Instructions to use hridya423/lexis-1.5b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use hridya423/lexis-1.5b-GGUF 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 hridya423/lexis-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf hridya423/lexis-1.5b-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf hridya423/lexis-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf hridya423/lexis-1.5b-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 hridya423/lexis-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf hridya423/lexis-1.5b-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 hridya423/lexis-1.5b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf hridya423/lexis-1.5b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/hridya423/lexis-1.5b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use hridya423/lexis-1.5b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hridya423/lexis-1.5b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hridya423/lexis-1.5b-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/hridya423/lexis-1.5b-GGUF:Q4_K_M
- Ollama
How to use hridya423/lexis-1.5b-GGUF with Ollama:
ollama run hf.co/hridya423/lexis-1.5b-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use hridya423/lexis-1.5b-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hridya423/lexis-1.5b-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "hridya423/lexis-1.5b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use hridya423/lexis-1.5b-GGUF with Docker Model Runner:
docker model run hf.co/hridya423/lexis-1.5b-GGUF:Q4_K_M
- Lemonade
How to use hridya423/lexis-1.5b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull hridya423/lexis-1.5b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.lexis-1.5b-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use hridya423/lexis-1.5b-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hridya423/lexis-1.5b-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default hridya423/lexis-1.5b-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use hridya423/lexis-1.5b-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf hridya423/lexis-1.5b-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "hridya423/lexis-1.5b-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
lexis-1.5b
Plain English in, one shell command out, for macOS, Linux and Windows. This is the default local model for Lexis, a terminal assistant that shows every command on an approval card before it runs.
It is a full fine-tune of Qwen2.5-Coder-1.5B-Instruct on 138,977 requests whose commands were each checked on their target OS. The file is Q4_K_M, 986 MB, and answers in about 250 ms on an Apple M4 Pro.
| macOS | Linux | Windows | All | |
|---|---|---|---|---|
| Final held-out set, first try | 265 / 337 | 284 / 332 | 208 / 250 | 757 / 919 (82.4%) |
For comparison, lexis-0.5b scores 714 / 919 at 398 MB and about 100 ms. METHODOLOGY.md covers the data, the checks, the evaluation, and the experiments that didn't help.
Use it through Lexis
curl -fsSL https://lexis.hridya.tech/install.sh | bash # macOS, Linux
irm https://lexis.hridya.tech/win.ps1 | iex # Windows
lx "biggest 10 files in my Downloads"
The installer downloads this model, sets up llama.cpp, and asks before running anything that changes your system.
Use it directly
The model expects exactly the prompt it was trained on: a fixed system prompt, then the request with a platform line appended.
llama-server -m lexis-1.5b-q4_k_m.gguf --jinja -c 4096 --port 8080
curl -s localhost:8080/v1/chat/completions -d '{
"temperature": 0,
"messages": [
{"role": "system", "content": "You are a bash command generator. Given a natural language request, output only the bash command that accomplishes it. No explanation, no markdown fences."},
{"role": "user", "content": "find all pdfs on my desktop\n(Platform: macOS (BSD), shell: zsh)"}
]}' | jq -r '.choices[0].message.content'
Platform lines used in training:
(Platform: macOS (BSD), shell: zsh)(Platform: Linux (GNU), shell: bash)(Platform: windows, shell: powershell), with this system prompt instead:You are a PowerShell command generator. Given a natural language request, output only the PowerShell command that accomplishes it. No explanation, no markdown fences.
To fix a failed command, use You fix broken bash commands. Given a failed command and its error output, output only the corrected command. No explanation. as the system prompt. The user message is the command after $ , then the error output on the following lines, with no platform line.
Limits
- One command per answer, no explanation. Multi-step tasks come back joined with
&∨. - About one first-try answer in six is wrong. Typical mistakes are a rarer tool it doesn't know, an invented flag, or a missed detail such as sort order or case sensitivity.
- It does what it's asked, including destructive things. It has no safety judgment of its own and will occasionally answer a harmless question with something drastic. Don't execute its output without a review step; Lexis provides one.
- Downloads last month
- 53
4-bit
Model tree for hridya423/lexis-1.5b-GGUF
Base model
Qwen/Qwen2.5-1.5B