Instructions to use grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-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 grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-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 grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-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 grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-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 grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M

SGLang

How to use grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF with Ollama:
```
ollama run hf.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF with Docker Model Runner:
```
docker model run hf.co/grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M
```

Lemonade

How to use grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grimjim/Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Llama-3.1-8B-Instruct-abliterated_via_adapter-GGUF

This repo contains select GGUF quants of a model that is a merge of pre-trained language models created using mergekit.

A LoRA was applied to "abliterate" refusals in meta-llama/Meta-Llama-3.1-8B-Instruct. The result appears to work despite the LoRA having been derived from Llama 3 instead of Llama 3.1, which implies that there is significant feature commonality between the 3 and 3.1 models.

The LoRA was extracted from failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 and uses meta-llama/Meta-Llama-3-8B-Instruct as a base.

Built with Llama.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using meta-llama/Meta-Llama-3.1-8B-Instruct + grimjim/Llama-3-Instruct-abliteration-LoRA-8B as a base.

Configuration

The following YAML configuration was used to produce this model:

base_model: meta-llama/Meta-Llama-3.1-8B-Instruct+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
dtype: bfloat16
merge_method: task_arithmetic
parameters:
  normalize: false
slices:
- sources:
  - layer_range: [0, 32]
    model: meta-llama/Meta-Llama-3.1-8B-Instruct+grimjim/Llama-3-Instruct-abliteration-LoRA-8B
    parameters:
      weight: 1.0