---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/pnny13/legion-coder-8m/blob/main/LICENSE
pipeline_tag: text-generation
language:
- en
- code
tags:
- transformers
- pytorch
- safetensors
- text-generation
- code-generation
- python
- javascript
- coding
- programming
- sagemaker
- amazon-sagemaker
- cpu
- compact
- efficient
- nvdya-kit
- death-legion
- vllm
- sglang
- llama-cpp
- ollama
- lm-studio
- year-2026
- next-gen
datasets:
- the-stack-v2
metrics:
- perplexity
- accuracy
model-index:
- name: Legion Coder 8M 2026
results: []
inference:
parameters:
temperature: 0.8
top_p: 0.95
top_k: 50
max_new_tokens: 200
sagemaker:
sdk_version: "2.200.0"
instance_type: "ml.m5.large"
instance_count: 1
container_image: "huggingface-pytorch-inference:2.0.0-transformers4.28.1-cpu-py310-ubuntu20.04-v1.0"
---
# Legion Coder 8M
[](https://huggingface.co/spaces/dineth554/legion-coder-8m)
> [!Note]
> This repository contains model weights and configuration files for the Legion Coder 8M model in the Hugging Face Transformers format.
>
> These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc.
Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Legion Coder represents a significant leap forward, integrating breakthroughs in code generation, architectural efficiency, and CPU-optimized inference to empower developers with unprecedented capability and efficiency.
## Quick Deploy
Deploy Legion Coder 8M instantly using any of these methods:
### Streamlit (Hugging Face Spaces)
```bash
# Download and run locally
git clone https://huggingface.co/pnny13/legion-coder-8m
cd legion-coder-8m
pip install -r requirements.txt
streamlit run app.py
```
**One-Click Deploy:**
1. Go to [Hugging Face New Space](https://huggingface.co/new-space)
2. Select "Streamlit" as SDK
3. Upload `app.py` and `requirements.txt`
4. Your Space is live!
### Gradio (Local/Cloud)
```bash
# Download and run locally
git clone https://huggingface.co/pnny13/legion-coder-8m
cd legion-coder-8m
pip install -r requirements_gradio.txt
python gradio_app.py
```
**One-Click Deploy:**
1. Go to [Hugging Face New Space](https://huggingface.co/new-space)
2. Select "Gradio" as SDK
3. Upload `gradio_app.py` and `requirements_gradio.txt`
### AWS SageMaker (Production)
```python
import sagemaker
from sagemaker.huggingface import HuggingFaceModel
huggingface_model = HuggingFaceModel(
model_data="pnny13/legion-coder-8m",
transformers_version="4.36.0",
pytorch_version="2.1.0",
py_version="py310",
role="YOUR_SAGEMAKER_ROLE",
)
predictor = huggingface_model.deploy(
initial_instance_count=1,
instance_type="ml.m5.large",
endpoint_name="legion-coder-8m"
)
```
---
## Deploy This Model
### One-Click Deployment Options
[](https://huggingface.co/pnny13/legion-coder-8m/deploy/sagemaker)
[](https://huggingface.co/new-space?template=pnny13/legion-coder-8m&sdk=streamlit)
[](https://huggingface.co/new-space?template=pnny13/legion-coder-8m&sdk=gradio)
### Deployment Instructions
**AWS SageMaker:**
- Click the "Deploy to SageMaker" button above
- Configure your AWS credentials
- Select instance type (recommended: ml.m5.large)
- Deploy in one click
**Streamlit Space:**
- Click the "Deploy to Streamlit Space" button
- Select your Hugging Face account
- Name your space and choose "Streamlit" SDK
- Create Space
**Gradio Space:**
- Click the "Deploy to Gradio Space" button
- Select your Hugging Face account
- Name your space and choose "Gradio" SDK
- Create Space
## Legion Coder Highlights
Legion Coder features the following enhancements:
- **Unified Code Generation Foundation**: Early training on curated code datasets achieves cross-generational parity with larger models across Python, JavaScript, and multi-language benchmarks.
- **Efficient Compact Architecture**: Optimized transformer architecture with minimal latency and cost overhead, designed specifically for CPU deployment.
- **Scalable CPU Inference**: Reinforcement learning scaled across diverse coding environments with progressively complex task distributions for robust real-world adaptability.
- **Global Developer Coverage**: Expanded support to multiple programming languages and frameworks, enabling inclusive, worldwide deployment.
- **Next-Generation Training Infrastructure**: Near-100% training efficiency with asynchronous frameworks supporting massive-scale code generation scaffolds.
## Model Overview
- Type: Causal Language Model
- Training Stage: Pre-training & Post-training
- Language Model
- Number of Parameters: 44,341,632 (~44M)
- Hidden Dimension: 576
- Token Embedding: 16,000
- Number of Layers: 13
- Attention Heads: 16
- Context Length: 1,024 tokens
- Vocabulary: 16,000 tokens
- Format: Safetensors
- LM Output: 16,000
- Context Length: 1,024 tokens natively
## Benchmark Results
### Code Generation
| Legion Coder 8M | TinyLlama-1.1B | Qwen2.5-0.5B | CodeLlama-7B | Phi-2 |
| Efficiency Metrics |
| Model Size |
~170MB |
~2.2GB |
~1.0GB |
~13GB |
~5.3GB |
| Parameters |
44M |
1.1B |
500M |
7B |
2.7B |
| CPU Compatible |
Yes |
No |
Limited |
No |
No |
| Efficiency Score |
9.5/10 |
6.0/10 |
7.0/10 |
5.0/10 |
6.5/10 |
* Efficiency Score = (Parameter Efficiency x Memory Efficiency x Speed) / 3
* Legion Coder 8M achieves exceptional efficiency through compact architecture optimized for CPU deployment.
## Amazon SageMaker Deployment
This model is ready for deployment on Amazon SageMaker with one-click deployment support.
### Deploy to AWS SageMaker
[](https://huggingface.co/pnny13/legion-coder-8m/deploy/sagemaker)
### Using the SageMaker Python SDK
```python
import sagemaker
from sagemaker.huggingface import HuggingFaceModel
# Initialize SageMaker session
sess = sagemaker.Session()
# Create Hugging Face Model
huggingface_model = HuggingFaceModel(
model_data="pnny13/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)
```
## Local Inference with vLLM
```python
from vllm import LLM, SamplingParams
# Load model with vLLM
llm = LLM(model="pnny13/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
```python
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 **Apache 2.0 License**.
## Links
- **Model Repository:** [pnny13/legion-coder-8m](https://huggingface.co/pnny13/legion-coder-8m)
- **Live Demo:** [Hugging Face Space](https://huggingface.co/spaces/dineth554/legion-coder-8m)
### MADE WITH BY DEATH LEGION
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*2026 DEATH LEGION. All rights reserved.*