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+---
+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
+
+<img width="400px" src="https://img.shields.io/badge/LEGION-CODER-ff0040?style=for-the-badge">
+
+[![Legion Coder Chat](https://img.shields.io/badge/LEGION%20Coder%20Chat-ff0040)](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.
+
+## 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
+
+<div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1000px;margin:0 auto;padding:16px 0">
+<table style="width:100%;border-collapse:collapse;font-size:13px">
+<thead><tr>
+<th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #ff0040;color:#ff0040"></th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">Legion Coder 8M</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">TinyLlama-1.1B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">Qwen2.5-0.5B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">CodeLlama-7B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #ff0040;color:#ff0040;font-size: 14px;">Phi-2</th></tr></thead>
+<tbody>
+<tr><td colspan="6" style="padding:8px 12px;font-weight:600;color:#ff0040;border-bottom:1px solid rgba(255, 0, 64, 0.2);background:rgba(255, 0, 64, 0.1)">Efficiency Metrics</td></tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Model Size</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~170MB</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~2.2GB</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~1.0GB</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~13GB</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">~5.3GB</td>
+</tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Parameters</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">44M</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">1.1B</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">500M</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">7B</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">2.7B</td>
+</tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">CPU Compatible</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">Yes</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">No</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">Limited</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">No</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">No</td>
+</tr>
+<tr>
+<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Efficiency Score</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 0.15)">9.5/10</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">6.0/10</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">7.0/10</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">5.0/10</td>
+<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">6.5/10</td>
+</tr>
+</tbody>
+</table>
+<p style="margin-top:12px;font-size:11px;opacity:0.7">
+* Efficiency Score = (Parameter Efficiency x Memory Efficiency x Speed) / 3<br>
+* Legion Coder 8M achieves exceptional efficiency through compact architecture optimized for CPU deployment.
+</p>
+</div>
+
+## Amazon SageMaker Deployment
+
+This model is ready for deployment on Amazon SageMaker with one-click deployment support.
+
+### Deploy to AWS SageMaker
+
+[![Deploy to SageMaker](https://img.shields.io/badge/Deploy%20to-AWS%20SageMaker-FF9900?style=for-the-badge&logo=amazon-aws)](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)
+
+<div align="center">
+
+### MADE WITH BY DEATH LEGION
+
+**Powered by nvdya-kit**
+
+*2026 DEATH LEGION. All rights reserved.*
+
+</div>