legion-coder-8m

Legion Coder 8M 2026

A 44M Parameter Transformer for Code Generation - 2026 Edition

Quick Links

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Libraries and Frameworks

Local Apps and Inference Engines

Notebooks and Cloud

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About

Legion Coder 2026 is a compact yet powerful 44M parameter transformer model optimized for coding tasks. Built with precision by DEATH LEGION and powered by nvdya-kit, this model delivers high-quality code generation in a lightweight package.

2026 Edition Features:

• Enhanced performance optimizations
• Updated documentation and branding
• Professional icon-based UI
• Advanced CSS animations
• Performance comparison charts

Features

• Clean Code Generation - PEP 8 compliant Python and more
• Debug Assistance - Help identify and fix code issues
• Code Explanation - Understand complex programming concepts
• Multi-language Support - Python, JavaScript, and more
• Fast Inference - Optimized for CPU deployment
• SageMaker Ready - One-click AWS deployment
• Template Ready - Duplicate this space to create your own

Model Specifications 2026

Model Comparison 2026

Efficiency Score = (Parameter Efficiency x Memory Efficiency x Speed) / 3

Legion Coder 8M 2026 achieves exceptional efficiency through:

• 260x smaller than CodeLlama-7B
• 13x smaller than TinyLlama-1.1B
• 6x smaller than Qwen2.5-0.5B
• Runs entirely on CPU with 8GB RAM

Amazon SageMaker Deployment

This model is ready for deployment on Amazon SageMaker with one-click deployment support.

Deploy to AWS SageMaker

Using the SageMaker Python SDK

import sagemaker
from sagemaker.huggingface import HuggingFaceModel

# Initialize SageMaker session
sess = sagemaker.Session()

# Create Hugging Face Model
huggingface_model = HuggingFaceModel(
    model_data="dineth554/legion-coder-8m",
    transformers_version="4.36.0",
    pytorch_version="2.1.0",
    py_version="py310",
    role="arn:aws:iam::YOUR_ACCOUNT_ID:role/YOUR_SAGEMAKER_ROLE",
    sagemaker_session=sess,
)

# Deploy to SageMaker
predictor = huggingface_model.deploy(
    initial_instance_count=1,
    instance_type="ml.m5.large",
    endpoint_name="legion-coder-8m-endpoint"
)

# Test the endpoint
result = predictor.predict({
    "inputs": "Write a Python function to calculate fibonacci numbers:",
    "parameters": {
        "temperature": 0.8,
        "max_new_tokens": 200
    }
})

print(result)

SageMaker Inference Script

The sagemaker_inference.py file in this repository provides the inference handler for SageMaker deployment.

Local Inference with vLLM

from vllm import LLM, SamplingParams

# Load model with vLLM
llm = LLM(model="dineth554/legion-coder-8m")

# Set sampling parameters
sampling_params = SamplingParams(
    temperature=0.8,
    top_p=0.95,
    max_tokens=200
)

# Generate code
prompt = "Write a Python function to calculate fibonacci numbers:"
outputs = llm.generate(prompt, sampling_params)
print(outputs[0].outputs[0].text)

Local Inference with SGLang

import sglang as sgl

# Define prompt template
@sgl.function
def code_gen(s, prompt):
    s += sgl.system("You are a helpful coding assistant.")
    s += sgl.user(prompt)
    s += sgl.assistant(sgl.gen("code", max_tokens=200))

# Run inference
result = code_gen.run(
    prompt="Write a Python function to calculate fibonacci numbers:",
    temperature=0.8
)
print(result["code"])

Technical Details

Training Data

• Python code from The Stack v2 dataset
• GitHub code repositories (filtered for quality)
• Code-specific preprocessing for indentation and special tokens

Training Procedure

• Optimizer: AdamW
• Learning Rate: 5e-4 with cosine decay
• Batch Size: 4 with gradient accumulation
• Training Steps: 10,000
• Precision: float32 (CPU-optimized)

License

This model is released under the MIT License.

Links

• Model Repository: dineth554/legion-coder-8m
• Live Demo: Hugging Face Space

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MADE WITH BY DEATH LEGION

Powered by nvdya-kit

2026 DEATH LEGION. All rights reserved.

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