Instructions to use afrideva/TinyKAI-3B-beta-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/TinyKAI-3B-beta-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/TinyKAI-3B-beta-GGUF",
	filename="tinykai-3b-beta.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 afrideva/TinyKAI-3B-beta-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/TinyKAI-3B-beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/TinyKAI-3B-beta-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/TinyKAI-3B-beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/TinyKAI-3B-beta-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/TinyKAI-3B-beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/TinyKAI-3B-beta-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/TinyKAI-3B-beta-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/TinyKAI-3B-beta-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/TinyKAI-3B-beta-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/TinyKAI-3B-beta-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/TinyKAI-3B-beta-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/TinyKAI-3B-beta-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afrideva/TinyKAI-3B-beta-GGUF:Q4_K_M

Ollama
How to use afrideva/TinyKAI-3B-beta-GGUF with Ollama:
```
ollama run hf.co/afrideva/TinyKAI-3B-beta-GGUF:Q4_K_M
```

Unsloth Studio new

How to use afrideva/TinyKAI-3B-beta-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/TinyKAI-3B-beta-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/TinyKAI-3B-beta-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/TinyKAI-3B-beta-GGUF to start chatting

Docker Model Runner
How to use afrideva/TinyKAI-3B-beta-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/TinyKAI-3B-beta-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/TinyKAI-3B-beta-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/TinyKAI-3B-beta-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.TinyKAI-3B-beta-GGUF-Q4_K_M

List all available models

lemonade list

Keynote-Technology/TinyKAI-3B-beta-GGUF

Quantized GGUF model files for TinyKAI-3B-beta from Keynote-Technology

Name	Quant method	Size
tinykai-3b-beta.q2_k.gguf	q2_k	2.15 GB
tinykai-3b-beta.q3_k_m.gguf	q3_k_m	2.27 GB
tinykai-3b-beta.q4_k_m.gguf	q4_k_m	2.58 GB
tinykai-3b-beta.q5_k_m.gguf	q5_k_m	2.76 GB
tinykai-3b-beta.q6_k.gguf	q6_k	3.64 GB
tinykai-3b-beta.q8_0.gguf	q8_0	3.64 GB

Original Model Card:

TinyKAI 3B is a fine-tuned LLM (Large Language Model) based off of OpenLlama 3B v2. The TinyKAI models are a series of lightweight LLMs under 5 Billion parameters, usually used for research.

Direct Use

TinyKAI 3B is optimal for research on large language models, specifically the influence of web data on the properties of large language models (fairness, safety, limitations, capabilities, etc.).

Training

This model was trained on a mixture of the Falcon refined-web dataset, the StarCoder dataset and the wikipedia, arxiv, book and stackexchange part of the RedPajama dataset.

Banned Use

Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful or insulting to anyone or any certain group.

Limitations

TinyKAI 3B is trained on English data only, and will not generate appropriately reasonable content in other languages. Being trained on a representative of the web, it will carry the stereotypes and biases commonly encountered online.

Recommendations

We recommend users of TinyKAI 3B to consider finetuning it for personal use, and for precautions to be taken for any commercial use.

WARNING!

This model runs on an older version of transformers, v4.10.0, and therefore may be unstable.

Downloads last month: 77

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for afrideva/TinyKAI-3B-beta-GGUF

Base model

Keynote-Technology/TinyKAI-3B-v0.1

Quantized

(1)

this model