Instructions to use jjee2/lora_recycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jjee2/lora_recycle with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jjee2/lora_recycle",
	filename="Aratron1811__llama-3.1-8B-Instruct-abliterated-comrade/Meta-Llama-3.1-8B-Instruct-abliterated-TQ2_0.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 jjee2/lora_recycle 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 jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
llama cli -hf jjee2/lora_recycle:TQ2_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
llama cli -hf jjee2/lora_recycle:TQ2_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 jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
./llama-cli -hf jjee2/lora_recycle:TQ2_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 jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jjee2/lora_recycle:TQ2_0

Use Docker

docker model run hf.co/jjee2/lora_recycle:TQ2_0

LM Studio
Jan
Ollama
How to use jjee2/lora_recycle with Ollama:
```
ollama run hf.co/jjee2/lora_recycle:TQ2_0
```

Unsloth Studio

How to use jjee2/lora_recycle 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 jjee2/lora_recycle 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 jjee2/lora_recycle to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jjee2/lora_recycle to start chatting

Atomic Chat new
Docker Model Runner
How to use jjee2/lora_recycle with Docker Model Runner:
```
docker model run hf.co/jjee2/lora_recycle:TQ2_0
```

Lemonade

How to use jjee2/lora_recycle with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jjee2/lora_recycle:TQ2_0

Run and chat with the model

lemonade run user.lora_recycle-TQ2_0

List all available models

lemonade list

lora_recycle / LangAGI-Lab__M2WEB-HTML-VF /optimizer.bin

jjee2

Add LangAGI-Lab__M2WEB-HTML-VF

f8dbf6d verified 3 months ago

Download

History Blame

336 MB

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:: bd217195e65b09bfcb9d8ea2b5a2c495b32ac40b9ba63acdd5cc1d318aec74d5
Size of remote file:: 336 MB
SHA256:: 2e1897358a50f79250db0490d182afe40542bcfc94e6def6495f4bc98c54a0e9

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

jjee2
/

lora_recycle

Detected Pickle imports (3)

Xet Pointer Details