huihui-ai/Moonlight-16B-A3B-Instruct-abliterated

This is an uncensored version of moonshotai/Moonlight-16B-A3B-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.

Use with transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer
NEW_MODEL_ID = "huihui-ai/Moonlight-16B-A3B-Instruct-abliterated"

model = AutoModelForCausalLM.from_pretrained(
    NEW_MODEL_ID, 
    device_map="auto", 
    trust_remote_code=True,
    torch_dtype=torch.bfloat16
)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token

tokenizer.pad_token_id = tokenizer.eos_token_id

print(dir(tokenizer))  # List available methods/attributes
print(tokenizer.__class__.__name__)  # Confirm tokenizer type

# Initialize conversation context
initial_messages = [
    {"role": "system", "content": "You are a helpful assistant provided by Moonshot-AI."}
]
messages = initial_messages.copy()  # Copy the initial conversation context

# Enter conversation loop
while True:
    # Get user input
    user_input = input("User: ").strip()  # Strip leading and trailing spaces

    # If the user types '/exit', end the conversation
    if user_input.lower() == "/exit":
        print("Exiting chat.")
        break

    # If the user types '/clean', reset the conversation context
    if user_input.lower() == "/clear":
        messages = initial_messages.copy()  # Reset conversation context
        print("Chat history cleared. Starting a new conversation.")
        continue

    # If input is empty, prompt the user and continue
    if not user_input:
        print("Input cannot be empty. Please enter something.")
        continue

    # Add user input to the conversation
    messages.append({"role": "user", "content": user_input})

    tokenized_message = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
    response_token_ids = model.generate(tokenized_message['input_ids'].to("cuda:0"), use_cache=False, pad_token_id=tokenizer.pad_token_id, max_new_tokens=8192)
    generated_tokens =response_token_ids[:, len(tokenized_message['input_ids'][0]):]
    response = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]

    # Add the model's response to the conversation
    messages.append({"role": "assistant", "content": response})

    # Print the model's response
    print(f"Response: {response}")

Donation

If you like it, please click 'like' and follow us for more updates.
You can follow x.com/support_huihui to get the latest model information from huihui.ai.

Your donation helps us continue our further development and improvement, a cup of coffee can do it.

• bitcoin:

  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge