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unsloth/Seed-Coder-8B-Instruct-bnb-4bit
Seed-Coder-8B-Instruct-bnb-4bit is a text generation model from unsloth. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
<div <p style="margin-top: 0;margin-bottom: 0;" <em<a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf"Unsloth Dynamic 2.0</a achieves superior accuracy & outperforms other leading quants.</em </p <div s…
Downloads · 30 days
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From the Hugging Face model README
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 | 🤗 Model | Pretrained on our model-centric code data. |
| 👉 Seed-Coder-8B-Instruct | 32K | 🤗 Model | Instruction-tuned for alignment with user intent. |
| Seed-Coder-8B-Reasoning | 64K | 🤗 Model | RL trained to boost reasoning capabilities. |
| Seed-Coder-8B-Reasoning-bf16 | 64K | 🤗 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 – 2502) |
|---|---|---|---|---|---|---|
| CodeLlama-7B-Instruct | 40.9 | 54.0 | 6.7 | 25.7 | 4.1 | 3.6 |
| DeepSeek-Coder-6.7B-Instruct | 74.4 | 74.9 | 20.0 | 43.8 | 15.5 | 9.6 |
| CodeQwen1.5-7B-Chat | 83.5 | 77.7 | 17.6 | 43.6 | 15.5 | 3.0 |
| Yi-Coder-9B-Chat | 82.3 | 82.0 | 26.7 | 49.0 | 17.6 | 17.5 |
| Llama-3.1-8B-Instruct | 68.3 | 70.1 | 17.1 | 40.5 | 13.5 | 11.5 |
| OpenCoder-8B-Instruct | 83.5 | 79.1 | 30.5 | 50.9 | 18.9 | 17.1 |
| Qwen2.5-Coder-7B-Instruct | 88.4 | 83.5 | 26.7 | 48.8 | 20.3 | 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 | 26.4 | 24.7 |
For detailed benchmark performance, please refer to our 📑 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}, } ``` -->