Instructions to use dineth554/legion-coder-8m-10k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dineth554/legion-coder-8m-10k with Transformers:

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

pipe = pipeline("text-generation", model="dineth554/legion-coder-8m-10k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dineth554/legion-coder-8m-10k")
model = AutoModelForCausalLM.from_pretrained("dineth554/legion-coder-8m-10k")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dineth554/legion-coder-8m-10k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dineth554/legion-coder-8m-10k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dineth554/legion-coder-8m-10k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dineth554/legion-coder-8m-10k

SGLang

How to use dineth554/legion-coder-8m-10k 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 "dineth554/legion-coder-8m-10k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dineth554/legion-coder-8m-10k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "dineth554/legion-coder-8m-10k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dineth554/legion-coder-8m-10k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use dineth554/legion-coder-8m-10k with Docker Model Runner:
```
docker model run hf.co/dineth554/legion-coder-8m-10k
```

legion-coder-8m-10k / config.json

dineth554

Upload config.json with huggingface_hub

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	{
	"model_type": "gpt2",
	"architectures": [
	"GPT2LMHeadModel"
	],
	"vocab_size": 16000,
	"d_model": 576,
	"num_layers": 13,
	"num_heads": 16,
	"d_ff": 1280,
	"max_seq_len": 1024,
	"dropout": 0.1,
	"pad_token_id": 0,
	"eos_token_id": 1,
	"unk_token_id": 2,
	"torch_dtype": "float32",
	"transformers_version": "4.36.0",
	"task": "text-generation",
	"pipeline_tag": "text-generation",
	"library_name": "transformers",
	"license": "apache-2.0",
	"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"],
	"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"
	}
	}