Instructions to use QuantFactory/Hercules-5.0-Qwen2-1.5B-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 QuantFactory/Hercules-5.0-Qwen2-1.5B-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 QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Hercules-5.0-Qwen2-1.5B-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 QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Hercules-5.0-Qwen2-1.5B-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 QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Hercules-5.0-Qwen2-1.5B-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 QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Hercules-5.0-Qwen2-1.5B-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 QuantFactory/Hercules-5.0-Qwen2-1.5B-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 QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hercules-5.0-Qwen2-1.5B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Hercules-5.0-Qwen2-1.5B-GGUF

This is quantized version of M4-ai/Hercules-5.0-Qwen2-1.5B created using llama.cpp

Model Description

We fine-tuned qwen2-1.5B on a high quality mix for general-purpose assistants. A DPO version of this will be released soon. We use the ChatML prompt format.

Model Details

This model has capabilities in math, coding, writing, and more. We fine-tuned it using a high quality mix for general-purpose assistants.

Developed by: M4-ai
Language(s) (NLP): English and maybe Chinese
License: apache-2.0
Finetuned from model: qwen2-1.5B

Uses

General purpose assistant, question answering, chain-of-thought, etc..

This language model made an impressive achievement, and correctly implemented a Multi Head Attention for use in a transformer neural network.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Data

Locutusque/hercules-v5.0

Evaluations

coming soon

Training Hyperparameters

Training regime: bf16 non-mixed precision

Technical Specifications

Hardware

We used 8 Kaggle TPUs, and we trained at a global batch size of 256 and sequence length of 1536.

Downloads last month: 289

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Hercules-5.0-Qwen2-1.5B-GGUF

Base model

M4-ai/Hercules-5.0-Qwen2-1.5B

Quantized

(3)

this model

QuantFactory
/

Hercules-5.0-Qwen2-1.5B-GGUF