Instructions to use failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 with Transformers:

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

pipe = pipeline("text-generation", model="failspy/Meta-Llama-3-8B-Instruct-abliterated-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("failspy/Meta-Llama-3-8B-Instruct-abliterated-v3")
model = AutoModelForMultimodalLM.from_pretrained("failspy/Meta-Llama-3-8B-Instruct-abliterated-v3")
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 failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "failspy/Meta-Llama-3-8B-Instruct-abliterated-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3

SGLang

How to use failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 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 "failspy/Meta-Llama-3-8B-Instruct-abliterated-v3" \
    --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": "failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
		"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 "failspy/Meta-Llama-3-8B-Instruct-abliterated-v3" \
        --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": "failspy/Meta-Llama-3-8B-Instruct-abliterated-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use failspy/Meta-Llama-3-8B-Instruct-abliterated-v3 with Docker Model Runner:
```
docker model run hf.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3
```

Can we use orthogonalization to make the LLaMa-3 (8B-70B) More Intelligent?

by Joseph717171 - opened Jun 2, 2024

Discussion

Joseph717171

Jun 2, 2024

•

edited Jun 2, 2024

Love your work! I have a question though: can we use orthogonalization to make LLaMa-3 more intelligent in its generations and prose? The thought just occurred to me after your LLaMa-3-MopeyMule Release, and my curiosity was reignited when I stumbled upon your Reddit post, detailing orthogonalization and ablation and your piqued curiosity to see what other purposes they can be used for. I think it would be cool to see orthogonalization used to make LLaMa-3’s generations more intelligent (context aware and with formatting awareness).

You’re work has some similarities to Vgel’s work with control vectors (Vgel’s blog detailing Control vectors)). Perhaps Vgel’s experiments might be helpful and enlightening for you and your work. 😁

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