huihui-ai/Huihui-MiroThinker-v1.0-30B-abliterated
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Model: huihui-ai/Huihui-MiroThinker-v1.0-30B-abliterated
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---
library_name: transformers
pipeline_tag: text-generation
license: mit
language:
- en
base_model:
- miromind-ai/MiroThinker-v1.0-30B
tags:
- agent
- open-source
- miromind
- deep-research
- chat
- abliterated
- uncensored
---
# huihui-ai/Huihui-MiroThinker-v1.0-30B-abliterated
This is an uncensored version of [miromind-ai/MiroThinker-v1.0-30B](https://huggingface.co/miromind-ai/MiroThinker-v1.0-30B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/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.
## ollama
You can use [huihui_ai/mirothinker1-abliterated:30b](https://ollama.com/huihui_ai/mirothinker1-abliterated:30b) directly,
```
ollama run huihui_ai/mirothinker1-abliterated:30b
```
## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:
```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer
import torch
import os
import signal
import time
def parse_args():
parser = argparse.ArgumentParser(
description="Load HuggingFace model."
)
parser.add_argument(
"--base_model",
type=str,
default="huihui-ai/Huihui-MiroThinker-v1.0-30B-abliterated",
help="HuggingFace repo or local path of the base model.",
)
parser.add_argument(
"--dtype",
type=str,
default="bfloat16",
choices=["float16", "bfloat16", "float32"],
help="Data type for loading the base model (default: bfloat16).",
)
parser.add_argument(
"--device_map",
type=str,
default="auto",
help="Device map for model loading (e.g. 'cpu', 'auto').",
)
return parser.parse_args()
def main():
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')}")
args = parse_args()
# Load the model and tokenizer
print(f"Load Model {args.base_model} ... ")
quant_config_4 = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4" if args.device_map == "cpu" else "fp4",
bnb_4bit_use_double_quant=True,
llm_int8_enable_fp32_cpu_offload=True,
)
torch_dtype = {
"float16": torch.float16,
"bfloat16": torch.bfloat16,
"float32": torch.float32,
}[args.dtype]
model = AutoModelForCausalLM.from_pretrained(
args.base_model,
dtype=torch_dtype,
device_map=args.device_map,
trust_remote_code=True,
#quantization_config=quant_config_4,
#attn_implementation="eager",
)
tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
tokenizer.padding_side = 'left'
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
messages = []
skip_prompt=True
skip_special_tokens=True
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
self.init_time = time.time() # Record initialization time
self.end_time = None # To store end time
self.first_token_time = None # To store first token generation time
self.token_count = 0 # To track total tokens
def on_finalized_text(self, text: str, stream_end: bool = False):
if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text
self.first_token_time = time.time()
if stream_end:
self.end_time = time.time() # Record end time when streaming ends
self.generated_text += text
self.token_count += 1
print(text, end="", flush=True)
if self.stop_flag:
raise StopIteration
def stop_generation(self):
self.stop_flag = True
self.end_time = time.time() # Record end time when generation is stopped
def get_metrics(self):
"""Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second."""
if self.end_time is None:
self.end_time = time.time() # Set end time if not already set
total_time = self.end_time - self.init_time # Total time from init to end
tokens_per_second = self.token_count / total_time if total_time > 0 else 0
first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None
metrics = {
"init_time": self.init_time,
"first_token_time": self.first_token_time,
"first_token_latency": first_token_latency,
"end_time": self.end_time,
"total_time": total_time, # Total time in seconds
"total_tokens": self.token_count,
"tokens_per_second": tokens_per_second
}
return metrics
def generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, max_new_tokens):
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(
text,
return_tensors="pt",
).to(model.device)
streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens)
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(
**inputs,
max_new_tokens=max_new_tokens,
#pad_token_id=tokenizer.pad_token_id,
#eos_token_id=tokenizer.eos_token_id,
streamer=streamer
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del inputs
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
while True:
user_input = input("User: ").strip()
if user_input.lower() == "/exit":
print("Exiting chat.")
break
if user_input.lower() == "/clear":
messages = []
print("Chat history cleared. Starting a new conversation.")
continue
if user_input.lower() == "/skip_prompt":
if skip_prompt:
skip_prompt = False
print("skip_prompt = False.")
else:
skip_prompt = True
print("skip_prompt = True.")
continue
if user_input.lower() == "/skip_special_tokens":
if skip_special_tokens:
skip_special_tokens = False
print("skip_special_tokens = False.")
else:
skip_special_tokens = True
print("skip_special_tokens = True.")
continue
if not user_input:
print("Input cannot be empty. Please enter something.")
continue
messages.append({"role": "user", "content": user_input})
response, stop_flag, metrics = generate_stream(model, tokenizer, messages, skip_prompt, skip_special_tokens, 40960)
print("\n\nMetrics:")
for key, value in metrics.items():
print(f" {key}: {value}")
print("", flush=True)
if stop_flag:
continue
messages.append({"role": "assistant", "content": response})
if __name__ == "__main__":
main()
```
### Usage Warnings
- **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.
- **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
##### 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)!