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
```

dineth554 commited on Mar 8

Commit

2ddf9d9

verified ·

1 Parent(s): 40578d9

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config.json +64 -0

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	@@ -0,0 +1,64 @@

+{
+  "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"
+  }
+}