Instructions to use Rohanify/Indenta-13M-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Rohanify/Indenta-13M-Python with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Rohanify/Indenta-13M-Python", filename="indenta-9m.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Rohanify/Indenta-13M-Python with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Rohanify/Indenta-13M-Python # Run inference directly in the terminal: llama-cli -hf Rohanify/Indenta-13M-Python
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Rohanify/Indenta-13M-Python # Run inference directly in the terminal: llama-cli -hf Rohanify/Indenta-13M-Python
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Rohanify/Indenta-13M-Python # Run inference directly in the terminal: ./llama-cli -hf Rohanify/Indenta-13M-Python
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Rohanify/Indenta-13M-Python # Run inference directly in the terminal: ./build/bin/llama-cli -hf Rohanify/Indenta-13M-Python
Use Docker
docker model run hf.co/Rohanify/Indenta-13M-Python
- LM Studio
- Jan
- vLLM
How to use Rohanify/Indenta-13M-Python with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Rohanify/Indenta-13M-Python" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Rohanify/Indenta-13M-Python", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Rohanify/Indenta-13M-Python
- Ollama
How to use Rohanify/Indenta-13M-Python with Ollama:
ollama run hf.co/Rohanify/Indenta-13M-Python
- Unsloth Studio new
How to use Rohanify/Indenta-13M-Python with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Rohanify/Indenta-13M-Python to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Rohanify/Indenta-13M-Python to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Rohanify/Indenta-13M-Python to start chatting
- Docker Model Runner
How to use Rohanify/Indenta-13M-Python with Docker Model Runner:
docker model run hf.co/Rohanify/Indenta-13M-Python
- Lemonade
How to use Rohanify/Indenta-13M-Python with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Rohanify/Indenta-13M-Python
Run and chat with the model
lemonade run user.Indenta-13M-Python-{{QUANT_TAG}}List all available models
lemonade list
Update README.md
Browse files
README.md
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license: cc-by-nc-4.0
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---
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license: cc-by-nc-4.0
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datasets:
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- flytech/python-codes-25k
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language:
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- en
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- py
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tags:
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- gpt2
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- gguf
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- python
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- code-generation
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pipeline_tag: text-generation
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ollama:
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template: |
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### Instruction:
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{{ .Prompt }}
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### Response:
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params:
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temperature: 0.4
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top_p: 0.9
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repeat_penalty: 1.1
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stop:
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- "### Instruction:"
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---
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# 🚀 Indenta-13M-Python (GGUF)
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An optimized from-scratch model made with a custom tokenizer and gpt-2 architecture.
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This model is made for python code completions and basic python code generation.
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This model has ~13M parameters, making it ideal for almost any machine!
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---
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## 🛠️ How to Run Instantly via Ollama
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Because the system configuration is baked directly into this Hugging Face repository card, nobody needs to manually create a local `Modelfile`.
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You or anyone else can spin it up immediately by targeting the Hugging Face repository link. Run this command in your terminal:
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```bash
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ollama run hf.co/Rohanify/Indenta-9M-Python
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