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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use cortexso/tinyllama 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 cortexso/tinyllama:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf cortexso/tinyllama:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/cortexso/tinyllama:Q4_K_M

LM Studio
Jan

vLLM

How to use cortexso/tinyllama with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cortexso/tinyllama"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cortexso/tinyllama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cortexso/tinyllama:Q4_K_M

Ollama
How to use cortexso/tinyllama with Ollama:
```
ollama run hf.co/cortexso/tinyllama:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use cortexso/tinyllama with Docker Model Runner:
```
docker model run hf.co/cortexso/tinyllama:Q4_K_M
```

Lemonade

How to use cortexso/tinyllama with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cortexso/tinyllama:Q4_K_M

Run and chat with the model

lemonade run user.tinyllama-Q4_K_M

List all available models

lemonade list

Atomic Chat

tinyllama / README.md

Minh141120

Update README.md

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	---
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- cortex.cpp
	---

	## Overview

	The [TinyLlama](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) project aims to pretrain a 1.1B Llama model on 3 trillion tokens. This is the chat model finetuned on a diverse range of synthetic dialogues generated by ChatGPT.

	## Variants

	\| No \| Variant \| Cortex CLI command \|
	\| --- \| --- \| --- \|
	\| 1 \| [TinyLLama-1b](https://huggingface.co/cortexso/tinyllama/tree/1b) \| `cortex run tinyllama:1b` \|


	## Use it with Jan (UI)

	1. Install Jan using [Quickstart](https://jan.ai/docs/quickstart)
	2. Use in Jan model Hub:
	```bash
	cortexhub/tinyllama
	```

	## Use it with Cortex (CLI)

	1. Install Cortex using [Quickstart](https://cortex.jan.ai/docs/quickstart)
	2. Run the model with command:
	```bash
	cortex run tinyllama
	```

	## Credits

	- Author: Microsoft
	- Converter: [Homebrew](https://www.homebrew.ltd/)
	- Original License: [License](https://choosealicense.com/licenses/apache-2.0/)
	- Papers: [Tinyllama Paper](https://arxiv.org/abs/2401.02385)