Instructions to use QuantFactory/Fatgirl_v2_DPO_8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/Fatgirl_v2_DPO_8B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Fatgirl_v2_DPO_8B-GGUF", filename="Fatgirl_v2_DPO_8B.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/Fatgirl_v2_DPO_8B-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 QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/Fatgirl_v2_DPO_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 QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Fatgirl_v2_DPO_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 QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/Fatgirl_v2_DPO_8B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/Fatgirl_v2_DPO_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 QuantFactory/Fatgirl_v2_DPO_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 QuantFactory/Fatgirl_v2_DPO_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 QuantFactory/Fatgirl_v2_DPO_8B-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use QuantFactory/Fatgirl_v2_DPO_8B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Fatgirl_v2_DPO_8B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Fatgirl_v2_DPO_8B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Fatgirl_v2_DPO_8B-GGUF-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)QuantFactory/Fatgirl_v2_DPO_8B-GGUF
This is quantized version of FourOhFour/Fatgirl_v2_DPO_8B created using llama.cpp
Original Model Card
Fatgirl_v2_DPO_8B
Fatgirl_v2_DPO_8B is a merge of the following models using LazyMergekit:
π§© Configuration
models:
- model: FourOhFour/Fatgirl_v2_8B+jeiku/Fatgirl_DPO_LoRA
merge_method: passthrough
dtype: bfloat16
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "jeiku/Fatgirl_v2_DPO_8B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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Inference Providers NEW
This model isn't deployed by any Inference Provider. π Ask for provider support
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Fatgirl_v2_DPO_8B-GGUF", filename="", )