Legion Coder 8M 2026

A 44M Parameter Transformer for Code Generation - 2026 Edition

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Libraries and Frameworks

Local Apps and Inference Engines

Notebooks and Cloud

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

Attribute	Value
Parameters	44,341,632 (~44M)
Model Size	~170MB
Architecture	GPT-style Transformer
Hidden Size	576
Layers	13
Attention Heads	16
Context Length	1,024 tokens
Vocabulary	16,000 tokens
Format	Safetensors
Edition	2026

Model Comparison 2026

Model	Parameters	Size	Efficiency Score	Best For
Legion Coder 8M	44M	~170MB	9.5/10	Code generation, CPU inference
TinyLlama-1.1B	1.1B	~2.2GB	6.0/10	General text, GPU required
Qwen2.5-0.5B	500M	~1.0GB	7.0/10	Multilingual, GPU recommended
CodeLlama-7B	7B	~13GB	5.0/10	Production code, GPU required
Phi-2	2.7B	~5.3GB	6.5/10	Reasoning, GPU required

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.

dineth554
/

legion-coder-8m