Instructions to use wasmdashai/Wasm-Coder-8B-Instruct-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wasmdashai/Wasm-Coder-8B-Instruct-V1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wasmdashai/Wasm-Coder-8B-Instruct-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wasmdashai/Wasm-Coder-8B-Instruct-V1")
model = AutoModelForCausalLM.from_pretrained("wasmdashai/Wasm-Coder-8B-Instruct-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wasmdashai/Wasm-Coder-8B-Instruct-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wasmdashai/Wasm-Coder-8B-Instruct-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wasmdashai/Wasm-Coder-8B-Instruct-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wasmdashai/Wasm-Coder-8B-Instruct-V1

SGLang

How to use wasmdashai/Wasm-Coder-8B-Instruct-V1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "wasmdashai/Wasm-Coder-8B-Instruct-V1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wasmdashai/Wasm-Coder-8B-Instruct-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "wasmdashai/Wasm-Coder-8B-Instruct-V1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wasmdashai/Wasm-Coder-8B-Instruct-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use wasmdashai/Wasm-Coder-8B-Instruct-V1 with Docker Model Runner:
```
docker model run hf.co/wasmdashai/Wasm-Coder-8B-Instruct-V1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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:

modelasg@gmail.com

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Safetensors

Model size

8B params

Tensor type

F32

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Quantizations

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