Instructions to use Mist26/react-ts-architect-0.5b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mist26/react-ts-architect-0.5b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Mist26/react-ts-architect-0.5b-gguf",
	filename="qwen2.5-coder-0.5b-instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Mist26/react-ts-architect-0.5b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mist26/react-ts-architect-0.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mist26/react-ts-architect-0.5b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Mist26/react-ts-architect-0.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Mist26/react-ts-architect-0.5b-gguf:Q4_K_M

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 Mist26/react-ts-architect-0.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Mist26/react-ts-architect-0.5b-gguf:Q4_K_M

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 Mist26/react-ts-architect-0.5b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mist26/react-ts-architect-0.5b-gguf:Q4_K_M

Use Docker

docker model run hf.co/Mist26/react-ts-architect-0.5b-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use Mist26/react-ts-architect-0.5b-gguf with Ollama:
```
ollama run hf.co/Mist26/react-ts-architect-0.5b-gguf:Q4_K_M
```

Unsloth Studio new

How to use Mist26/react-ts-architect-0.5b-gguf 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 Mist26/react-ts-architect-0.5b-gguf 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 Mist26/react-ts-architect-0.5b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Mist26/react-ts-architect-0.5b-gguf to start chatting

Pi new

How to use Mist26/react-ts-architect-0.5b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Mist26/react-ts-architect-0.5b-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "react-ts-architect-0.5b-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Mist26/react-ts-architect-0.5b-gguf with Docker Model Runner:
```
docker model run hf.co/Mist26/react-ts-architect-0.5b-gguf:Q4_K_M
```

Lemonade

How to use Mist26/react-ts-architect-0.5b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mist26/react-ts-architect-0.5b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.react-ts-architect-0.5b-gguf-Q4_K_M

List all available models

lemonade list

react-ts-architect-0.5b-gguf

A fine-tuned, Q4_K_M-quantised version of unsloth/Qwen2.5-Coder-0.5B-Instruct trained to act as a React + TypeScript Architect capable of producing full, multi-file component trees with correct typings and CSS.

Intended Use

Platform	Runtime
Android (Termux)	llama.cpp
Desktop	llama.cpp / Ollama / LM Studio

Prompt Format

### System: You are a React TS Architect. ...
### Instruction: <task>
### Input: <optional context>
### Response:

Training Details

Parameter	Value
Base model	`unsloth/Qwen2.5-Coder-0.5B-Instruct`
LoRA rank	32
Max sequence length	2048
Optimiser	AdamW 8-bit
Steps	150
Quantisation	Q4_K_M (GGUF)
Framework	Unsloth + TRL