Instructions to use chanwit/stories110M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use chanwit/stories110M 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 chanwit/stories110M:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf chanwit/stories110M:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf chanwit/stories110M:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf chanwit/stories110M:Q4_K_S

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 chanwit/stories110M:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf chanwit/stories110M:Q4_K_S

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 chanwit/stories110M:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chanwit/stories110M:Q4_K_S

Use Docker

docker model run hf.co/chanwit/stories110M:Q4_K_S

LM Studio
Jan
Ollama
How to use chanwit/stories110M with Ollama:
```
ollama run hf.co/chanwit/stories110M:Q4_K_S
```

Unsloth Studio

How to use chanwit/stories110M 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 chanwit/stories110M 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 chanwit/stories110M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for chanwit/stories110M to start chatting

Docker Model Runner
How to use chanwit/stories110M with Docker Model Runner:
```
docker model run hf.co/chanwit/stories110M:Q4_K_S
```

Lemonade

How to use chanwit/stories110M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull chanwit/stories110M:Q4_K_S

Run and chat with the model

lemonade run user.stories110M-Q4_K_S

List all available models

lemonade list

Atomic Chat

stories110M / ggml-model-Q4_0.gguf

chanwit

Upload 15 files

ed57388 over 2 years ago

Download

History Blame GGUF Editor new Delete

82.6 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 81ba6f0e8614cf29fedbd85d606d0ace6611abfbdec60532f878b3384417e76c
Size of remote file:: 82.6 MB
SHA256:: b440c8fbe99b3e3fa85617cde7b0e1b0cb575faf874794321e717d1dc5805b51

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.