wasmdashai / Seed-Coder-8B-Instruct-V1

Wasm-Coder-8B-Instruct-V1

Wasm-Coder-8B-Instruct-V1 is an 8-billion parameter instruction-tuned language model developed by wasmdashai , , code generation, and technical reasoning. It is designed to help developers working on edge computing, browser-based runtimes, and low-level systems programming.

🚀 Introduction

Wasm-Coder-8B-Instruct-V1 is part of the Wasm-Coder family—models specifically tailored for tasks involving WebAssembly, Rust, C/C++, and embedded systems programming. The model has been instruction-tuned on a diverse dataset combining code, documentation, compiler logs, and structured code reasoning tasks.

Key Features:

• Strong performance in code synthesis , bug fixing , and code explanation , especially for Rust and WebAssembly projects.
• Efficient for edge devices , browsers , and serverless runtimes .
• Based on a powerful transformer architecture with performance enhancements such as RoPE and SwiGLU.
• Trained with instruction-following datasets for natural conversations and multi-turn reasoning.
• Supports long-context processing (up to 32,768 tokens) with optional rope scaling.

🧠 Model Details

• Architecture : Decoder-only transformer

• Parameters : 8B

• Training : Pretrained + Instruction fine-tuning

• Supported Context Length : 32,768 tokens

• Specialization : WebAssembly, Rust, C/C++, Systems Programming

• Components :

  • RoPE (Rotary Positional Embeddings)
  • SwiGLU activation
  • RMSNorm
  • QKV Attention Bias

💻 Quickstart

Install dependencies:

pip install --upgrade transformers

Example code to load and run the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "wasmdashai/Wasm-Coder-8B-Instruct-V1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write a Rust function that compiles to WebAssembly and adds two numbers."

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(result)

📚 Long-Context Support

To process long inputs (e.g., full source files or compiler traces), use YaRN-based RoPE scaling :

Add this to config.json :

{
  "rope_scaling": {
    "type": "yarn",
    "factor": 4.0,
    "original_max_position_embeddings": 32768
  }
}

🔧 Use Cases

• WebAssembly code generation and debugging
• Rust/C++ code explanation and transformation
• Embedded/IoT code support
• Smart contract logic for blockchain environments using Wasm
• Code agents and assistants running in browsers

📬 Contact

📧 For questions, collaborations, or commercial licensing:

[email protected]