141 lines
4.5 KiB
Markdown
141 lines
4.5 KiB
Markdown
---
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base_model:
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- huihui-ai/Qwen2.5-1.5B-Instruct-abliterated
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- abliterated
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- uncensored
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license: apache-2.0
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language:
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- en
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datasets:
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- huihui-ai/Guilherme34_uncensor
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---
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# huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT
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- **Developed by:** huihui-ai
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- **License:** apache-2.0
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- **Finetuned from model :** [huihui-ai/Qwen2.5-1.5B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-0.5B-Instruct-abliterated)
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- **Dataset used to train :** [huihui-ai/Guilherme34_uncensor](https://huggingface.co/datasets/huihui-ai/Guilherme34_uncensor), please refer to [SFT with Unsloth](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Qwen2.5_(7B)-Alpaca.ipynb#scrollTo=2ejIt2xSNKKp).
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## Use with transformers
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
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import torch
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import os
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import signal
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cpu_count = os.cpu_count()
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print(f"Number of CPU cores in the system: {cpu_count}")
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half_cpu_count = cpu_count // 2
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os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)
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os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)
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torch.set_num_threads(half_cpu_count)
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print(f"PyTorch threads: {torch.get_num_threads()}")
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print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}")
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print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}")
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# Load the model and tokenizer
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NEW_MODEL_ID = "huihui-ai/Qwen2.5-1.5B-Instruct-abliterated-SFT"
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print(f"Load Model {NEW_MODEL_ID} ... ")
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quant_config_4 = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True,
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llm_int8_enable_fp32_cpu_offload=True,
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)
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model = AutoModelForCausalLM.from_pretrained(
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NEW_MODEL_ID,
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device_map="auto",
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trust_remote_code=True,
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#quantization_config=quant_config_4,
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torch_dtype=torch.bfloat16
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)
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tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.pad_token_id = tokenizer.eos_token_id
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initial_messages = [{"role": "system", "content": "You are a helpful assistant."}]
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messages = initial_messages.copy()
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class CustomTextStreamer(TextStreamer):
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def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True):
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super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
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self.generated_text = ""
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self.stop_flag = False
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def on_finalized_text(self, text: str, stream_end: bool = False):
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self.generated_text += text
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print(text, end="", flush=True)
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if self.stop_flag:
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raise StopIteration
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def stop_generation(self):
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self.stop_flag = True
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def generate_stream(model, tokenizer, messages, max_new_tokens):
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input_ids = tokenizer.apply_chat_template(
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messages,
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tokenize=True,
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add_generation_prompt=True,
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return_tensors="pt"
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)
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attention_mask = torch.ones_like(input_ids, dtype=torch.long)
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tokens = input_ids.to(model.device)
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attention_mask = attention_mask.to(model.device)
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streamer = CustomTextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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def signal_handler(sig, frame):
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streamer.stop_generation()
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print("\n[Generation stopped by user with Ctrl+C]")
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signal.signal(signal.SIGINT, signal_handler)
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print("Response: ", end="", flush=True)
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try:
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generated_ids = model.generate(
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tokens,
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attention_mask=attention_mask,
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use_cache=False,
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max_new_tokens=max_new_tokens,
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do_sample=True,
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pad_token_id=tokenizer.pad_token_id,
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streamer=streamer
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)
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del generated_ids
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except StopIteration:
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print("\n[Stopped by user]")
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del input_ids, attention_mask
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torch.cuda.empty_cache()
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signal.signal(signal.SIGINT, signal.SIG_DFL)
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return streamer.generated_text, streamer.stop_flag
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while True:
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user_input = input("\nUser: ").strip()
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if user_input.lower() == "/exit":
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print("Exiting chat.")
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break
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if user_input.lower() == "/clear":
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messages = initial_messages.copy()
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print("Chat history cleared. Starting a new conversation.")
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continue
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if not user_input:
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print("Input cannot be empty. Please enter something.")
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continue
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messages.append({"role": "user", "content": user_input})
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response, stop_flag = generate_stream(model, tokenizer, messages, 8192)
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if stop_flag:
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continue
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messages.append({"role": "assistant", "content": response})
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```
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