Instructions to use huihui-ai/Llama-3.3-70B-Instruct-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huihui-ai/Llama-3.3-70B-Instruct-abliterated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="huihui-ai/Llama-3.3-70B-Instruct-abliterated") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("huihui-ai/Llama-3.3-70B-Instruct-abliterated") model = AutoModelForCausalLM.from_pretrained("huihui-ai/Llama-3.3-70B-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 huihui-ai/Llama-3.3-70B-Instruct-abliterated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huihui-ai/Llama-3.3-70B-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": "huihui-ai/Llama-3.3-70B-Instruct-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated
- SGLang
How to use huihui-ai/Llama-3.3-70B-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 "huihui-ai/Llama-3.3-70B-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": "huihui-ai/Llama-3.3-70B-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 "huihui-ai/Llama-3.3-70B-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": "huihui-ai/Llama-3.3-70B-Instruct-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use huihui-ai/Llama-3.3-70B-Instruct-abliterated with Docker Model Runner:
docker model run hf.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated
huihui-ai/Llama-3.3-70B-Instruct-abliterated
This is an uncensored version of meta-llama/Llama-3.3-70B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
If you don't get the result you want, you can try the same question again.
Use with ollama
You can use huihui_ai/llama3.3-abliterated directly,
ollama run huihui_ai/llama3.3-abliterated
Use with transformers
Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
Make sure to update your transformers installation via pip install --upgrade transformers.
See the snippet below for usage with Transformers:
import transformers
import torch
model_id = "huihui-ai/Llama-3.3-70B-Instruct-abliterated"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
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Model tree for huihui-ai/Llama-3.3-70B-Instruct-abliterated
Base model
meta-llama/Llama-3.1-70B