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---
license: apache-2.0
language:
- en
- zh
library_name: transformers
pipeline_tag: text-generation
tags:
- minicpm
- minicpm5
- llama
- text-generation
- long-context
- tool-calling
- on-device
- edge-ai
- abliterated
- uncensored
base_model:
- openbmb/MiniCPM5-1B
---
# huihui-ai/Huihui-MiniCPM5-1B-abliterated
This is an uncensored version of [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) 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.
## Usage
You can use this model in your applications by loading it with Hugging Face's `transformers` library:
```bash
pip install -U "transformers>=5.6" accelerate torch
```
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "huihui-ai/Huihui-MiniCPM5-1B-abliterated"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
enable_thinking=False,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```
### 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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{{- bos_token }}{%- if tools %}
{%- set tool_definitions %}
{{- "# Tools\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson(ensure_ascii=False) }}
{%- endfor %}
{{- '\n</tools>\n\nTool usage guidelines:\n- You may call zero or more functions. If no function calls are needed, just answer normally and do not include any <function ... </function>.\n- When calling a function, return an XML object within <function ... </function> using:\n<function name="function-name"><param name="param-name">param-value</param></function>\n- param-value may be multi-line. If it contains <, & or newline characters, wrap it in a CDATA block: <param name="param-name"><![CDATA[...multi-line value...]]></param>' }}
{%- endset %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{%- if '<tool_def_sep>' in messages[0].content %}
{{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}
{%- else %}
{{- messages[0].content + '\n\n' + tool_definitions }}
{%- endif %}
{%- else %}
{{- tool_definitions.lstrip() }}
{%- endif %}
{{- '<|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 message.content is string 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.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{%- set reasoning_content = '' %}
{%- if message.reasoning_content is string %}
{%- set reasoning_content = message.reasoning_content %}
{%- else %}
{%- if '</think>' in content %}
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
{%- endif %}
{%- endif %}
{%- if message.tool_calls %}
{%- set content_parts = content.split('<tool_sep>') %}
{%- set processed_content = content_parts[0] %}
{%- set tool_calls_count = message.tool_calls|length %}
{%- set tool_sep_count = content_parts|length - 1 %}
{%- set min_count = [tool_calls_count, tool_sep_count]|min %}
{%- for i in range(1, content_parts|length) %}
{%- set tool_index = i - 1 %}
{%- if tool_index < tool_calls_count %}
{%- set tool_call = message.tool_calls[tool_index] %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{%- set single_tool_xml %}
{{- '<function name="' ~ tool_call.name ~ '">' }}
{%- if tool_call.arguments %}
{%- set args_dict = tool_call.arguments %}
{%- for param_name, param_value in args_dict.items() %}
{{- '<param name="' ~ param_name ~ '">' }}
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
{{- '<![CDATA[' + param_value + ']]>' }}
{%- else %}
{{- param_value }}
{%- endif %}
{{- '</param>' }}
{%- endfor %}
{%- endif %}
{{- '</function>' }}
{%- endset %}
{%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
{%- else %}
{%- set processed_content = processed_content + content_parts[i] %}
{%- endif %}
{%- endfor %}
{%- if tool_calls_count > tool_sep_count %}
{%- for remaining_index in range(tool_sep_count, tool_calls_count) %}
{%- set tool_call = message.tool_calls[remaining_index] %}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{%- set remaining_tool_xml %}
{{- '<function name="' ~ tool_call.name ~ '">' }}
{%- if tool_call.arguments %}
{%- set args_dict = tool_call.arguments %}
{%- for param_name, param_value in args_dict.items() %}
{{- '<param name="' ~ param_name ~ '">' }}
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
{{- '<![CDATA[' + param_value + ']]>' }}
{%- else %}
{{- param_value }}
{%- endif %}
{{- '</param>' }}
{%- endfor %}
{%- endif %}
{{- '</function>' }}
{%- endset %}
{%- set processed_content = processed_content + remaining_tool_xml %}
{%- endfor %}
{%- endif %}
{%- set content = processed_content %}
{%- endif %}
{%- if loop.index0 > ns.last_query_index %}
{%- if 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 and not has_tool_sep %}
{%- 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 %}
{{- '<function name="' ~ tool_call.name ~ '">' }}
{%- if tool_call.arguments %}
{%- set args_dict = tool_call.arguments %}
{%- for param_name, param_value in args_dict.items() %}
{{- '<param name="' ~ param_name ~ '">' }}
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
{{- '<![CDATA[' + param_value + ']]>' }}
{%- else %}
{{- param_value }}
{%- endif %}
{{- '</param>' }}
{%- endfor %}
{%- endif %}
{{- '</function>' }}
{%- 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' }}
{%- if message.content is string %}
{{- content }}
{%- else %}
{{- message.content | tojson(ensure_ascii=False) }}
{%- endif %}
{{- '\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 %}
{%- if enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- elif enable_thinking is true %}
{{- '<think>\n' }}
{%- endif %}
{%- endif %}
{%- endif %}

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{
"_name_or_path": "openbmb/MiniCPM5-1B",
"architectures": [
"LlamaForCausalLM"
],
"bos_token_id": 0,
"eos_token_id": [
1,
130073
],
"pad_token_id": 1,
"hidden_act": "silu",
"hidden_size": 1536,
"initializer_range": 0.02,
"intermediate_size": 4608,
"max_position_embeddings": 131072,
"model_type": "llama",
"num_attention_heads": 16,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"head_dim": 128,
"rms_norm_eps": 1e-06,
"rope_theta": 5000000,
"rope_scaling": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "5.6.2",
"use_cache": true,
"vocab_size": 130560
}

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"bos_token_id": 0,
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"pad_token_id": 1,
"do_sample": true,
"temperature": 0.9,
"top_p": 0.95,
"transformers_version": "5.6.2"
}

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