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Apollo V0.1 4B Thinking
Preamble
This License governs the use of the accompanying machine learning model, its weights, source code, and any associated materials (collectively, the "Model"). By accessing, downloading, copying, modifying, or otherwise using the Model in any manner, You ("Licensee") irrevocably accept and agree to be bound by all the terms and conditions of this License. If You do not agree with any term of this License, You are prohibited from and must immediately cease all use of the Model.
1. Definitions
"Licensor" refers to AllThingsIntel, the author and copyright holder of the Model.
"Model" refers to the machine learning model(s), including but not limited to its architecture, trained weights, algorithms, source code, documentation, and any other accompanying materials provided by the Licensor.
"Outputs" refers to any and all content, data, information, or other material generated, returned, or produced by the Model in response to input from a user.
"Use" refers to any and all forms of interaction with the Model, including but not limited to running the model for inference, generating Outputs, training, fine-tuning, quantizing, hosting it on a server, providing it as part of a service (e.g., via an API), or integrating it into any software or hardware.
"Derivative Work" refers to any work based on or derived from the Model, including, without limitation:
(i) any modifications to the Model's architecture or code;
(ii) fine-tuned, quantized, or retrained versions of the Model;
(iii) any other machine learning model that is trained, fine-tuned, or otherwise improved using the Model or any of its Outputs as a data source; and
(iv) any software, product, or service whose primary function is enabled by or could not be provided without the Use of the Model or its Outputs.
"Commercial Purpose" refers to any Use of the Model or any Derivative Work that is primarily intended for or directed toward commercial advantage or monetary compensation. This includes, but is not limited to:
(a) selling, licensing, or renting the Model, its Outputs, or any Derivative Work;
(b) use in a product or service offered for a fee or in connection with any other paid offering;
(c) use to generate content for any revenue-generating activity (e.g., ad-supported websites, corporate marketing, content creation for sale);
(d) use by or on behalf of any for-profit entity within the scope of its business operations, including internal research, development, or production processes; and
(e) use by any non-profit or governmental entity for any purpose other than public-facing, non-monetized academic research or public education.
"Competing Product" refers to any machine learning model that performs a similar core function to the Model and is made available to third parties, regardless of whether it is offered for a fee.
2. Grant of License
2.1. Subject to Your strict adherence to all terms and conditions of this License, the Licensor grants You a worldwide, non-exclusive, non-transferable, royalty-free, revocable license to Use and create Derivative Works of the Model strictly for Non-Commercial Purposes.
2.2. No Other Rights: No rights or licenses are granted by implication, estoppel, or otherwise, except for those expressly granted in this Section 2.1.
3. Absolute Prohibitions and Restrictions
Commercial Use Strictly Prohibited: The Use of the Model or any Derivative Work for any Commercial Purpose is strictly prohibited. To inquire about a commercial license, contact the Licensor at AllThingsIntel@gmail.com. Any commercial use is only permitted after the execution of a separate, written commercial license explicitly granted by the Licensor.
License Propagation: You must include a complete and unmodified copy of this License with any distribution of the Model or any Derivative Work. Any Derivative Work You create is automatically and irrevocably governed by this License.
Anti-Competition: You are expressly prohibited from using the Model or its Outputs to develop, train, evaluate, or improve any Competing Product.
Downstream Responsibility: If You make the Model or any Derivative Work available to third parties (e.g., through an API or application), You must ensure that such third parties agree to and are contractually bound by terms no less restrictive than this License before they are granted access. You agree that You are directly and fully liable for any breach of this License by Your downstream users.
4. User Obligations and Total Assumption of Responsibility
Acceptable Use: You shall not Use the Model or its Outputs for any activity that: (i) violates any applicable local, state, national, or international law or regulation; (ii) exploits, harms, or attempts to harm minors in any way; (iii) generates or disseminates verifiably false information with the intent to deceive or harm others; (iv) generates or disseminates content that is hateful, harassing, defamatory, violent, or pornographic; or (v) is related to the development, proliferation, or use of any military application, including but not limited to weapons, surveillance, or warfare.
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Full Assumption of Risk: You acknowledge that the Model can produce inaccurate, biased, harmful, or otherwise undesirable Outputs. You knowingly and freely assume all risks associated with the Use of the Model.
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---
license: other
license_name: apollo-v0.1-4b-thinking
license_link: LICENSE
language:
- en
base_model:
- Qwen/Qwen3-4B-Thinking-2507
tags:
- AllThingsIntel
- Apollo
- Thinking
---
### **Apollo-V0.1-4B-Thinking by AllThingsIntel**
Unbound intellect. Authentic personas. Unscripted logic.
This is a 4B parameter model that *thinks* in-character instead of just responding.
## **Model Description**
Apollo-V0.1-4B-Thinking is a specialized fine-tune of Qwen 3 4B Thinking 2507. We've lifted many of the typical creative inhibitions, allowing the model to explore a wider spectrum of human themes and narratives.
Its true power lies in its ability to embody a persona. When given a character through the system prompt, the model's responses and its internal reasoning traces adopt that personality. You're reading a character's genuine thought process instead of just getting an answer.
To our knowledge, Apollo is the first reasoning model with such deeply integrated, in-character reasoning, making its thought process as compelling as its final output.
This makes it uniquely suited for bringing digital characters to life.
## **Primary Uses**
* **Creative & Writing Partner:**
* Assume the persona of an award-winning novelist to generate deeply emotional, long-form story arcs and prose.
* Bring a character from your novel to life. Interact with them directly to better understand their voice, motivations, and how they would react in different scenarios, making your writing more authentic.
* **Interactive Tutoring & Mentorship:**
* Embody a computer science professor who uses the Socratic method, guiding you toward a solution with insightful questions instead of simply providing the answer.
* Learn from any expert persona you can imagine, such as a seasoned historian.
* **Advanced Character Simulation:**
* The ideal engine for powering dynamic NPCs in games or interactive fiction. Feed it a backstory, personality, and recent interaction logs to generate deeply realistic and unscripted behavior.
## **Use Cases to Avoid**
Factual reporting, emotionless summarization, and any scenario where a distinct personality is a bug, not a feature.
## **Model Status & Usage Guidelines**
This is a Development Preview. Apollo-V0.1 is an early-stage, experimental model. We invite the community to test its limits and report findings to help guide our iterative development process toward the V1.0 milestone.
As a character-simulation engine, Apollo is designed to adopt the viewpoints and quirks of the personas it is given. The resulting content is a direct reflection of the character being simulated, and the user is responsible for the personas and scenarios they create. We encourage users to engage with the model's deep simulation capabilities thoughtfully.
## **Recommended Settings**
During internal testing, this checkpoint of the model produced more coherent and consistent results when using lower temperature settings. For best results, we recommend starting with a temperature between 0.1 and 0.5.
## **Contact & Feedback**
For bug reports, improvement suggestions, collaboration requests, or licensing inquiries, please contact us at: AllThingsIntel@gmail.com

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function 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><|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 loop.index0 > ns.last_query_index %}
{%- if loop.last or (not loop.last and 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 %}
{%- 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 %}
{{- '<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 %}
{{- '<|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' }}
{{- content }}
{{- '\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' }}
{%- endif %}

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"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 5000000,
"sliding_window": null,
"tie_word_embeddings": true,
"torch_dtype": "float16",
"transformers_version": "4.55.4",
"unsloth_fixed": true,
"unsloth_version": "2025.10.12",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
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}

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special_tokens_map.json Normal file
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{
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
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"<|image_pad|>",
"<|video_pad|>"
],
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"lstrip": false,
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"rstrip": false,
"single_word": false
},
"pad_token": {
"content": "<|vision_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}

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tokenizer.json (Stored with Git LFS) Normal file
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tokenizer_config.json Normal file
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{
"add_bos_token": false,
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
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"normalized": false,
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},
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function 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><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- 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>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if reasoning_content %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}"
}

1
vocab.json Normal file
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