---
library_name: transformers
pipeline_tag: text-generation
---


# Wasm-Coder-8B-Instruct-V1

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

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## 🚀 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.

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## 🧠 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

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## 💻 Quickstart

Install dependencies:

```bash
pip install --upgrade transformers
```

Example code to load and run the model:

```python
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)
```

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## 📚 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`:

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

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## 🔧 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

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## 📬 Contact

📧 For questions, collaborations, or commercial licensing:

**[modelasg@gmail.com](mailto:modelasg@gmail.com)**

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