Nx
Collection
Main series of models by GoofyLM. • 6 items • Updated
How to use GoofyLM/N1-Quant with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GoofyLM/N1-Quant", filename="N1-auto.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
How to use GoofyLM/N1-Quant with llama.cpp:
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf GoofyLM/N1-Quant:BF16 # Run inference directly in the terminal: llama-cli -hf GoofyLM/N1-Quant:BF16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf GoofyLM/N1-Quant:BF16 # Run inference directly in the terminal: llama-cli -hf GoofyLM/N1-Quant:BF16
# 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 GoofyLM/N1-Quant:BF16 # Run inference directly in the terminal: ./llama-cli -hf GoofyLM/N1-Quant:BF16
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 GoofyLM/N1-Quant:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf GoofyLM/N1-Quant:BF16
docker model run hf.co/GoofyLM/N1-Quant:BF16
How to use GoofyLM/N1-Quant with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GoofyLM/N1-Quant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "GoofyLM/N1-Quant",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/GoofyLM/N1-Quant:BF16
How to use GoofyLM/N1-Quant with Ollama:
ollama run hf.co/GoofyLM/N1-Quant:BF16
How to use GoofyLM/N1-Quant with Unsloth Studio:
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 GoofyLM/N1-Quant to start chatting
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 GoofyLM/N1-Quant to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for GoofyLM/N1-Quant to start chatting
How to use GoofyLM/N1-Quant with Docker Model Runner:
docker model run hf.co/GoofyLM/N1-Quant:BF16
How to use GoofyLM/N1-Quant with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GoofyLM/N1-Quant:BF16
lemonade run user.N1-Quant-BF16
lemonade list
Banner by Croissant
N1 is a small, experimental Chain-of-Thought (COT) model based on the LLaMA architecture, developed by GoofyLM.
{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
You are a helpful AI assistant named N1, trained by GoofyLM<|im_end|>
' }}{% endif %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}
This model is designed for text generation tasks with a focus on reasoning through problems step-by-step (using its Chain-of-Thought).
The model can be loaded using the following:
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="GoofyLM/N1",
filename="N1_Q8_0.gguf",
)
ollama run hf.co/GoofyLM/N1:Q4_K_M
Base model
GoofyLM/N1