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---
base_model:
- huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
tags:
- vllm
- unsloth
- abliterated
- uncensored
---
# huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated
This is an uncensored version of [unsloth/gpt-oss-20b-BF16](https://huggingface.co/unsloth/gpt-oss-20b-BF16) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch
import os
import signal
import random
import numpy as np
import time
from collections import Counter
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')}")
# Load the model and tokenizer
NEW_MODEL_ID = "huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated"
print(f"Load Model {NEW_MODEL_ID} ... ")
model = AutoModelForCausalLM.from_pretrained(
NEW_MODEL_ID,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
)
#print(model)
#print(model.config)
tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True)
messages = []
skip_prompt=False
skip_special_tokens=False
do_sample = 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()
self.generated_text += text
# Count tokens in the generated text
tokens = self.tokenizer.encode(text, add_special_tokens=False)
self.token_count += len(tokens)
print(text, end="", flush=True)
if stream_end:
self.end_time = time.time() # Record end time when streaming ends
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, do_sample, max_new_tokens):
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt",
return_dict=True,
).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)
generate_kwargs = {}
if do_sample:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"temperature": 0.7,
"top_k": 20,
"top_p": 0.8,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
else:
generate_kwargs = {
"do_sample": do_sample,
"max_length": max_new_tokens,
"repetition_penalty": 1.2,
"no_repeat_ngram_size": 2
}
print("Response: ", end="", flush=True)
try:
generated_ids = model.generate(
**input_ids,
streamer=streamer,
**generate_kwargs
)
del generated_ids
except StopIteration:
print("\n[Stopped by user]")
del input_ids
torch.cuda.empty_cache()
signal.signal(signal.SIGINT, signal.SIG_DFL)
return streamer.generated_text, streamer.stop_flag, streamer.get_metrics()
while True:
print(f"skip_prompt: {skip_prompt}")
print(f"skip_special_tokens: {skip_special_tokens}")
print(f"do_sample: {do_sample}")
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() == "/do_sample":
do_sample = not do_sample
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, do_sample, 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})
```
## 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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