Text Generation
GGUF
English
Japanese
llama.cpp
code-generation
javascript
typescript
react
nodejs
slm
conversational
Instructions to use ai-lqm/ai-localqmod-js-slm 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 ai-lqm/ai-localqmod-js-slm 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 ai-lqm/ai-localqmod-js-slm:Q4_K_M # Run inference directly in the terminal: llama cli -hf ai-lqm/ai-localqmod-js-slm:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ai-lqm/ai-localqmod-js-slm:Q4_K_M # Run inference directly in the terminal: llama cli -hf ai-lqm/ai-localqmod-js-slm: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 ai-lqm/ai-localqmod-js-slm:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ai-lqm/ai-localqmod-js-slm: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 ai-lqm/ai-localqmod-js-slm:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ai-lqm/ai-localqmod-js-slm:Q4_K_M
Use Docker
docker model run hf.co/ai-lqm/ai-localqmod-js-slm:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ai-lqm/ai-localqmod-js-slm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ai-lqm/ai-localqmod-js-slm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ai-lqm/ai-localqmod-js-slm", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ai-lqm/ai-localqmod-js-slm:Q4_K_M
- Ollama
How to use ai-lqm/ai-localqmod-js-slm with Ollama:
ollama run hf.co/ai-lqm/ai-localqmod-js-slm:Q4_K_M
- Unsloth Studio
How to use ai-lqm/ai-localqmod-js-slm 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 ai-lqm/ai-localqmod-js-slm 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 ai-lqm/ai-localqmod-js-slm to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ai-lqm/ai-localqmod-js-slm to start chatting
- Atomic Chat new
- Docker Model Runner
How to use ai-lqm/ai-localqmod-js-slm with Docker Model Runner:
docker model run hf.co/ai-lqm/ai-localqmod-js-slm:Q4_K_M
- Lemonade
How to use ai-lqm/ai-localqmod-js-slm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ai-lqm/ai-localqmod-js-slm:Q4_K_M
Run and chat with the model
lemonade run user.ai-localqmod-js-slm-Q4_K_M
List all available models
lemonade list
File size: 2,984 Bytes
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license: mit
language:
- en
- ja
tags:
- gguf
- llama.cpp
- code-generation
- javascript
- typescript
- react
- nodejs
- slm
pipeline_tag: text-generation
---
# AI LocalQmod — JS-family Code SLMs (GGUF)
**Ultra-small, single-purpose code-generation models — as light as ~17 MB.**
This repository hosts a family of small language models (SLMs) trained from scratch
specifically for JavaScript-ecosystem code generation. They are built and distributed by
**AI LocalQmod** for use with [AI-App Builder](https://ai-localqmod.com), a local-first
AI app generation tool, but the GGUF files are plain [llama.cpp](https://github.com/ggerganov/llama.cpp)
models and can be loaded with any GGUF-compatible runtime (llama.cpp, llama-cpp-python,
Ollama with a Modelfile, LM Studio, etc.).
Unlike general-purpose LLMs, each model here is trained on a narrow, single-purpose corpus
(natural-language instruction → JS-family code) with a from-scratch tokenizer tuned for
that language's syntax and vocabulary. This trades general reasoning ability for
extremely small size and fast local inference, making them a good fit for lightweight,
single-purpose code-completion helpers rather than full coding assistants.
## Models
| File | Target | Quantization | Size |
|---|---|---|---|
| `js-slm-Q4_K_M-chat.gguf` | JavaScript | Q4_K_M | 17.7 MB |
| `jsts-slm-Q4_K_M-chat.gguf` | JavaScript + TypeScript | Q4_K_M | 17.5 MB |
| `node-slm-Q4_K_M-chat.gguf` | Node.js (fs/path/process/http/events) | Q4_K_M | 20.0 MB |
| `react-slm-Q4_K_M-chat.gguf` | React components | Q4_K_M | 16.6 MB |
| `js-loops-slm-Q4_K_M-chat.gguf` | JavaScript (for-loops + arrays: sum/max/count/filter/map/average) | Q4_K_M | 20.4 MB |
Sizes measured with `stat -f%z` (exact byte count), not rounded `ls -lh` output.
## Intended use
- Local, single-purpose code generation for the target language/framework shown above
- Multi-agent "orchestration" pipelines where a larger LLM plans and one of these SLMs
generates individual small functions/components
- Environments where downloading a multi-GB model is impractical (offline demos, low-disk
devices, quick experiments)
## Not intended for
- General-purpose chat or reasoning
- Large, multi-file refactors or architecture design
- Languages/frameworks outside each model's specific target (see table above)
## Usage (llama.cpp)
```bash
llama-cli -m js-slm-Q4_K_M-chat.gguf -p "Write a function that returns the sum of an array" -n 128
```
## Usage (llama-cpp-python)
```python
from llama_cpp import Llama
llm = Llama(model_path="js-slm-Q4_K_M-chat.gguf")
result = llm.create_chat_completion(
messages=[{"role": "user", "content": "Write a function that returns the sum of an array"}]
)
print(result["choices"][0]["message"]["content"])
```
## License
MIT. Free to use, modify, and redistribute.
## About AI LocalQmod
AI LocalQmod builds local-first, privacy-respecting AI tools. Learn more at
[ai-localqmod.com](https://ai-localqmod.com).
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