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see the proof: Seed-Coder/imgs/seed-coder_intro_performance.jpg at master · ByteDance-Seed/Seed-Coder · GitHub

LOOKS LIKE CHINESE AI AIN’T HITTING ANY WALLS.

BIG UPDATE: GitHub - ByteDance-Seed/Seed-Coder: Seed-Coder is a family of open-source code LLMs comprising base, instruct and reasoning models of 8B size, developed by ByteDance Seed.

To run the Seed-Coder model from the GitHub repository, you need to follow the instructions provided in the repository's README. Below is a step-by-step guide based on the information available in the README and related sources. Note that the exact steps may depend on your system setup and whether you want to use the model for inference, fine-tuning, or development.

Prerequisites

Before running Seed-Coder, ensure you have the following:

Hardware:

A machine with a GPU (NVIDIA GPUs are recommended for optimal performance).

Sufficient memory (e.g., 16GB+ GPU VRAM for the 8B model, depending on the configuration).

Software:

Python 3.8 or higher.

PyTorch (preferably with CUDA support for GPU acceleration).

A package manager like pip or conda.

Git (to clone the repository).

Dependencies:

The repository likely depends on libraries like transformers, vLLM, or llama.cpp for model inference, as mentioned in related posts.

Additional dependencies may be listed in a requirements.txt file or the README.

Step-by-Step Instructions

  1. Clone the Repository: Open a terminal and clone the Seed-Coder repository:

    bash

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    git clone https://github.com/ByteDance-Seed/Seed-Coder.git cd Seed-Coder

    This downloads the repository to your local machine.

  2. Install Dependencies: The README may include a requirements.txt file or specific installation instructions. If a requirements.txt file exists, install the dependencies using:

    bash

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    pip install -r requirements.txt

    If no requirements.txt is provided, you may need to install key libraries manually. Based on related posts, Seed-Coder works with transformers, vLLM, and llama.cpp. Install these as follows:

    bash

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    pip install torch transformers vllm

    For llama.cpp, you may need to follow its specific installation instructions (e.g., cloning its repository and building it).

    If you’re using a conda environment, create and activate one first:

    bash

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    conda create -n seed-coder python=3.10 conda activate seed-coder

  3. Download the Model Weights: Seed-Coder is a family of 8B models (base, instruct, and reasoning). The README likely provides links to model weights hosted on platforms like Hugging Face. For example:

    • Check the repository for a link to the model on Hugging Face (e.g., ByteDance-Seed/Seed-Coder-8B).

    • Download the model weights manually or use the transformers library to load them directly (see step 4).

    If the weights are not publicly available, the README may instruct you to contact the ByteDance Seed team for access.

  4. Run the Model: Seed-Coder supports multiple frameworks (transformers, vLLM, llama.cpp). Here’s how to run it with each:

    Option 1: Using transformers (Hugging Face)

    If the model is hosted on Hugging Face, you can load and run it using the transformers library. Example code:

    python

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    from transformers import AutoModelForCausalLM, AutoTokenizer # Load the model and tokenizer model_name = "ByteDance-Seed/Seed-Coder-8B" # Replace with the actual model name tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") # Prepare input prompt = "Write a Python function to calculate the factorial of a number." inputs = tokenizer(prompt, return_tensors="pt").to("cuda") # Generate output outputs = model.generate(**inputs, max_length=200) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

    Ensure your GPU is set up with CUDA, and adjust torch_dtype or device_map based on your hardware.

    Option 2: Using vLLM

    vLLM is a high-performance inference engine. If the README recommends vLLM, install it (as shown in step 2) and run the model as follows:

    python

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    from vllm import LLM, SamplingParams # Initialize the model model = LLM(model="ByteDance-Seed/Seed-Coder-8B", dtype="bfloat16", tensor_parallel_size=1) # Set sampling parameters sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=512) # Generate output prompt = "Write a Python function to calculate the factorial of a number." outputs = model.generate([prompt], sampling_params) print(outputs[0].outputs[0].text)

    Adjust tensor_parallel_size based on the number of GPUs available.

    Option 3: Using llama.cpp

    If you prefer llama.cpp for CPU or lightweight GPU inference, follow its setup instructions to compile the library. Then, convert the model weights to the GGUF format (if required) and run inference using llama.cpp’s command-line tools or Python bindings. The README may provide specific instructions for this.

  5. Run Example Scripts: The repository may include example scripts in the examples/ folder or similar. Check the README for specific commands, such as:

    bash

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    python examples/run_inference.py --model ByteDance-Seed/Seed-Coder-8B --prompt "Your prompt here"

    Replace the model name and prompt as needed.

  6. Fine-Tuning (Optional): If you want to fine-tune the model, the README may provide instructions for preparing datasets and running training scripts. This typically involves:

    • Preparing a dataset in a format compatible with the model.

    • Using a training framework like PyTorch or transformers.

    • Running a training script, e.g.:

      bash

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      python train.py --model ByteDance-Seed/Seed-Coder-8B --dataset your_dataset

    Check the repository for detailed fine-tuning instructions.

  7. Troubleshooting:

    • GPU Memory Issues: If you encounter out-of-memory errors, reduce the batch size, use torch_dtype=torch.float16, or enable model parallelism with vLLM.

    • Missing Dependencies: Ensure all required libraries are installed. Check the README or error messages for clues.

    • Model Access: If the model weights are not publicly available, follow the README’s instructions to request access from ByteDance Seed.

Additional Notes

  • License: Seed-Coder is MIT-licensed, so you can use it freely for research or commercial purposes, subject to the license terms.

  • Performance: Posts on X suggest that Seed-Coder outperforms models like Qwen Coder and DeepSeek Coder, making it a strong choice for code-related tasks.

  • Documentation: The README is your primary source for setup instructions. If it’s unclear or incomplete, check the repository’s issues section or related Hugging Face model cards for additional guidance.

  • System-Specific Setup:

    • Linux: The above steps should work directly.

    • Windows: Use WSL2 or a Linux VM for easier setup, as some dependencies (e.g., llama.cpp) are better supported on Linux.

    • Mac: For CPU inference, llama.cpp is a good option. GPU support may be limited unless using Apple Silicon with specific frameworks.

If You Encounter Issues

  • Check the README: Ensure you’re following the exact instructions provided.

  • Search Issues: Look at the repository’s GitHub Issues page for similar problems.

  • Community Help: Check discussions on platforms like Hugging Face or X for user tips. For example, posts on X highlight compatibility with transformers, vLLM, and llama.cpp.

  • Contact Maintainers: If the README mentions a contact (e.g., an email like seed@bytedance.com), reach out for clarification.

If you provide more details about your system (e.g., OS, GPU, preferred framework) or specific errors you encounter, I can tailor the instructions further. Let me know!