Seed Coder 8B Reasoning GGUF

🚀 Seed-Coder-8B-Reasoning

Seed-Coder-8B-Reasoning is a powerful open - source code model. It features high - performance in coding tasks, with a focus on reasoning capabilities. It also has a model - centric and transparent data pipeline.

🚀 Quick Start

Prerequisites

You will need to install the latest versions of transformers and accelerate:

pip install -U transformers accelerate

Example Code

Here is a simple example demonstrating how to load the model and perform code generation using the Hugging Face pipeline API:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "ByteDance-Seed/Seed-Coder-8B-Reasoning"

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)

✨ Features

Overall Features of Seed - Coder

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.

Features of Seed - Coder - 8B - Reasoning

This repo contains 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

📦 Installation

No specific installation steps other than the prerequisite library installation are provided in the original document.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "ByteDance-Seed/Seed-Coder-8B-Reasoning"

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)

📚 Documentation

Model Downloads

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).

Information Table

📄 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.

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