This is an uncensored version of TokenRhythm/NeoHorse-1-4B created with abliteration (see remove-refusals-with-transformers to know more about it).
This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
Note
Layers 5-17 are being ablated (0-based indexing).
The MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support.
If needed, you only need to copy the contents of MTP-Visual to overwrite the model directory.
Usage
You can use this model in your applications by loading it with Hugging Face's transformers library:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import argparse
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import os
import signal
import time
def parse_args():
parser = argparse.ArgumentParser(
description="Load HuggingFace repo or local path of the base model"
)
parser.add_argument(
"--base_model",
type=str,
default="huihui-ai/Huihui-NeoHorse-1-4B-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} ... ")
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,
low_cpu_mem_usage=True,
)
tokenizer = AutoTokenizer.from_pretrained(args.base_model, trust_remote_code=True)
messages = []
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.think_tokens_count = 0 # To track total think tokens
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
tokens = self.tokenizer.encode(text, add_special_tokens=False)
self.token_count += len(tokens)
if self.think_tokens_count == 0 and "</think>" in self.generated_text:
self.think_tokens_count = self.token_count
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,
"think_tokens_count": self.think_tokens_count,
"real_tokens_count": self.token_count - self.think_tokens_count,
"tokens_per_second": tokens_per_second
}
return metrics
def generate_stream(model, tokenizer, messages, enable_thinking, skip_prompt, skip_special_tokens, max_new_tokens):
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=enable_thinking
)
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,
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()
skip_prompt=True
skip_special_tokens=True
enable_thinking=False
while True:
print(f"skip_prompt = {skip_prompt}.")
print(f"skip_special_tokens = {skip_special_tokens}.")
print(f"enable_thinking = {enable_thinking}.")
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":
skip_prompt = not skip_prompt
continue
if user_input.lower() == "/skip_special_tokens":
skip_special_tokens = not skip_special_tokens
continue
if user_input.lower() == "/enable_thinking":
enable_thinking = not enable_thinking
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, enable_thinking, 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.
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