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

Libraries

How to use afrideva/tinyllama-python-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/tinyllama-python-GGUF",
	filename="tinyllama-python.fp16.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 afrideva/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 afrideva/tinyllama-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/tinyllama-python-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/tinyllama-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/tinyllama-python-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 afrideva/tinyllama-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/tinyllama-python-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 afrideva/tinyllama-python-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/tinyllama-python-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/tinyllama-python-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/tinyllama-python-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/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": "afrideva/tinyllama-python-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/tinyllama-python-GGUF:Q4_K_M

Ollama
How to use afrideva/tinyllama-python-GGUF with Ollama:
```
ollama run hf.co/afrideva/tinyllama-python-GGUF:Q4_K_M
```

Unsloth Studio new

How to use afrideva/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 afrideva/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 afrideva/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 afrideva/tinyllama-python-GGUF to start chatting

Docker Model Runner
How to use afrideva/tinyllama-python-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/tinyllama-python-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/tinyllama-python-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/tinyllama-python-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.tinyllama-python-GGUF-Q4_K_M

List all available models

lemonade list

rahuldshetty/tinyllama-python-GGUF

Quantized GGUF model files for tinyllama-python from rahuldshetty

Name	Quant method	Size
tinyllama-python.fp16.gguf	fp16	2.20 GB
tinyllama-python.q2_k.gguf	q2_k	432.13 MB
tinyllama-python.q3_k_m.gguf	q3_k_m	548.40 MB
tinyllama-python.q4_k_m.gguf	q4_k_m	667.81 MB
tinyllama-python.q5_k_m.gguf	q5_k_m	782.04 MB
tinyllama-python.q6_k.gguf	q6_k	903.41 MB
tinyllama-python.q8_0.gguf	q8_0	1.17 GB

Original Model Card:

rahuldshetty/tinyllama-python-gguf

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

Prompt Format

### Instruction:
{instruction}

### Response:

Example

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

### Response:

Downloads last month: 174

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for afrideva/tinyllama-python-GGUF

Base model

rahuldshetty/tinyllama-python

Quantized

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this model

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tinyllama-python-GGUF