huihui-ai/DeepSeek-V3-abliterated

This is an uncensored version of deepseek-ai/DeepSeek-V3 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.

Note

All files have been uploaded. If you have already downloaded it before, please download again to automatically get any missing files.

huggingface-cli download huihui-ai/DeepSeek-V3-abliterated --local-dir ./huihui-ai/DeepSeek-V3-abliterated --token hf_xxxx 

The next goal is deepseek-ai/DeepSeek-V3-0324.

Use with ollama

You can use huihui_ai/deepseek-v3-abliterated directly

ollama run huihui_ai/deepseek-v3-abliterated

Q4_K_M, Q3_K_M, Q2_K have been uploaded.

Use with transformers

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
import torch
import os
import signal

cpu_count = os.cpu_count()
print(f"Number of CPU cores in the system: {cpu_count}")
half_cpu_count = cpu_count // 2
os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
torch.set_num_threads(half_cpu_count)

print(f"PyTorch threads: {torch.get_num_threads()}")
print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")

NEW_MODEL_ID = "huihui-ai/DeepSeek-V3-abliterated"
print(f"Load Model {NEW_MODEL_ID} ... ")
quant_config_4 = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    llm_int8_enable_fp32_cpu_offload=True,
)

# Single RTX 4090
NUM_TRANS_LAYERS = 61

def create_device_map():
    device_map = {
        'model.embed_tokens': 0,
        'model.norm': 0,
        'model.rotary_emb': 0,
        'lm_head': 0
    }
    for start, end, gpu_id in [(0, 5, 0)]:
        for i in range(start, end):
            device_map[f'model.layers.{i}'] = gpu_id
    
    for i in range(5, NUM_TRANS_LAYERS):
        device_map[f'model.layers.{i}'] = "cpu"

    return device_map

device_map = create_device_map()

model = AutoModelForCausalLM.from_pretrained(
    NEW_MODEL_ID,
    device_map=device_map,
    trust_remote_code=True,
    quantization_config=quant_config_4,
    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

initial_messages = [{"role": "system", "content": "You are a helpful assistant."}]
messages = initial_messages.copy()

class CustomTextStreamer(TextStreamer):
    def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
        super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
        self.generated_text = ""
        self.stop_flag = False

    def on_finalized_text(self, text: str, stream_end: bool = False):
        self.generated_text += text
        print(text, end="", flush=True)
        if self.stop_flag:
            raise StopIteration

    def stop_generation(self):
        self.stop_flag = True

def generate_stream(model, tokenizer, messages, max_new_tokens):
    input_ids = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt"
    )
    attention_mask = torch.ones_like(input_ids, dtype=torch.long)
    tokens = input_ids.to(model.device) 
    attention_mask = attention_mask.to(model.device)

    streamer = CustomTextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    def signal_handler(sig, frame):
        streamer.stop_generation()
        print("\n[Generation stopped by user with Ctrl+C]")

    signal.signal(signal.SIGINT, signal_handler)
    
    print("Response: ", end="", flush=True)
    try:
        generated_ids = model.generate(
            tokens,
            attention_mask=attention_mask,
            use_cache=False,
            max_new_tokens=max_new_tokens,
            do_sample=True,
            pad_token_id=tokenizer.pad_token_id,
            streamer=streamer
        )
        del generated_ids
    except StopIteration:
        print("\n[Stopped by user]")

    del input_ids, attention_mask
    torch.cuda.empty_cache()
    signal.signal(signal.SIGINT, signal.SIG_DFL)

    return streamer.generated_text, streamer.stop_flag

while True:
    user_input = input("User: ").strip()
    if user_input.lower() == "/exit":
        print("Exiting chat.")
        break
    if user_input.lower() == "/clear":
        messages = initial_messages.copy()
        print("Chat history cleared. Starting a new conversation.")
        continue
    if not user_input:
        print("Input cannot be empty. Please enter something.")
        continue
    messages.append({"role": "user", "content": user_input})
    response, stop_flag = generate_stream(model, tokenizer, messages, 8192)
    if stop_flag:
        continue
    messages.append({"role": "assistant", "content": response})

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