Instructions to use failspy/llama-3-70B-Instruct-abliterated-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use failspy/llama-3-70B-Instruct-abliterated-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("failspy/llama-3-70B-Instruct-abliterated-GGUF", dtype="auto")

llama-cpp-python

How to use failspy/llama-3-70B-Instruct-abliterated-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="failspy/llama-3-70B-Instruct-abliterated-GGUF",
	filename="llama-3-70B-Instruct-abliterated-fp16-00001-of-00017.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 failspy/llama-3-70B-Instruct-abliterated-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf failspy/llama-3-70B-Instruct-abliterated-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 failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf failspy/llama-3-70B-Instruct-abliterated-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 failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M

Use Docker

docker model run hf.co/failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use failspy/llama-3-70B-Instruct-abliterated-GGUF with Ollama:
```
ollama run hf.co/failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M
```

Unsloth Studio

How to use failspy/llama-3-70B-Instruct-abliterated-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 failspy/llama-3-70B-Instruct-abliterated-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 failspy/llama-3-70B-Instruct-abliterated-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for failspy/llama-3-70B-Instruct-abliterated-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use failspy/llama-3-70B-Instruct-abliterated-GGUF with Docker Model Runner:
```
docker model run hf.co/failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M
```

Lemonade

How to use failspy/llama-3-70B-Instruct-abliterated-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull failspy/llama-3-70B-Instruct-abliterated-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-70B-Instruct-abliterated-GGUF-Q4_K_M

List all available models

lemonade list

failspy commited on May 7, 2024

Commit

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@@ -11,12 +11,15 @@ TL;DR: this model has had certain weights manipulated to "inhibit" the model's a
 ## GGUF quants
 Uploaded quants:
 fp16 (in main) - good for converting to other platforms or getting the quantization you actually want, not recommended
 q8_0 (in own branch) - if you've got the spare capacity, might as well?
 q6_0 (in own branch) - this will probably be the best balance in terms of quality/performance
 q4 (in main) - recommended for ~48GB VRAM setups
 q3 (in main) - decent quality
 q2 (in main) - surprisingly decent quality
 ## For the people who like tinkering or looking to save bandwidth

 ## GGUF quants
 Uploaded quants:
 fp16 (in main) - good for converting to other platforms or getting the quantization you actually want, not recommended
 q8_0 (in own branch) - if you've got the spare capacity, might as well?
 q6_0 (in own branch) - this will probably be the best balance in terms of quality/performance
 q4 (in main) - recommended for ~48GB VRAM setups
 q3 (in main) - decent quality
 q2 (in main) - surprisingly decent quality
 ## For the people who like tinkering or looking to save bandwidth