Instructions to use netcat420/MFANNv0.22.1-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use netcat420/MFANNv0.22.1-Q8_0-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="netcat420/MFANNv0.22.1-Q8_0-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("netcat420/MFANNv0.22.1-Q8_0-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use netcat420/MFANNv0.22.1-Q8_0-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 netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
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 netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
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 netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- vLLM
How to use netcat420/MFANNv0.22.1-Q8_0-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "netcat420/MFANNv0.22.1-Q8_0-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": "netcat420/MFANNv0.22.1-Q8_0-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
- SGLang
How to use netcat420/MFANNv0.22.1-Q8_0-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 "netcat420/MFANNv0.22.1-Q8_0-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": "netcat420/MFANNv0.22.1-Q8_0-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 "netcat420/MFANNv0.22.1-Q8_0-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": "netcat420/MFANNv0.22.1-Q8_0-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use netcat420/MFANNv0.22.1-Q8_0-GGUF with Ollama:
ollama run hf.co/netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use netcat420/MFANNv0.22.1-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
- Lemonade
How to use netcat420/MFANNv0.22.1-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull netcat420/MFANNv0.22.1-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.MFANNv0.22.1-Q8_0-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
this is the bugfix release to MFANNv0.22 which had errors involving refusing to generate a response to certain questions, usually by asking you what you would like to do and ignoring your question, along with some perplexity issues. i suspect this has to do with the DARE-TIES merging i have been doing, which up until now has actually proven to work well but i guess after a good bit of iterations pass, degradation appears to show up. so for the time being im sticking to this new SLERP merging routine i have planned.
designed to be a generally intelligent chain of thought reasoner model. this model is currently still in pre release v0.x beta, and may produce responses not suitable for all audiences due to its uncensored nature.
Thank you so much to @mlabonne for creating the base model mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated for this experiment! please go show him some love too!
System prompt:
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a helpful, respectful and honest assistant. Always answer as helpfully as possible.<|eot_id|>
netcat420/MFANNv0.22.1-Q8_0-GGUF
This model was converted to GGUF format from netcat420/MFANNv0.22.1 using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo netcat420/MFANNv0.22.1-Q8_0-GGUF --hf-file mfannv0.22.1-q8_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo netcat420/MFANNv0.22.1-Q8_0-GGUF --hf-file mfannv0.22.1-q8_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo netcat420/MFANNv0.22.1-Q8_0-GGUF --hf-file mfannv0.22.1-q8_0.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo netcat420/MFANNv0.22.1-Q8_0-GGUF --hf-file mfannv0.22.1-q8_0.gguf -c 2048
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Base model
netcat420/MFANN-llama3.1-abliterated-v2