---
library_name: exllamav2
pipeline_tag: text-generation
license: mit
base_model:
- ByteDance-Seed/Seed-Coder-8B-Reasoning
---
# Seed-Coder-8B-Reasoning-exl2
Original model: [Seed-Coder-8B-Reasoning-bf16](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Reasoning-bf16) by [ByteDance Seed](https://huggingface.co/ByteDance-Seed)
## Quants
[4bpw h6 (main)](https://huggingface.co/cgus/Seed-Coder-8B-Reasoning-exl2/tree/main)
[4.5bpw h6](https://huggingface.co/cgus/Seed-Coder-8B-Reasoning-exl2/tree/4.5bpw-h6)
[5bpw h6](https://huggingface.co/cgus/Seed-Coder-8B-Reasoning-exl2/tree/5bpw-h6)
[6bpw h6](https://huggingface.co/cgus/Seed-Coder-8B-Reasoning-exl2/tree/6bpw-h6)
[8bpw h8](https://huggingface.co/cgus/Seed-Coder-8B-Reasoning-exl2/tree/8bpw-h8)
## Quantization notes
Made with Exllamav2 0.2.9 dev with default dataset.
These quants can be used with RTX GPU (Windows) or RTX/ROCm (Linux) with TabbyAPI or Text-Generation-WebUI.
Ensure it fits your VRAM since RAM offloading isn't supported by Exllamav2 library.
# Seed-Coder-8B-Reasoning-bf16
## Introduction
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 is the **bf16 version** of the Seed-Coder-8B-Reasoning model, which has the following features:
- Type: Causal language models
- Training Stage: Pretraining & Post-training
- Data Source: Public datasets
- Context Length: 65,536
## Model Downloads
| Model Name | Length | Download | Notes |
|---------------------------------------------------------|-----------|------------------------------------|-----------------------|
| Seed-Coder-8B-Base | 32K | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base) | Pretrained on our model-centric code data. |
| Seed-Coder-8B-Instruct | 32K | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Instruct) | Instruction-tuned for alignment with user intent. |
| Seed-Coder-8B-Reasoning | 32K | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Reasoning) | RL trained to boost reasoning capabilities. |
| 👉 **Seed-Coder-8B-Reasoning** (bf16) | 32K | 🤗 [Model](https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Reasoning-bf16) | RL trained to boost reasoning capabilities. This is the **bf16 version**. |
## Requirements
You will need to install the latest versions of `transformers` and `accelerate`:
```bash
pip install -U transformers accelerate
```
## Quickstart
Here is a simple example demonstrating how to load the model and perform code generation using the Hugging Face `pipeline` API:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "ByteDance-Seed/Seed-Coder-8B-Reasoning-bf16"
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=16384)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
```
## Evaluation
Seed-Coder-8B-Reasoning strikes impressive performance on competitive programming, demonstrating that smaller LLMs can also be competent on complex reasoning tasks. Our model surpasses QwQ-32B and DeepSeek-R1 on IOI'2024, and achieves an ELO rating comparable to o1-mini on Codeforces contests.
For detailed benchmark performance, please refer to our [📑 Technical Report](https://github.com/ByteDance-Seed/Seed-Coder/blob/master/Seed-Coder.pdf).
## 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.
-Technical%20Report-brightgreen?logo=arxiv&logoColor=white){kind=icon}
