Instructions to use sixf0ur/tiny-lm-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sixf0ur/tiny-lm-chat with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sixf0ur/tiny-lm-chat",
	filename="tiny-lm-chat-f16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sixf0ur/tiny-lm-chat with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sixf0ur/tiny-lm-chat:F16
# Run inference directly in the terminal:
llama-cli -hf sixf0ur/tiny-lm-chat:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sixf0ur/tiny-lm-chat:F16
# Run inference directly in the terminal:
llama-cli -hf sixf0ur/tiny-lm-chat: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 sixf0ur/tiny-lm-chat:F16
# Run inference directly in the terminal:
./llama-cli -hf sixf0ur/tiny-lm-chat: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 sixf0ur/tiny-lm-chat:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sixf0ur/tiny-lm-chat:F16

Use Docker

docker model run hf.co/sixf0ur/tiny-lm-chat:F16

LM Studio
Jan
Ollama
How to use sixf0ur/tiny-lm-chat with Ollama:
```
ollama run hf.co/sixf0ur/tiny-lm-chat:F16
```

Unsloth Studio

How to use sixf0ur/tiny-lm-chat 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 sixf0ur/tiny-lm-chat 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 sixf0ur/tiny-lm-chat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sixf0ur/tiny-lm-chat to start chatting

Atomic Chat new
Docker Model Runner
How to use sixf0ur/tiny-lm-chat with Docker Model Runner:
```
docker model run hf.co/sixf0ur/tiny-lm-chat:F16
```

Lemonade

How to use sixf0ur/tiny-lm-chat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sixf0ur/tiny-lm-chat:F16

Run and chat with the model

lemonade run user.tiny-lm-chat-F16

List all available models

lemonade list

sixf0ur commited on 7 days ago

Commit

170e91d

verified ·

1 Parent(s): e1470f4

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -14,7 +14,7 @@ tags:
 # tiny-lm-chat
 tiny-lm-chat is an ultra-lightweight, 14-million-parameter Small Language Model based on the Gemma architecture.
-It has been trained from scratch and fine-tuned for conversational efficiency within a compact 192-token context window.
 It was designed to do singleturn chats in very simple language (input / output).
 ## Evaluation Metrics

 # tiny-lm-chat
 tiny-lm-chat is an ultra-lightweight, 14-million-parameter Small Language Model based on the Gemma architecture.
+It has been trained from scratch for conversational efficiency within a compact 192-token context window.
 It was designed to do singleturn chats in very simple language (input / output).
 ## Evaluation Metrics