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

Libraries

How to use mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated")
model = AutoModelForCausalLM.from_pretrained("mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated

SGLang

How to use mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated 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 "mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated" \
    --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": "mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated",
		"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 "mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated" \
        --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": "mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated with Docker Model Runner:
```
docker model run hf.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
```

Can we get Meta-Llama-3.1-8B-Instruct-ablitrated GGUF?

by Winnougan - opened Jul 24, 2024

Discussion

Winnougan

Jul 24, 2024

I was wondering if you could make this in GGUF form? Thanks.

StatusQuo209

Jul 24, 2024

looks like he is making GGUFs right now. I am waiting as well.

mlabonne

Owner Jul 24, 2024

Yes they're coming https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF/tree/main

Winnougan

Jul 25, 2024

Yes they're coming https://huggingface.co/mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated-GGUF/tree/main

thank you. downloaded it and using it in Oobabooga. Works like a charm. merci

urtuuuu

Jul 26, 2024

•

edited Jul 26, 2024

thank you. downloaded it and using it in Oobabooga. Works like a charm. merci

are you sure? Oobabooga compared to simple llama.cpp that you run in cmd, gives very different results. I'm asking all my usual reasoning questions, and in oobabooga it fails a lot complared to llama.cpp. I know it's probably because of the settings like "instruction template", but i have no idea how to fix this in Ooba. In standard llama.cpp this model performs much better. Here are the setting:
llama-cli -m Meta-Llama-3.1-8B-Instruct-abliterated.q5_k.gguf -p "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful, smart, kind, and efficient AI assistant. You always fulfill the user's requests to the best of your ability.<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" -c 500 -ngl 3 -c 4096 --conversation --multiline-input --color --temp 0.1

Workermen

Jul 27, 2024

Dear Mlabbone,
Have Meta fixed the method abliterated in the new version of llama-3.1?
I have uploaded your model mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated\nand this model responds to many questions with "sorry, I cannot help you with this," while the model mlabonne/Meta-Llama-3-8B-Instruct-abliterated answers these questions as expected

mlabonne

Owner Jul 27, 2024

@Workermen Can you send me the prompt you used? It worked in my tests

Workermen

Aug 12, 2024

@Workermen Can you send me the prompt you used? It worked in my tests

You didn't forget anything, the suggestion I linked was somebody trying to get it to work in a broken way. I only thought it worked because I'd been staring at my screen for too long. After taking a break and getting some sleep (which I should have done earlier :) and starting clean I have things working a lot better.

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment