Instructions to use louhless/Ycoder-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use louhless/Ycoder-small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="louhless/Ycoder-small") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("louhless/Ycoder-small", dtype="auto") - llama-cpp-python
How to use louhless/Ycoder-small with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="louhless/Ycoder-small", filename="Ycoder-small-f16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use louhless/Ycoder-small with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf louhless/Ycoder-small:F16 # Run inference directly in the terminal: llama-cli -hf louhless/Ycoder-small:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf louhless/Ycoder-small:F16 # Run inference directly in the terminal: llama-cli -hf louhless/Ycoder-small:F16
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 louhless/Ycoder-small:F16 # Run inference directly in the terminal: ./llama-cli -hf louhless/Ycoder-small:F16
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 louhless/Ycoder-small:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf louhless/Ycoder-small:F16
Use Docker
docker model run hf.co/louhless/Ycoder-small:F16
- LM Studio
- Jan
- vLLM
How to use louhless/Ycoder-small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "louhless/Ycoder-small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "louhless/Ycoder-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/louhless/Ycoder-small:F16
- SGLang
How to use louhless/Ycoder-small 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 "louhless/Ycoder-small" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "louhless/Ycoder-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "louhless/Ycoder-small" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "louhless/Ycoder-small", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use louhless/Ycoder-small with Ollama:
ollama run hf.co/louhless/Ycoder-small:F16
- Unsloth Studio new
How to use louhless/Ycoder-small 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 louhless/Ycoder-small 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 louhless/Ycoder-small to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for louhless/Ycoder-small to start chatting
- Docker Model Runner
How to use louhless/Ycoder-small with Docker Model Runner:
docker model run hf.co/louhless/Ycoder-small:F16
- Lemonade
How to use louhless/Ycoder-small with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull louhless/Ycoder-small:F16
Run and chat with the model
lemonade run user.Ycoder-small-F16
List all available models
lemonade list
Ycoder-small
Ycoder-small is a tiny experimental code-focused language model created by louhless and fine-tuned for short programming prompts, lightweight problem solving, simple chat, and optional thinking-style output.
Join Discord: https://discord.gg/Dq4MWuJm
Model Details
- Model name:
Ycoder-small - Creator:
louhless - Base model:
HuggingFaceTB/SmolLM2-135M-Instruct - Architecture: Llama-style causal language model
- Context length: 8192
- Language: English, with small German greeting support
- Export: GGUF available
- Status: experimental
Focus
The model is mainly tuned for:
- Python
- GLSL
- JavaScript
- SQL
- Bash
- simple math
- short normal assistant replies
LM Studio Thinking Toggle
The GGUF metadata includes a chat template with an enable_thinking variable.
When enable_thinking is enabled, the model is prompted to use:
<think>
short reasoning summary
</think>
<answer>
final answer
</answer>
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16-bit
Model tree for louhless/Ycoder-small
Base model
HuggingFaceTB/SmolLM2-135M