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Model: jtatman/merged-llama32-1b-inappropriate-triceratops
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---
library_name: transformers
tags:
- llama
license: mit
base_model:
- nicoboss/Llama-3.2-1B-Instruct-Uncensored
- meta-llama/Llama-3.2-1B-Instruct
---
# Model Card for Model ID
This model is a base-lora-lora merge of:
- jtatman/llama-32-1b-lewd-lora
- jtatman/llama-32-1b-deepseek-dolphin-lora
on a base of:
- nicoboss/Llama-3.2-1B-Instruct-Uncensored
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** J. Tatman
- **Funded by [optional]:** Pocket Change
- **Shared by [optional]:** The Management
- **Model type:** Llama 1 billion parameters
- **Language(s) (NLP):** Multiple
- **License:** MIT/MetaLlama
- **Finetuned from model :** nicoboss/Llama-3.2-1B-Instruct-Uncensored
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** nicoboss/Llama-3.2-1B-Instruct-Uncensored
- **Paper [optional]:** Meta/Llama
## Uses
Combining uncensored (abliterated) models with artificial guardrails
removed, refusals refused, and so on gives a theoretical purer base model.
Adding uncensored small lora-applied models with the same original base
should enhance cooperative weights as the refusals are not added back
or are minimized and discareded.
In theory.
### Direct Use
As a 1b model, this should have uses beyond a typical 1b llama model.
As a 1b model, it won't be capable of everything and must be allowed
to make mistakes and learn in an agentic capacity. As a long context
1b model, it's conceivable that "agentic" use is possible, and that
the "agent" using it can supplant with short and long term memory
strategies that help reorient the model more quickly with outside
context compression.
## Bias, Risks, and Limitations
This model is theoretically uncensored, and as such has no guardrails preventing it from being used for something nefarious.
### Recommendations
Operate at your own risk
## How to Get Started with the Model
## Evaluation
TBA

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{{- bos_token }}
{%- if custom_tools is defined %}
{%- set tools = custom_tools %}
{%- endif %}
{%- if not tools_in_user_message is defined %}
{%- set tools_in_user_message = true %}
{%- endif %}
{%- if not date_string is defined %}
{%- if strftime_now is defined %}
{%- set date_string = strftime_now("%d %b %Y") %}
{%- else %}
{%- set date_string = "26 Jul 2024" %}
{%- endif %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = none %}
{%- endif %}
{#- This block extracts the system message, so we can slot it into the right place. #}
{%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{%- set system_message = "" %}
{%- endif %}
{#- System message #}
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
{%- if tools is not none %}
{{- "Environment: ipython\n" }}
{%- endif %}
{{- "Cutting Knowledge Date: December 2023\n" }}
{{- "Today Date: " + date_string + "\n\n" }}
{%- if tools is not none and not tools_in_user_message %}
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{%- endif %}
{{- system_message }}
{{- "<|eot_id|>" }}
{#- Custom tools are passed in a user message with some extra guidance #}
{%- if tools_in_user_message and not tools is none %}
{#- Extract the first user message so we can plug it in here #}
{%- if messages | length != 0 %}
{%- set first_user_message = messages[0]['content']|trim %}
{%- set messages = messages[1:] %}
{%- else %}
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
{%- endif %}
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
{{- "Given the following functions, please respond with a JSON for a function call " }}
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
{{- "Do not use variables.\n\n" }}
{%- for t in tools %}
{{- t | tojson(indent=4) }}
{{- "\n\n" }}
{%- endfor %}
{{- first_user_message + "<|eot_id|>"}}
{%- endif %}
{%- for message in messages %}
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
{%- elif 'tool_calls' in message %}
{%- if not message.tool_calls|length == 1 %}
{{- raise_exception("This model only supports single tool-calls at once!") }}
{%- endif %}
{%- set tool_call = message.tool_calls[0].function %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
{{- '{"name": "' + tool_call.name + '", ' }}
{{- '"parameters": ' }}
{{- tool_call.arguments | tojson }}
{{- "}" }}
{{- "<|eot_id|>" }}
{%- elif message.role == "tool" or message.role == "ipython" %}
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
{%- if message.content is mapping or message.content is iterable %}
{{- message.content | tojson }}
{%- else %}
{{- message.content }}
{%- endif %}
{{- "<|eot_id|>" }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
{%- endif %}

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"dtype": "float16",
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 64,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 16,
"num_key_value_heads": 8,
"pad_token_id": null,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"factor": 32.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_theta": 500000.0,
"rope_type": "llama3"
},
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"use_cache": true,
"vocab_size": 128256
}

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{
"bos_token_id": 128000,
"do_sample": true,
"eos_token_id": [
128001,
128008,
128009
],
"temperature": 0.6,
"top_p": 0.9,
"transformers_version": "5.0.0"
}

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size 2471645464

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{
"backend": "tokenizers",
"bos_token": "<|begin_of_text|>",
"clean_up_tokenization_spaces": true,
"eos_token": "<|eot_id|>",
"is_local": false,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"pad_token": "<|eot_id|>",
"tokenizer_class": "TokenizersBackend"
}