Instructions to use afrideva/TinyLLama-v0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/TinyLLama-v0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/TinyLLama-v0-GGUF",
	filename="tinyllama-v0.fp16.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 afrideva/TinyLLama-v0-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-v0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyLLama-v0-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-v0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf afrideva/TinyLLama-v0-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-v0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/TinyLLama-v0-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-v0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/TinyLLama-v0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/TinyLLama-v0-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/TinyLLama-v0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/TinyLLama-v0-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-v0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/TinyLLama-v0-GGUF:Q4_K_M

Ollama
How to use afrideva/TinyLLama-v0-GGUF with Ollama:
```
ollama run hf.co/afrideva/TinyLLama-v0-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/TinyLLama-v0-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-v0-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-v0-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-v0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use afrideva/TinyLLama-v0-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/TinyLLama-v0-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/TinyLLama-v0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/TinyLLama-v0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyLLama-v0-GGUF-Q4_K_M

List all available models

lemonade list

Maykeye/TinyLLama-v0-GGUF

Quantized GGUF model files for TinyLLama-v0 from Maykeye

Name	Quant method	Size
tinyllama-v0.fp16.gguf	fp16	11.08 MB
tinyllama-v0.q2_k.gguf	q2_k	5.47 MB
tinyllama-v0.q3_k_m.gguf	q3_k_m	5.63 MB
tinyllama-v0.q4_k_m.gguf	q4_k_m	5.79 MB
tinyllama-v0.q5_k_m.gguf	q5_k_m	5.95 MB
tinyllama-v0.q6_k.gguf	q6_k	6.72 MB
tinyllama-v0.q8_0.gguf	q8_0	6.75 MB

Original Model Card:

This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture.

Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running the cells. Validation content is not used by the script so you put anythin in
Backup directory has a script do_backup that I used to copy weights from remote machine to local. Weight are generated too quickly, so by the time script copied weihgt N+1
This is extremely PoC version. Training truncates stories that are longer than context size and doesn't use any sliding window to train story not from the start
Training took approximately 9 hours (3 hours per epoch) on 40GB A100. ~30GB VRAM was used
I use tokenizer from open_llama_3b. However I had troubles with it locally(https://github.com/openlm-research/open_llama/issues/69). I had no troubles on the cloud machine with preninstalled libraries.
Demo script is demo.py
Validation script is provided: valid.py. use it like python valid.py path/to/TinyStoriesV2-GPT4-valid.txt [optional-model-id-or-path]: After training I decided that it's not necessary to beat validation into chunks
Also this version uses very stupid caching mechinsm to shuffle stories for training: it keeps cache of N recently loaded chunks so if random shuffle asks for a story, it may use cache or load chunk. Training dataset is too small, so in next versions I will get rid of it.

from transformers import AutoModelForCausalLM, AutoTokenizer

Downloads last month: 122

GGUF

Model size

4.62M 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-v0-GGUF

Base model

Maykeye/TinyLLama-v0

Quantized

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