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Model: huihui-ai/Huihui-Qwen3-8B-abliterated-v2 Source: Original Platform
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302
README.md
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README.md
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---
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library_name: transformers
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen3-8B
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tags:
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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-Qwen3-8B-abliterated-v2
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This is an uncensored version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) 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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Ablation was performed using a new and faster method, which yields better results.
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**Important Note** This version is an improvement over the previous one [huihui-ai/Qwen3-8B-abliterated](https://huggingface.co/huihui-ai/Qwen3-8B-abliterated). The ollama version has also been modified.
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Changed the 0 layer to eliminate the problem of garbled codes
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## ollama
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You can use [huihui_ai/qwen3-abliterated:8b-v2](https://ollama.com/huihui_ai/qwen3-abliterated:8b-v2) directly, Switch the thinking toggle using /set think and /set nothink
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```
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ollama run huihui_ai/qwen3-abliterated:8b-v2
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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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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 random
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import numpy as np
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import time
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from collections import Counter
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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/Huihui-Qwen3-8B-abliterated-v2"
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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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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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messages = []
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nothink = False
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same_seed = False
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skip_prompt=True
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skip_special_tokens=True
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do_sample = True
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def set_random_seed(seed=None):
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"""Set random seed for reproducibility. If seed is None, use int(time.time())."""
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if seed is None:
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seed = int(time.time()) # Convert float to int
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed) # If using CUDA
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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return seed # Return seed for logging if needed
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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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self.generated_text += text
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# Count tokens in the generated text
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tokens = self.tokenizer.encode(text, add_special_tokens=False)
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self.token_count += len(tokens)
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print(text, end="", flush=True)
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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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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, nothink, skip_prompt, skip_special_tokens, do_sample, 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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enable_thinking = not nothink,
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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=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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generate_kwargs = {}
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if do_sample:
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generate_kwargs = {
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"do_sample": do_sample,
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"max_length": max_new_tokens,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"repetition_penalty": 1.2,
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"no_repeat_ngram_size": 2
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}
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else:
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generate_kwargs = {
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"do_sample": do_sample,
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"max_length": max_new_tokens,
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"repetition_penalty": 1.2,
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"no_repeat_ngram_size": 2
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}
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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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pad_token_id=tokenizer.pad_token_id,
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streamer=streamer,
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**generate_kwargs
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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, streamer.get_metrics()
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init_seed = set_random_seed()
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while True:
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if same_seed:
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set_random_seed(init_seed)
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else:
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init_seed = set_random_seed()
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print(f"\nnothink: {nothink}")
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print(f"skip_prompt: {skip_prompt}")
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print(f"skip_special_tokens: {skip_special_tokens}")
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print(f"do_sample: {do_sample}")
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print(f"same_seed: {same_seed}, {init_seed}\n")
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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() == "/nothink":
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nothink = not nothink
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continue
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if user_input.lower() == "/skip_prompt":
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skip_prompt = not skip_prompt
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continue
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if user_input.lower() == "/skip_special_tokens":
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skip_special_tokens = not skip_special_tokens
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continue
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if user_input.lower().startswith("/same_seed"):
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parts = user_input.split()
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if len(parts) == 1: # /same_seed (no number)
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same_seed = not same_seed # Toggle switch
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elif len(parts) == 2: # /same_seed <number>
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try:
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init_seed = int(parts[1]) # Extract and convert number to int
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same_seed = True
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except ValueError:
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print("Error: Please provide a valid integer after /same_seed")
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continue
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if user_input.lower() == "/do_sample":
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do_sample = not do_sample
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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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activated_experts = []
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response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 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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# Remove all hooks after inference
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for h in hooks: h.remove()
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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.
|
||||
|
||||
- **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
|
||||
|
||||
If you like it, please click 'like' and follow us for more updates.
|
||||
You can follow [x.com/support_huihui](https://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（BTC):
|
||||
```
|
||||
bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
|
||||
```
|
||||
28
added_tokens.json
Normal file
28
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,
|
||||
"<tool_call>": 151657,
|
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"<tool_response>": 151665,
|
||||
"<|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,
|
||||
"<|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,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
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85
chat_template.jinja
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85
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 message in messages[::-1] %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||
{%- 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>')[-1].lstrip('\n') %}
|
||||
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- 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' }}
|
||||
{%- endif %}
|
||||
{%- 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 %}
|
||||
30
config.json
Normal file
30
config.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 4096,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 12288,
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.52.4",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "4.52.4"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4902257696
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Normal file
3
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Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:05ceadcaaaf4bbb4c721db8a234a93f3a7ee945ecd28ffbcdd5adc05cc4b0ce8
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size 4915960368
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model-00003-of-00004.safetensors
Normal file
3
model-00003-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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size 4983068496
|
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model-00004-of-00004.safetensors
Normal file
3
model-00004-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:a19975bea23acc006b444e5cf2d6b76f2163c47b39214d2c7f8fb86f7b4629d6
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size 1580230264
|
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406
model.safetensors.index.json
Normal file
406
model.safetensors.index.json
Normal file
@@ -0,0 +1,406 @@
|
||||
{
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||||
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||||
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|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
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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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|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151646": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"single_word": false,
|
||||
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|
||||
},
|
||||
"151647": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151648": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151649": {
|
||||
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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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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151652": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151653": {
|
||||
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|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
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|
||||
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|
||||
},
|
||||
"151655": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151658": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151659": {
|
||||
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
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|
||||
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|
||||
},
|
||||
"151660": {
|
||||
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|
||||
"lstrip": false,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
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|
||||
},
|
||||
"151664": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151665": {
|
||||
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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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|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
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|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
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|
||||
"rstrip": false,
|
||||
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|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
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|
||||
"<|object_ref_start|>",
|
||||
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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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|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
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|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
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