Text Generation
Transformers
GGUF
English
causal-lm
fine-tuned
code
python
glsl
javascript
sql
bash
reasoning
vision
experimental
conversational
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use louhless/Ycoder-small with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
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
- Atomic Chat new
- 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
| language: | |
| - en | |
| tags: | |
| - text-generation | |
| - causal-lm | |
| - fine-tuned | |
| - gguf | |
| - code | |
| - python | |
| - glsl | |
| - javascript | |
| - sql | |
| - bash | |
| - reasoning | |
| - vision | |
| - experimental | |
| license: apache-2.0 | |
| base_model: HuggingFaceTB/SmolLM2-135M-Instruct | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| model_creator: louhless | |
| # 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: | |
| ```text | |
| <think> | |
| short reasoning summary | |
| </think> | |
| <answer> | |
| final answer | |
| </answer> |