Seed Coder 8B Reasoning GGUF

🚀 Seed-Coder-8B-Reasoning GGUF Models

This project offers a range of GGUF models based on Seed-Coder-8B-Reasoning, with various quantization methods and model formats to meet different hardware and performance requirements.

🚀 Quick Start

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 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

Model Generation Details

This model was generated using llama.cpp at commit 5787b5da.

Quantization beyond the IMatrix

Testing a new quantization method using rules to bump important layers above what the standard imatrix would use. I have found that the standard IMatrix does not perform very well at low bit quantization and for MOE models. So I am using llama.cpp --tensor-type to bump up selected layers. See Layer bumping with llama.cpp This does create larger model files but increases precision for a given model size.

Please provide feedback on how you find this method performs

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

• BF16 (Brain Float 16) – Use if BF16 acceleration is available
  • A 16-bit floating-point format designed for faster computation while retaining good precision.
  • Provides similar dynamic range as FP32 but with lower memory usage.
  • Recommended if your hardware supports BF16 acceleration (check your device's specs).
  • Ideal for high-performance inference with reduced memory footprint compared to FP32.
  • Use BF16 if:
    • Your hardware has native BF16 support (e.g., newer GPUs, TPUs).
    • You want higher precision while saving memory.
    • You plan to requantize the model into another format.
  • Avoid BF16 if:
    • Your hardware does not support BF16 (it may fall back to FP32 and run slower).
    • You need compatibility with older devices that lack BF16 optimization.
• F16 (Float 16) – More widely supported than BF16
  • A 16-bit floating-point high precision but with less of range of values than BF16.
  • Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
  • Slightly lower numerical precision than BF16 but generally sufficient for inference.
  • Use F16 if:
    • Your hardware supports FP16 but not BF16.
    • You need a balance between speed, memory usage, and accuracy.
    • You are running on a GPU or another device optimized for FP16 computations.
  • Avoid F16 if:
    • Your device lacks native FP16 support (it may run slower than expected).
    • You have memory limitations.
• Hybrid Precision Models (e.g., bf16_q8_0, f16_q4_K) – Best of Both Worlds
  • These formats selectively quantize non-essential layers while keeping key layers in full precision (e.g., attention and output layers).
  • Named like bf16_q8_0 (meaning full-precision BF16 core layers + quantized Q8_0 other layers).
  • Strike a balance between memory efficiency and accuracy, improving over fully quantized models without requiring the full memory of BF16/F16.
  • Use Hybrid Models if:
    • You need better accuracy than quant-only models but can’t afford full BF16/F16 everywhere.
    • Your device supports mixed-precision inference.
    • You want to optimize trade-offs for production-grade models on constrained hardware.
  • Avoid Hybrid Models if:
    • Your target device doesn’t support mixed or full-precision acceleration.
    • You are operating under ultra-strict memory limits (in which case use fully quantized formats).
• Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference
  • Quantization reduces model size and memory usage while maintaining as much accuracy as possible.
  • Lower-bit models (Q4_K) – Best for minimal memory usage, may have lower precision.
  • Higher-bit models (Q6_K, Q8_0) – Better accuracy, requires more memory.
  • Use Quantized Models if:
    • You are running inference on a CPU and need an optimized model.
    • Your device has low VRAM and cannot load full-precision models.
    • You want to reduce memory footprint while keeping reasonable accuracy.
  • Avoid Quantized Models if:
    • You need maximum accuracy (full-precision models are better for this).
    • Your hardware has enough VRAM for higher-precision formats (BF16/F16).
• Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)
  • These models are optimized for very high memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.
  • IQ3_XS: Ultra-low-bit quantization (3-bit) with very high memory efficiency.
    • Use case: Best for ultra-low-memory devices where even Q4_K is too large.
    • Trade-off: Lower accuracy compared to higher-bit quantizations.
  • IQ3_S: Small block size for maximum memory efficiency.
    • Use case: Best for low-memory devices where IQ3_XS is too aggressive.
  • IQ3_M: Medium block size for better accuracy than IQ3_S.
    • Use case: Suitable for low-memory devices where IQ3_S is too limiting.
  • Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
    • Use case: Best for low-memory devices where Q6_K is too large.
  • Q4_0: Pure 4-bit quantization, optimized for ARM devices.
    • Use case: Best for ARM-based devices or low-memory environments.
• Ultra Low-Bit Quantization (IQ1_S IQ1_M IQ2_S IQ2_M IQ2_XS IQ2_XSS)
  • Ultra-low-bit quantization (1 2-bit) with extreme memory efficiency.
  • Use case: Best for cases were you have to fit the model into very constrained memory
  • Trade-off: Very Low Accuracy. May not function as expected. Please test fully before using.

Summary Table: Model Format Selection

📚 Documentation

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 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

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.

Testing the Free Network Monitor Assistant

Help me test my AI-Powered Free Network Monitor Assistant with quantum-ready security checks: Free Network Monitor

The full Open Source Code for the Free Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Free Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

How to test

Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space)

• ✅ Zero-configuration setup
• ⏱️ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🙏 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

• 💪 TurboLLM – Uses gpt-4.1-mini :
  • It performs very well but unfortunately OpenAI charges per token. For this reason tokens usage is limited.
  • Create custom cmd processors to run .net code on Free Network Monitor Agents
  • Real-time network diagnostics and monitoring
  • Security Audits
  • Penetration testing (Nmap/Metasploit)
• 🤗 HugLLM – Latest Open-source models:
  • Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

Example commands you could test

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Free Network Monitor Agent to run the .net code from. This is a very flexible and powerful feature. Use with caution!

📄 License

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.

@misc{seed2025seedcoderletcodemodel,
      title={{Seed-Coder}: Let the Code Model Curate Data for Itself},
      author={{ByteDance Seed} and Yuyu Zhang and Jing Su and Yifan Sun and Chenguang Xi and Xia Xiao and Shen Zheng and Anxiang Zhang and Kaibo Liu and Daoguang Zan and Tao Sun and Jinhua Zhu and Shulin Xin and Dong Huang and Yetao Bai and Lixin Dong and Chao Li and Jianchong Chen and Hanzhi Zhou and Yifan Huang and Guanghan Ning and Xierui Song and Jiaze Chen and Siyao Liu and Kai Shen and Liang Xiang and Yonghui Wu},
      year={2025},
      eprint={2506.03524},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.03524},
}

Final Word

I fund the servers used to create these model files, run the Free Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Free Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you!