers:** Neural Magic Quantized version of [DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct). It achieves an average score of 79.60 on the [HumanEval+](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark, whereas the unquantized model achieves 79.33. ### Model Optimizations This model was obtained by quantizing the weights and activations of [DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) to FP8 data type, ready for inference with vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized. Symmetric per-tensor quantization is applied, in which a single linear scaling maps the FP8 representations of the quantized weights and activations. [AutoFP8](https://github.com/neuralmagic/AutoFP8) is used for quantization with 512 sequences of UltraChat. ## 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/DeepSeek-Coder-V2-Lite-Instruct-FP8" 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, trust_remote_code=True, max_model_len=4096) 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 This model was created by applying [AutoFP8 with calibration samples from ultrachat](https://github.com/neuralmagic/AutoFP8/blob/147fa4d9e1a90ef8a93f96fc7d9c33056ddc017a/example_dataset.py) with expert gates kept at original precision, as presented in the code snipet below. Although AutoFP8 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 AutoFP8. ```python from datasets import load_dataset from transformers import AutoTokenizer from auto_fp8 import AutoFP8ForCausalLM, BaseQuantizeConfig pretrained_model_dir = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct" quantized_model_dir = "DeepSeek-Coder-V2-Lite-Instruct-FP8" tokenizer = AutoTokenizer.from_pretrained(pretrained_model_dir, use_fast=True, model_max_length=4096) tokenizer.pad_token = tokenizer.eos_token ds = load_dataset("mgoin/ultrachat_2k", split="train_sft").select(range(512)) examples = [tokenizer.apply_chat_template(batch["messages"], tokenize=False) for batch in ds] examples = tokenizer(examples, padding=True, truncation=True, return_tensors="pt").to("cuda") quantize_config = BaseQuantizeConfig( quant_method="fp8", activation_scheme="static" ignore_patterns=["re:.*lm_head"], ) model = AutoFP8ForCausalLM.from_pretrained( pretrained_model_dir, quantize_config=quantize_config ) model.quantize(examples) model.save_quantized(quantized_model_dir) ``` ## Evaluation The model was evaluated on the [HumanEval+](https://github.com/openai/human-eval?tab=readme-ov-file) benchmark with the [Neural Magic fork](https://github.com/neuralmagic/evalplus) of the [EvalPlus implementation of HumanEval+](https://github.com/evalplus/evalplus) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following command: ``` python codegen/generate.py --model neuralmagic/DeepSeek-Coder-V2-Lite-Instruct-FP8 --temperature 0.2 --n_samples 50 --resume --root ~ --dataset humaneval python evalplus/sanitize.py ~/humaneval/neuralmagic--DeepSeek-Coder-V2-Lite-Instruct-FP8_vllm_temp_0.2 evalplus.evaluate --dataset humaneval --samples ~/humaneval/neuralmagic--DeepSeek-Coder-V2-Lite-Instruct-FP8_vllm_temp_0.2-sanitized ``` ### Accuracy #### HumanEval+ evaluation scores
Benchmark DeepSeek-Coder-V2-Lite-Instruct DeepSeek-Coder-V2-Lite-Instruct-FP8(this model) Recovery
base pass@1 80.8 79.3 98.14%
base pass@10 83.4 84.6 101.4%
base+extra pass@1 75.8 74.9 98.81%
base+extra pass@10 77.3 79.6 102.9%
Average 79.33 79.60 100.3%