Instructions to use Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated")
model = PeftModel.from_pretrained(base_model, "Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade 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 Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16
# Run inference directly in the terminal:
llama cli -hf Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16
# Run inference directly in the terminal:
llama cli -hf Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16

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 Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16
# Run inference directly in the terminal:
./llama-cli -hf Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16

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 Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16

Use Docker

docker model run hf.co/Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16

LM Studio
Jan
Ollama
How to use Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade with Ollama:
```
ollama run hf.co/Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16
```

Unsloth Studio

How to use Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade 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 Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade 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 Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade to start chatting

Docker Model Runner
How to use Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade with Docker Model Runner:
```
docker model run hf.co/Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16
```

Lemonade

How to use Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade:F16

Run and chat with the model

lemonade run user.llama-3.1-8B-Instruct-abliterated-comrade-F16

List all available models

lemonade list

Atomic Chat

llama-3.1-8B-Instruct-abliterated-comrade

This model is a fine-tuned version of mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated on the generator dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-06
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant
lr_scheduler_warmup_ratio: 0.03
num_epochs: 2

Training results

Framework versions

PEFT 0.12.0
Transformers 4.44.2
Pytorch 2.4.0
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 34

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

8-bit

16-bit

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Model tree for Aratron1811/llama-3.1-8B-Instruct-abliterated-comrade

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Finetuned

mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated

Adapter

(16)

this model