Instructions to use afrideva/Hercules-Mini-1.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afrideva/Hercules-Mini-1.8B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="afrideva/Hercules-Mini-1.8B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("afrideva/Hercules-Mini-1.8B-GGUF", dtype="auto")

llama-cpp-python

How to use afrideva/Hercules-Mini-1.8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/Hercules-Mini-1.8B-GGUF",
	filename="hercules-mini-1.8b.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use afrideva/Hercules-Mini-1.8B-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/Hercules-Mini-1.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/Hercules-Mini-1.8B-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/Hercules-Mini-1.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/Hercules-Mini-1.8B-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/Hercules-Mini-1.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/Hercules-Mini-1.8B-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/Hercules-Mini-1.8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/Hercules-Mini-1.8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/Hercules-Mini-1.8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use afrideva/Hercules-Mini-1.8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afrideva/Hercules-Mini-1.8B-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": "afrideva/Hercules-Mini-1.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/afrideva/Hercules-Mini-1.8B-GGUF:Q4_K_M

SGLang

How to use afrideva/Hercules-Mini-1.8B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "afrideva/Hercules-Mini-1.8B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/Hercules-Mini-1.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "afrideva/Hercules-Mini-1.8B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afrideva/Hercules-Mini-1.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use afrideva/Hercules-Mini-1.8B-GGUF with Ollama:
```
ollama run hf.co/afrideva/Hercules-Mini-1.8B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use afrideva/Hercules-Mini-1.8B-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/Hercules-Mini-1.8B-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/Hercules-Mini-1.8B-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/Hercules-Mini-1.8B-GGUF to start chatting

Docker Model Runner
How to use afrideva/Hercules-Mini-1.8B-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/Hercules-Mini-1.8B-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/Hercules-Mini-1.8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/Hercules-Mini-1.8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hercules-Mini-1.8B-GGUF-Q4_K_M

List all available models

lemonade list

Hercules-Mini-1.8B-GGUF

Quantized GGUF model files for Hercules-Mini-1.8B from M4-ai

Original Model Card:

Hercules-Mini-1.8B

We fine-tuned Qwen1.5-1.8B on Locutusque's Hercules-v4.

Model Details

Model Description

This model has capabilities in math, coding, function calling, roleplay, and more. We fine-tuned it using 700,000 examples of Hercules-v4.

Developed by: M4-ai
Language(s) (NLP): English and maybe Chinese
License: tongyi-qianwen license
Finetuned from model: Qwen1.5-1.8B

Uses

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

Bias, Risks, and Limitations

The eos token was not setup properly, so to prevent infinite generation you'll need to implement a stopping criteria when the model generates the <|im_end|> token.

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.

Evaluation

Coming soon

Training Details

Training Data

https://huggingface.co/datasets/Locutusque/hercules-v4.0

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

Contributions

Thanks to @Tonic, @aloobun, @fhai50032, and @Locutusque for their contributions to this model.

Downloads last month: 82

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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