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cgus/Seed-Coder-8B-Instruct-exl2
Seed-Coder-8B-Instruct-exl2 is a machine learning model from cgus. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for exllamav2. The card lists the license as mit.
Original model: Seed-Coder-8B-Instruct by ByteDance Seed 4bpw h6 (main) 4.5bpw h6
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From the Hugging Face model README
Original model: Seed-Coder-8B-Instruct by ByteDance Seed
4bpw h6 (main)
4.5bpw h6
5bpw h6
6bpw h6
8bpw h8
Made with Exllamav2 0.2.9 dev with default dataset.
Quants can be used with RTX GPU (Windows) or RTX/ROCm (Linux) with TabbyAPI or Text-Generation-WebUI.
We are thrilled to introduce Seed-Coder, a powerful, transparent, and parameter-efficient family of open-source code models at the 8B scale, featuring base, instruct, and reasoning variants. Seed-Coder contributes to promote the evolution of open code models through the following highlights.
This repo contains the Seed-Coder-8B-Instruct model, which has the following features:
| Model Name | Length | Download | Notes |
|---|---|---|---|
| Seed-Coder-8B-Base | 32K | ๐ค Model | Pretrained on our model-centric code data. |
| ๐ Seed-Coder-8B-Instruct | 32K | ๐ค Model | Instruction-tuned for alignment with user intent. |
| Seed-Coder-8B-Reasoning | 32K | ๐ค Model | RL trained to boost reasoning capabilities. |
You will need to install the latest versions of transformers and accelerate:
pip install -U transformers accelerate
Here is a simple example demonstrating how to load the model and generate code using the Hugging Face pipeline API:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "ByteDance-Seed/Seed-Coder-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
messages = [
{"role": "user", "content": "Write a quick sort algorithm."},
]
input_ids = tokenizer.apply_chat_template(
messages,
tokenize=True,
return_tensors="pt",
add_generation_prompt=True,
).to(model.device)
outputs = model.generate(input_ids, max_new_tokens=512)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
Seed-Coder-8B-Instruct has been evaluated on a wide range of coding tasks, including code generation, code reasoning, code editing, and software engineering, achieving state-of-the-art performance among ~8B open-source models.
| Model | HumanEval | MBPP | MHPP | BigCodeBench (Full) | BigCodeBench (Hard) | LiveCodeBench (2410 โ 2502) |
|---|---|---|---|---|---|---|
| CodeLlama-7B-Instruct | 40.9 | 54.0 | 6.7 | 21.9 | 3.4 | 3.6 |
| DeepSeek-Coder-6.7B-Instruct | 74.4 | 74.9 | 20.0 | 35.5 | 10.1 | 9.6 |
| CodeQwen1.5-7B-Chat | 83.5 | 77.7 | 17.6 | 39.6 | 18.9 | 3.0 |
| Yi-Coder-9B-Chat | 82.3 | 82.0 | 26.7 | 38.1 | 11.5 | 17.5 |
| Llama-3.1-8B-Instruct | 68.3 | 70.1 | 17.1 | 36.6 | 13.5 | 11.5 |
| OpenCoder-8B-Instruct | 83.5 | 79.1 | 30.5 | 40.3 | 16.9 | 17.1 |
| Qwen2.5-Coder-7B-Instruct | 88.4 | 82.0 | 26.7 | 41.0 | 18.2 | 17.3 |
| Qwen3-8B | 84.8 | 77.0 | 32.8 | 51.7 | 23.0 | 23.5 |
| Seed-Coder-8B-Instruct | 84.8 | 85.2 | 36.2 | 53.3 | 20.5 | 24.7 |
For detailed benchmark performance, please refer to our ๐ Technical Report.
This project is licensed under the MIT License. See the LICENSE file for details.
<!-- ## Citation If you find our work helpful, feel free to give us a cite. ``` @article{zhang2025seedcoder, title={Seed-Coder: Let the Code Model Curate Data for Itself}, author={Xxx}, year={2025}, eprint={2504.xxxxx}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/xxxx.xxxxx}, } ``` -->