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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use afrideva/tinyllama-python-GGUF 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 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 serve -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

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

Atomic Chat new
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

tinyllama-python-GGUF / README.md

afrideva

Upload README.md with huggingface_hub

9b785a0 verified over 2 years ago

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	---
	base_model: rahuldshetty/tinyllama-python
	datasets:
	- iamtarun/python_code_instructions_18k_alpaca
	inference: false
	language:
	- en
	license: apache-2.0
	model_creator: rahuldshetty
	model_name: tinyllama-python
	pipeline_tag: text-generation
	quantized_by: afrideva
	tags:
	- code
	- gguf
	- ggml
	- quantized
	- q2_k
	- q3_k_m
	- q4_k_m
	- q5_k_m
	- q6_k
	- q8_0
	widget:
	- text: '### Instruction:

	Write a function to find square of a number.


	### Response:'
	- text: '### Instruction:

	Write a function to calculate factorial.


	### Response:'
	- text: '### Instruction:

	Write a function to check whether a number is prime.


	### Response:'
	---
	# rahuldshetty/tinyllama-python-GGUF

	Quantized GGUF model files for [tinyllama-python](https://huggingface.co/rahuldshetty/tinyllama-python) from [rahuldshetty](https://huggingface.co/rahuldshetty)


	\| Name \| Quant method \| Size \|
	\| ---- \| ---- \| ---- \|
	\| [tinyllama-python.fp16.gguf](https://huggingface.co/afrideva/tinyllama-python-GGUF/resolve/main/tinyllama-python.fp16.gguf) \| fp16 \| 2.20 GB \|
	\| [tinyllama-python.q2_k.gguf](https://huggingface.co/afrideva/tinyllama-python-GGUF/resolve/main/tinyllama-python.q2_k.gguf) \| q2_k \| 432.13 MB \|
	\| [tinyllama-python.q3_k_m.gguf](https://huggingface.co/afrideva/tinyllama-python-GGUF/resolve/main/tinyllama-python.q3_k_m.gguf) \| q3_k_m \| 548.40 MB \|
	\| [tinyllama-python.q4_k_m.gguf](https://huggingface.co/afrideva/tinyllama-python-GGUF/resolve/main/tinyllama-python.q4_k_m.gguf) \| q4_k_m \| 667.81 MB \|
	\| [tinyllama-python.q5_k_m.gguf](https://huggingface.co/afrideva/tinyllama-python-GGUF/resolve/main/tinyllama-python.q5_k_m.gguf) \| q5_k_m \| 782.04 MB \|
	\| [tinyllama-python.q6_k.gguf](https://huggingface.co/afrideva/tinyllama-python-GGUF/resolve/main/tinyllama-python.q6_k.gguf) \| q6_k \| 903.41 MB \|
	\| [tinyllama-python.q8_0.gguf](https://huggingface.co/afrideva/tinyllama-python-GGUF/resolve/main/tinyllama-python.q8_0.gguf) \| q8_0 \| 1.17 GB \|



	## Original Model Card:
	# rahuldshetty/tinyllama-python-gguf

	- Base model: [unsloth/tinyllama-bnb-4bit](https://huggingface.co/unsloth/tinyllama-bnb-4bit)
	- Dataset: [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca)
	- Training Script: [unslothai: Alpaca + TinyLlama + RoPE Scaling full example.ipynb](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)


	## Prompt Format

	```
	### Instruction:
	{instruction}

	### Response:
	```

	## Example

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

	### Response:
	```