wasmdashai
/

Wasm-Coder-8B-Instruct-V1

@@ -3,197 +3,109 @@ library_name: transformers
 pipeline_tag: text-generation
 ---
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 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), optimized for WebAssembly (Wasm)-oriented programming tasks, 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:
+```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)
+```
+---
+## 📚 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
+  }
+}
+```
+---
+## 🔧 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]](mailto:[email protected])**
+---