Instructions to use rahuldshetty/tinyllama-python-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rahuldshetty/tinyllama-python-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rahuldshetty/tinyllama-python-gguf",
	filename="tinyllama-python-unsloth.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use rahuldshetty/tinyllama-python-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rahuldshetty/tinyllama-python-gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf rahuldshetty/tinyllama-python-gguf:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf rahuldshetty/tinyllama-python-gguf:Q2_K
# Run inference directly in the terminal:
llama-cli -hf rahuldshetty/tinyllama-python-gguf:Q2_K

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 rahuldshetty/tinyllama-python-gguf:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf rahuldshetty/tinyllama-python-gguf:Q2_K

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 rahuldshetty/tinyllama-python-gguf:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rahuldshetty/tinyllama-python-gguf:Q2_K

Use Docker

docker model run hf.co/rahuldshetty/tinyllama-python-gguf:Q2_K

LM Studio
Jan

vLLM

How to use rahuldshetty/tinyllama-python-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rahuldshetty/tinyllama-python-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rahuldshetty/tinyllama-python-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rahuldshetty/tinyllama-python-gguf:Q2_K

Ollama
How to use rahuldshetty/tinyllama-python-gguf with Ollama:
```
ollama run hf.co/rahuldshetty/tinyllama-python-gguf:Q2_K
```

Unsloth Studio new

How to use rahuldshetty/tinyllama-python-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 rahuldshetty/tinyllama-python-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 rahuldshetty/tinyllama-python-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rahuldshetty/tinyllama-python-gguf to start chatting

Docker Model Runner
How to use rahuldshetty/tinyllama-python-gguf with Docker Model Runner:
```
docker model run hf.co/rahuldshetty/tinyllama-python-gguf:Q2_K
```

Lemonade

How to use rahuldshetty/tinyllama-python-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rahuldshetty/tinyllama-python-gguf:Q2_K

Run and chat with the model

lemonade run user.tinyllama-python-gguf-Q2_K

List all available models

lemonade list

rahuldshetty/tinyllama-python-gguf

Quantized GGUF model files for tinyllama-python.

Base model: unsloth/tinyllama-bnb-4bit
Dataset: iamtarun/python_code_instructions_18k_alpaca
Training Script: unslothai: Alpaca + TinyLlama + RoPE Scaling full example.ipynb

Name	Quant method	Size
tinyllama-python-unsloth.Q2_K.gguf	fp16	432 MB

Prompt Format

### Instruction:
{instruction}

### Response:

Example

### Instruction:
Write a function to find cube of a number.

### Response:

Downloads last month: 34

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

rahuldshetty
/

tinyllama-python-gguf

rahuldshetty/tinyllama-python-gguf

Prompt Format

Example

Dataset used to train rahuldshetty/tinyllama-python-gguf