--- title: starcoder2-15b-quantized.w8a8 canonical_url: "https://www.modelscope.cn/models/neuralmagic/starcoder2-15b-quantized.w8a8" md_url: "https://www.modelscope.cn/models/neuralmagic/starcoder2-15b-quantized.w8a8.md" repository: neuralmagic/starcoder2-15b-quantized.w8a8 last_updated: 2026-08-24 license: bigcode-openrail-m pipeline_tag: text-generation tasks: - text-generation model_type: - starcoder2 architectures: - Starcoder2ForCausalLM parameters: 16.0B tensor_type: - F32 - I8 library_name: - transformer - safetensors - pytorch frameworks: - pytorch downloads: 2402 stars: 0 tags: - code --- # starcoder2-15b-quantized.w8a8 > starcoder2-15b-quantized.w8a8 - neuralmagic 在 ModelScope 开源的模型。starcoder2-3b-quantized.w8a8 neuralmagic/starcoder2-15b-quantized.w8a8 是 ModelScope 魔搭社区上的 16.0B 参数text-generation模型，采用 bigcode-openrail-m 许可。 - **Repository**: neuralmagic/starcoder2-15b-quantized.w8a8 - **License**: bigcode-openrail-m - **Tasks**: text-generation - **Parameters**: 16.0B - **Tags**: code - **Downloads**: 2402 - **Stars**: 0 - **Last updated**: 2026-08-24 Source: https://www.modelscope.cn/models/neuralmagic/starcoder2-15b-quantized.w8a8 --- # starcoder2-3b-quantized.w8a8 ## Model Overview - **Model Architecture:** StarCoder2 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Activation quantization:** INT8 - **Weight quantization:** INT8 - **Intended Use Cases:** Intended for commercial and research use. Similarly to [starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b), this model is intended for code generation and is _not_ an instruction model. Commands like "Write a function that computes the square root." do not work well. - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). - **Release Date:** 8/1/2024 - **Version:** 1.0 - **License(s):** bigcode-openrail-m - **Model Developers:** Neural Magic Quantized version of [starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b). It achieves a HumanEval pass@1 of 44.6, whereas the unquantized model achieves 44.8 when evaluated under the same conditions. ### Model Optimizations This model was obtained by quantizing the weights of [starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) 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, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension. Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations. 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. GPTQ used a 1% damping factor and 256 sequences of 8,192 random tokens. ## 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/starcoder2-15b-quantized.w8a8" number_gpus = 1 sampling_params = SamplingParams(temperature=0.2, top_p=0.95, max_tokens=256) tokenizer = AutoTokenizer.from_pretrained(model_id) prompts = ["def print_hello_world():"] llm = LLM(model=model_id, tensor_parallel_size=number_gpus) 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 using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below. ```python from transformers import AutoTokenizer from datasets import Dataset from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot from llmcompressor.modifiers.quantization import GPTQModifier import random model_id = "bigcode/starcoder2-15b" num_samples = 256 max_seq_len = 8192 tokenizer = AutoTokenizer.from_pretrained(model_id) max_token_id = len(tokenizer.get_vocab()) - 1 input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)] attention_mask = num_samples * [max_seq_len * [1]] ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask}) recipe = GPTQModifier( targets="Linear", scheme="W8A8", ignore=["lm_head"], dampening_frac=0.01, ) model = SparseAutoModelForCausalLM.from_pretrained( model_id, device_map="auto", trust_remote_code=True, ) oneshot( model=model, dataset=ds, recipe=recipe, max_seq_length=max_seq_len, num_calibration_samples=num_samples, ) model.save_pretrained("starcoder2-15b-quantized.w8a8") ``` ## Evaluation The model was evaluated on the [HumanEval](https://arxiv.org/abs/2107.03374) and [HumanEval+](https://arxiv.org/abs/2305.01210) benchmarks, using the generation configuration from [Big Code Models Leaderboard](https://huggingface.co/spaces/bigcode/bigcode-models-leaderboard). We used Neural Magic's fork of [evalplus](https://github.com/neuralmagic/evalplus) and the [vLLM](https://docs.vllm.ai/en/stable/) engine, using the following commands: ``` python codegen/generate.py \ --model neuralmagic/starcoder2-15b-quantized.w8a8 \ --bs 16 \ --temperature 0.2 \ --n_samples 50 \ --dataset humaneval \ -- root "." python3 evalplus/sanitize.py humaneval/neuralmagic--starcoder2-15b-quantized.w8a8_vllm_temp_0.2 evalplus.evaluate --dataset humaneval --samples humaneval/neuralmagic--starcoder2-15b-quantized.w8a8_vllm_temp_0.2-sanitized ``` ### Accuracy
Benchmark starcoder2-15b starcoder2-15b-quantized.w8a8 (this model) Recovery
HumanEval pass@1 44.8 44.6 99.6%
HumanEval pass@10 62.7 63.3 101.0%
HumanEval+ pass@1 38.6 38.1 98.7%
HumanEval+ pass@10 54.9 55.5 101.1%