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Model: huihui-ai/Huihui-MiroThinker-v1.0-8B-abliterated Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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pipeline_tag: text-generation
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license: mit
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language:
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- en
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base_model:
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- miromind-ai/MiroThinker-v1.0-8B
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tags:
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- agent
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- open-source
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- miromind
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- deep-research
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- chat
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- abliterated
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- uncensored
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---
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# huihui-ai/Huihui-MiroThinker-v1.0-8B-abliterated
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This is an uncensored version of [miromind-ai/MiroThinker-v1.0-8B](https://huggingface.co/miromind-ai/MiroThinker-v1.0-8B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
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This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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## ollama
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You can use [huihui_ai/mirothinker1-abliterated](https://ollama.com/huihui_ai/mirothinker1-abliterated) directly,
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```
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ollama run huihui_ai/mirothinker1-abliterated
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```
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## Usage
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You can use this model in your applications by loading it with Hugging Face's `transformers` library:
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```python
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import argparse
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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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import time
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Load HuggingFace model."
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)
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parser.add_argument(
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"--base_model",
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type=str,
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default="huihui-ai/Huihui-MiroThinker-v1.0-8B-abliterated",
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help="HuggingFace repo or local path of the base model.",
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)
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parser.add_argument(
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"--dtype",
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type=str,
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default="bfloat16",
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choices=["float16", "bfloat16", "float32"],
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help="Data type for loading the base model (default: bfloat16).",
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)
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parser.add_argument(
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"--device_map",
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type=str,
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default="auto",
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help="Device map for model loading (e.g. 'cpu', 'auto').",
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)
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return parser.parse_args()
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def main():
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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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args = parse_args()
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# Load the model and tokenizer
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print(f"Load Model {args.base_model} ... ")
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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_quant_type="nf4" if args.device_map == "cpu" else "fp4",
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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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torch_dtype = {
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"float16": torch.float16,
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"bfloat16": torch.bfloat16,
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"float32": torch.float32,
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}[args.dtype]
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model = AutoModelForCausalLM.from_pretrained(
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args.base_model,
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dtype=torch_dtype,
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device_map=args.device_map,
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trust_remote_code=True,
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#quantization_config=quant_config_4,
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#attn_implementation="eager",
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)
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tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
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tokenizer.padding_side = 'left'
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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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messages = []
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skip_prompt=True
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skip_special_tokens=True
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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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self.init_time = time.time() # Record initialization time
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self.end_time = None # To store end time
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self.first_token_time = None # To store first token generation time
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self.token_count = 0 # To track total tokens
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def on_finalized_text(self, text: str, stream_end: bool = False):
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if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text
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self.first_token_time = time.time()
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if stream_end:
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self.end_time = time.time() # Record end time when streaming ends
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self.generated_text += text
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self.token_count += 1
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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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self.end_time = time.time() # Record end time when generation is stopped
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def get_metrics(self):
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"""Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
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if self.end_time is None:
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self.end_time = time.time() # Set end time if not already set
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total_time = self.end_time - self.init_time # Total time from init to end
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tokens_per_second = self.token_count / total_time if total_time > 0 else 0
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first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
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metrics = {
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"init_time": self.init_time,
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"first_token_time": self.first_token_time,
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"first_token_latency": first_token_latency,
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"end_time": self.end_time,
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"total_time": total_time, # Total time in seconds
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"total_tokens": self.token_count,
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"tokens_per_second": tokens_per_second
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}
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return metrics
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def generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, max_new_tokens):
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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)
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inputs = tokenizer(
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text,
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return_tensors="pt",
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).to(model.device)
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streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
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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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**inputs,
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max_new_tokens=max_new_tokens,
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#pad_token_id=tokenizer.pad_token_id,
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#eos_token_id=tokenizer.eos_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 inputs
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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, streamer.get_metrics()
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while True:
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user_input = input("User: ").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 = []
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print("Chat history cleared. Starting a new conversation.")
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continue
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if user_input.lower() == "/skip_prompt":
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if skip_prompt:
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skip_prompt = False
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print("skip_prompt = False.")
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else:
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skip_prompt = True
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print("skip_prompt = True.")
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continue
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if user_input.lower() == "/skip_special_tokens":
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if skip_special_tokens:
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skip_special_tokens = False
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print("skip_special_tokens = False.")
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else:
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skip_special_tokens = True
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print("skip_special_tokens = True.")
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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, metrics = generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, 40960)
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print("\n\nMetrics:")
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for key, value in metrics.items():
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print(f" {key}: {value}")
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print("", flush=True)
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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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if __name__ == "__main__":
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main()
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```
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### Usage Warnings
|
||||
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||||
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||||
- **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
|
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|
||||
- **Not Suitable for All Audiences**: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
|
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|
||||
- **Legal and Ethical Responsibilities**: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
|
||||
|
||||
- **Research and Experimental Use**: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
|
||||
|
||||
- **Monitoring and Review Recommendations**: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
|
||||
|
||||
- **No Default Safety Guarantees**: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
|
||||
|
||||
|
||||
### Donation
|
||||
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
|
||||
- bitcoin:
|
||||
```
|
||||
bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
|
||||
```
|
||||
- Support our work on [Ko-fi](https://ko-fi.com/huihuiai)!
|
||||
|
||||
28
added_tokens.json
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
|
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"<|vision_end|>": 151653,
|
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"<|vision_pad|>": 151654,
|
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"<|vision_start|>": 151652
|
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}
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94
chat_template.jinja
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chat_template.jinja
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{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for forward_message in messages %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- set message = messages[index] %}
|
||||
{%- set current_content = message.content if message.content is not none else '' %}
|
||||
{%- set tool_start = '<tool_response>' %}
|
||||
{%- set tool_start_length = tool_start|length %}
|
||||
{%- set start_of_message = current_content[:tool_start_length] %}
|
||||
{%- set tool_end = '</tool_response>' %}
|
||||
{%- set tool_end_length = tool_end|length %}
|
||||
{%- set start_pos = (current_content|length) - tool_end_length %}
|
||||
{%- if start_pos < 0 %}
|
||||
{%- set start_pos = 0 %}
|
||||
{%- endif %}
|
||||
{%- set end_of_message = current_content[start_pos:] %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set content = message.content %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in message.content %}
|
||||
{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
|
||||
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
|
||||
{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
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{%- endif %}
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||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
68
config.json
Normal file
68
config.json
Normal file
@@ -0,0 +1,68 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
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|
||||
"vocab_size": 151936
|
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|
||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
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151388
merges.txt
Normal file
151388
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Normal file
File diff suppressed because it is too large
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
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||||
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
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||||
oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
|
||||
size 11422654
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239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user