đ Seed-Coder-8B-Instruct
Seed-Coder-8B-Instruct is a powerful, transparent, and parameter - efficient open - source code model at the 8B scale, achieving state - of - the - art performance in various coding tasks.
đ Quick Start
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.
- Model - centric: Seed - Coder predominantly leverages LLMs instead of hand - crafted rules for code data filtering, minimizing manual effort in pretraining data construction.
- Transparent: We openly share detailed insights into our model - centric data pipeline, including methods for curating GitHub data, commits data, and code - related web data.
- Powerful: Seed - Coder achieves state - of - the - art performance among open - source models of comparable size across a diverse range of coding tasks.
This repo contains the Seed - Coder - 8B - Instruct model, which has the following features:
Property |
Details |
Model Type |
Causal language models |
Training Stage |
Pretraining & Post - training |
Data Source |
Public datasets, synthetic data |
Context Length |
32,768 |
đĻ Installation
You will need to install the latest versions of transformers
and accelerate
:
pip install -U transformers accelerate
đģ Usage Examples
Basic Usage
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)
đ Documentation
Model Downloads
Model Name |
Length |
Download |
Notes |
Seed - Coder - 8B - Base |
32K |
đ¤ [Model](https://huggingface.co/ByteDance - Seed/Seed - Coder - 8B - Base) |
Pretrained on our model - centric code data. |
đ Seed - Coder - 8B - Instruct |
32K |
đ¤ [Model](https://huggingface.co/ByteDance - Seed/Seed - Coder - 8B - Instruct) |
Instruction - tuned for alignment with user intent. |
Seed - Coder - 8B - Reasoning |
64K |
đ¤ [Model](https://huggingface.co/ByteDance - Seed/Seed - Coder - 8B - Reasoning) |
RL trained to boost reasoning capabilities. |
Seed - Coder - 8B - Reasoning - bf16 |
64K |
đ¤ [Model](https://huggingface.co/ByteDance - Seed/Seed - Coder - 8B - Reasoning - bf16) |
RL trained to boost reasoning capabilities. |
Evaluation
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 â 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 [đ Technical Report](https://github.com/ByteDance - Seed/Seed - Coder/blob/master/Seed - Coder.pdf).
đ License
This project is licensed under the MIT License. See the [LICENSE file](https://github.com/ByteDance - Seed/Seed - Coder/blob/master/LICENSE) for details.