Instructions to use ysn-rfd/gpt4all-falcon-Q2_K-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 ysn-rfd/gpt4all-falcon-Q2_K-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 ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_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 ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_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 ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K

Use Docker

docker model run hf.co/ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ysn-rfd/gpt4all-falcon-Q2_K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ysn-rfd/gpt4all-falcon-Q2_K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ysn-rfd/gpt4all-falcon-Q2_K-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K

Ollama
How to use ysn-rfd/gpt4all-falcon-Q2_K-GGUF with Ollama:
```
ollama run hf.co/ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K
```
Unsloth Desktop
Docker Model Runner
How to use ysn-rfd/gpt4all-falcon-Q2_K-GGUF with Docker Model Runner:
```
docker model run hf.co/ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K
```

Lemonade

How to use ysn-rfd/gpt4all-falcon-Q2_K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ysn-rfd/gpt4all-falcon-Q2_K-GGUF:Q2_K

Run and chat with the model

lemonade run user.gpt4all-falcon-Q2_K-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

gpt4all-falcon-Q2_K-GGUF / gpt4all-falcon-q2_k.gguf

ysn-rfd

Upload gpt4all-falcon-q2_k.gguf with huggingface_hub

8158766 verified about 2 years ago

Download

History Blame GGUF Editor new Delete

3.86 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: e0ca0ac2a3e7e4cb9cccf568a5765486277583ef6dca6d7e1b89109b7e0a9905
Size of remote file:: 3.86 GB
SHA256:: 04189a37bc9375c76ee2f6563e6dec39f84bbf1705490a252265324d1653ff22

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.