Text Generation
Transformers
Safetensors
PyTorch
English
code
gpt2
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
text-generation-inference
Instructions to use dineth554/legion-coder-8m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dineth554/legion-coder-8m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dineth554/legion-coder-8m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dineth554/legion-coder-8m") model = AutoModelForCausalLM.from_pretrained("dineth554/legion-coder-8m") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use dineth554/legion-coder-8m 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" # 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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dineth554/legion-coder-8m
- SGLang
How to use dineth554/legion-coder-8m 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" \ --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", "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" \ --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", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dineth554/legion-coder-8m with Docker Model Runner:
docker model run hf.co/dineth554/legion-coder-8m
| n e | |
| < | | |
| | > | |
| |> </w> | |
| d e | |
| <| ne | |
| <|ne w | |
| <|new l | |
| <|newl i | |
| <|newli ne | |
| <|newline |></w> | |
| i n | |
| <| in | |
| <|in de | |
| <|inde n | |
| <|inden t | |
| <|indent |></w> | |
| s </w> | |
| s s</w> | |
| : </w> | |
| a ss</w> | |
| p ass</w> | |
| " " | |
| s e | |
| f </w> | |
| ) :</w> | |
| de f</w> | |
| c e | |
| se l | |
| sel f | |
| "" " | |
| r e | |
| , </w> | |
| re t | |
| ret u | |
| retu r | |
| retur n | |
| return </w> | |
| _ _ | |
| ( self | |
| x ce | |
| xce p | |
| xcep t | |
| r o | |
| r u | |
| P ro | |
| d a | |
| da t | |
| dat a | |
| x </w> | |
| ( x | |
| (x ,</w> | |
| y ):</w> | |
| """ Pro | |
| """Pro ce | |
| """Proce ss</w> | |
| data """ | |
| data""" </w> | |
| c l | |
| cl ass</w> | |
| __ in | |
| __in i | |
| __ini t | |
| __init __ | |
| __init__ (self | |
| __init__(self ,</w> | |
| self ):</w> | |
| ru n | |
| run (self | |
| run(self ):</w> | |
| N o | |
| No ne | |
| None </w> | |
| se :</w> | |
| t r | |
| tr y | |
| try :</w> | |
| e xcept | |
| except </w> | |
| E xcept | |
| Except i | |
| Excepti o | |
| Exceptio n | |
| Exception :</w> | |
| i f</w> | |
| T ru | |
| Tru e | |
| True :</w> | |
| e l | |
| el se:</w> | |
| a l | |
| c (x,</w> | |
| f o | |
| fo r | |
| for </w> | |
| in </w> | |
| r a | |
| ra n | |
| ran g | |
| rang e | |
| range ( | |
| range( 1 | |
| range(1 0 | |
| range(10 ):</w> | |
| a r | |
| c al | |
| cal c(x,</w> | |
| m t | |
| Pro c | |
| Proc :</w> | |
| p ar | |
| par se | |
| parse (x,</w> | |
| data </w> | |
| F mt | |
| Fmt :</w> | |
| p ro | |
| pro c(x,</w> | |
| P ar | |
| Par se:</w> | |
| f mt | |
| fmt (x,</w> | |
| V al | |
| Val :</w> | |
| re s</w> | |
| y </w> | |
| v al | |
| val (x,</w> | |