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---
license: apache-2.0
library_name: transformers
base_model:
- ibm-granite/granite-4.1-30b
tags:
- language
- granite-4.1
- abliterated
- uncensored
---
# huihui-ai/Huihui-granite-4.1-30b-abliterated
This is an uncensored version of [ibm-granite/granite-4.1-30b](https://huggingface.co/ibm-granite/granite-4.1-30b) 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.
** Note ** The files have already been downloaded before, so there's no need to download them again. Due to a misoperation, the files now show the latest modification timestamp.
## ollama
Please use the latest version of [ollama](https://github.com/ollama/ollama/releases/tag)
You can use [huihui_ai/granite4.1-abliterated:30b](https://ollama.com/huihui_ai/granite4.1-abliterated:30b) directly,
```
ollama run huihui_ai/granite4.1-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, TextStreamer
import torch
import os
import signal
import time
def parse_args():
parser = argparse.ArgumentParser(
description="Merge LoRA weights into huihui-ai/Huihui-granite-4.1-30b-abliterated base model and save the full model."
)
parser.add_argument(
"--base_model",
type=str,
default="huihui-ai/Huihui-granite-4.1-30b-abliterated",
help="HuggingFace repo or local path of the base model.",
)
parser.add_argument(
"--dtype",
type=str,
default="bfloat16",
choices=["auto", "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 = {
"auto": "auto",
"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,
)
tokenizer = AutoTokenizer.from_pretrained(args.base_model)
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, enable_thinking, 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,
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()
messages = []
skip_prompt=True
skip_special_tokens=True
while True:
print(f"skip_prompt = {skip_prompt}.")
print(f"skip_special_tokens = {skip_special_tokens}.\n")
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 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.
### 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)!

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{%- set tools_system_message_prefix = 'You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>' %}
{%- set tools_system_message_suffix = '\n</tools>\n\nFor each tool 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>. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.' %}
{%- set documents_system_message_prefix = 'You are a helpful assistant with access to the following documents. You may use one or more documents to assist with the user query.\n\nYou are given a list of documents within <documents></documents> XML tags:\n<documents>' %}
{%- set documents_system_message_suffix = '\n</documents>\n\nWrite the response to the user\'s input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.' %}
{%- if available_tools is defined and available_tools %}
{%- set tools = available_tools %}
{%- endif %}
{%- set ns = namespace(tools_system_message=tools_system_message_prefix,
documents_system_message=documents_system_message_prefix,
system_message=''
) %}
{%- if tools %}
{%- for tool in tools %}
{%- set ns.tools_system_message = ns.tools_system_message + '\n' + (tool | tojson) %}
{%- endfor %}
{%- set ns.tools_system_message = ns.tools_system_message + tools_system_message_suffix %}
{%- else %}
{%- set ns.tools_system_message = '' %}
{%- endif %}
{%- if documents %}
{%- for document in documents %}
{%- set ns.documents_system_message = ns.documents_system_message + '\n' + (document | tojson) %}
{%- endfor %}
{%- set ns.documents_system_message = ns.documents_system_message + documents_system_message_suffix %}
{%- else %}
{%- set ns.documents_system_message = '' %}
{%- endif %}
{%- if messages[0].role == 'system' %}
{%- if messages[0].content is string %}
{%- set ns.system_message = messages[0].content %}
{%- elif messages[0].content is iterable %}
{%- for entry in messages[0].content %}
{%- if entry.type== 'text' %}
{%- if ns.system_message != '' %}
{%- set ns.system_message = ns.system_message + '\n' %}
{%- endif %}
{%- set ns.system_message = ns.system_message + entry.text %}
{%- endif %}
{%- endfor %}
{%- endif %}
{%- if tools and documents %}
{%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message + '\n\n' + ns.documents_system_message %}
{%- elif tools %}
{%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message %}
{%- elif documents %}
{%- set ns.system_message = ns.system_message + '\n\n' + ns.documents_system_message %}
{%- endif %}
{%- else %}
{%- if tools and documents %}
{%- set ns.system_message = ns.tools_system_message + '\n\n' + ns.documents_system_message %}
{%- elif tools %}
{%- set ns.system_message = ns.tools_system_message %}
{%- elif documents %}
{%- set ns.system_message = ns.documents_system_message %}
{%- endif %}
{%- endif %}
{%- if ns.system_message %}
{{- '<|start_of_role|>system<|end_of_role|>' + ns.system_message + '<|end_of_text|>\n' }}
{%- endif %}
{%- for message in messages %}
{%- set content = namespace(val='') %}
{%- if message.content is string %}
{%- set content.val = message.content %}
{%- else %}
{%- if message.content is iterable %}
{%- for entry in message.content %}
{%- if entry.type== 'text' %}
{%- if content.val != '' %}
{%- set content.val = content.val + '\n' %}
{%- endif %}
{%- set content.val = content.val + entry.text %}
{%- endif %}
{%- endfor %}
{%- endif %}
{%- endif %}
{%- if (message.role == 'user') or (message.role == 'system' and not loop.first) %}
{{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val + '<|end_of_text|>\n' }}
{%- elif message.role == 'assistant' %}
{{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val }}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content.val) 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 %}
{{- '<|end_of_text|>\n' }}
{%- elif message.role == 'tool' %}
{%- if loop.first or (messages[loop.index0 - 1].role != 'tool') %}
{{- '<|start_of_role|>user<|end_of_role|>' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content.val }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != 'tool') %}
{{- '<|end_of_text|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_of_role|>assistant<|end_of_role|>' }}
{%- endif %}

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{
"architectures": [
"GraniteForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"attention_multiplier": 0.0078125,
"bos_token_id": 100257,
"embedding_multiplier": 12.0,
"eos_token_id": 100257,
"hidden_act": "silu",
"hidden_size": 4096,
"init_method": "mup",
"initializer_range": 0.1,
"intermediate_size": 32768,
"logits_scaling": 16.0,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "granite",
"num_attention_heads": 32,
"num_hidden_layers": 64,
"num_key_value_heads": 8,
"pad_token_id": 100256,
"residual_multiplier": 0.175,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"rope_theta": 50000000,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"transformers_version": "4.53.3",
"use_cache": true,
"vocab_size": 100352
}

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{
"_from_model_config": true,
"bos_token_id": 100257,
"eos_token_id": 100257,
"pad_token_id": 100256,
"transformers_version": "4.53.3"
}

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},
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}

1
vocab.json Normal file
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