Instructions to use louhless/Ycoder-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

Ycoder-small

File size: 1,307 Bytes

---
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>