Instructions to use antiven0m/finch-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use antiven0m/finch-gguf with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("antiven0m/finch-gguf", dtype="auto") - llama-cpp-python
How to use antiven0m/finch-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="antiven0m/finch-gguf", filename="finch-Q3_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 antiven0m/finch-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf antiven0m/finch-gguf:Q6_K # Run inference directly in the terminal: llama-cli -hf antiven0m/finch-gguf:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf antiven0m/finch-gguf:Q6_K # Run inference directly in the terminal: llama-cli -hf antiven0m/finch-gguf:Q6_K
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 antiven0m/finch-gguf:Q6_K # Run inference directly in the terminal: ./llama-cli -hf antiven0m/finch-gguf:Q6_K
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 antiven0m/finch-gguf:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf antiven0m/finch-gguf:Q6_K
Use Docker
docker model run hf.co/antiven0m/finch-gguf:Q6_K
- LM Studio
- Jan
- Ollama
How to use antiven0m/finch-gguf with Ollama:
ollama run hf.co/antiven0m/finch-gguf:Q6_K
- Unsloth Studio new
How to use antiven0m/finch-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 antiven0m/finch-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 antiven0m/finch-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for antiven0m/finch-gguf to start chatting
- Docker Model Runner
How to use antiven0m/finch-gguf with Docker Model Runner:
docker model run hf.co/antiven0m/finch-gguf:Q6_K
- Lemonade
How to use antiven0m/finch-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull antiven0m/finch-gguf:Q6_K
Run and chat with the model
lemonade run user.finch-gguf-Q6_K
List all available models
lemonade list
Finch 7b Merge
A SLERP merge of my two current fav 7B models
macadeliccc/WestLake-7B-v2-laser-truthy-dpo & SanjiWatsuki/Kunoichi-DPO-v2-7B
A set of GGUF quants of Finch
Settings
I reccomend using the ChatML format. As for samplers, I reccomend the following:
Temperature: 1.2
Min P: 0.2
Smoothing Factor: 0.2
Mergekit Config
base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
dtype: float16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.0, 0.5, 0.3, 0.7, 1.0]
- filter: mlp
value: [1.0, 0.5, 0.7, 0.3, 0.0]
- value: 0.5
slices:
- sources:
- layer_range: [0, 32]
model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
- layer_range: [0, 32]
model: SanjiWatsuki/Kunoichi-DPO-v2-7B
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