L3.1-70B-Animus-V14.0-EXL3

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Important: Chat Template

This model uses the Llama-3.1-Instruct template. Ensure your client is configured correctly to avoid degraded performance.

Human-Readable Format:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{{SYSTEM_PROMPT}}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{USER_MESSAGE}}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ASSISTANT_RESPONSE}}<|eot_id|>

✅ Chat Completion (recommended): Use your inference server's built-in chat template (llama.cpp, TabbyAPI, vllm, etc.). The correct formatting is handled automatically.

Jinja Template:

Click to view Jinja Template

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

Quantized Models

The quantized model files are available for download. Click the buttons below to view the files.

Download GGUF Files →

How to Download

You can download specific model quantizations using the Hugging Face Command Line Interface (CLI). This allows you to select the exact version you need.

1. Install huggingface-hub with CLI support:

pip install -U "huggingface_hub[cli]"

2. Download a specific quant:

Use the command below, replacing the revision with the desired model version from the repository's branches.

hf download Darkhn-Quants-3/L3.1-70B-Animus-V14.0-EXL3 --revision "6.0bpw_H16" --local-dir ./L3.1-70B-Animus-V14.0-EXL3

Character Card & Lore Book

For the best roleplaying experience, it is highly recommended to use the provided character card and lore book. These files help guide the model's persona and provide rich, in-universe context.

Download Files →

Sampler Presets

For a seamless setup in SillyTavern, you can download pre-configured sampler presets. These are tuned to provide an optimal balance between creativity and narrative coherence for this model.

Simply download the .json file below and import it into SillyTavern's sampler presets menu.

Download SillyTavern Presets →

For those that dont use silly tavern, Samplers settings are:

Temperature: 1.0

Min P: 0.02

Roleplay Format Guide

For the best results, use this structured format. This helps the AI clearly distinguish between actions, inner thoughts, and dialogue.

Actions / Descriptions

*He walked across the room and stared out the window.*

Inner Thoughts

*-I wonder what she's thinking.-*

Dialogue

Alex (Curious): "What do you see out there?"

Standard novel-style formatting is also understood, but this structured format is preferred for clarity.

Roleplay Example

Click the button below to view a full, unedited chatlog demonstrating the model's narrative style and character portrayal.

View Chatlog Example →

Model Description

This is Version 14.0, in the Animus series. V14.0 is now built on the powerful Llama-3.1-70B-Instruct architecture.

V14.0's strength comes from a novel dataset designed to teach the model the why behind the lore, not just the what. The training data has been heavily expanded for this version:

• Base Samples Doubled: The previous foundation of in-character study sessions and uncensored roleplays has been doubled in size to deepen contextual understanding.
• 1,000 Instruction Q&A Samples: Additional Wings of Fire-based instruction formatting.
• 1,000 NSFW/BAD Ending Samples: Non-Wings of Fire scenarios added to diversify narrative flexibility and handle darker, complex outcomes.

The result is a model with exceptionally strong prose and a deep grasp of in-universe lore, making for a highly immersive and accurate roleplaying experience.

Note for roleplay, it follows system prompt and first message, meaning if the first assistant message is short, the following messages will be short.

Training Details

V14.0 Training Process

V14.0 was trained using a full fine-tuning process on Llama 3.1 70B architecture.

• Base Model: meta-llama/Llama-3.1-70B-Instruct
• Hardware: 1x NVIDIA B200
• Training Time: 36 hours
• Epochs: 2
• Method: QloRA / Fine-tuning

Training Dataset

The V14.0 dataset has been significantly expanded from previous versions:

• Doubled Base Dataset (14,000 examples): The original foundation of In-Character Q&A and Uncensored Roleplay examples was doubled to reinforce the lore foundation and enhance roleplay quality.
• Instruction Q&A (1,000 examples): Additional Wings of Fire-based instruction Q&A sets.
• NSFW / Bad Endings (1,000 examples): Non-Wings of Fire scenarios specifically targeting mature themes and bad endings to widen the model's range of dramatic narrative capabilities.

All datasets underwent a rigorous cleaning process to remove formatting artifacts resulting in a cleaner and more natural narrative style.

Intended Use & Limitations

• Intended Use: The primary purpose of this model is for creative and roleplaying within the Wings of Fire universe. However, user feedback indicates it is also highly effective for general-purpose roleplaying.
• Limitations & Quirks:
  • Performance on tasks outside of its training domain (general knowledge, coding, etc.) is not guaranteed and will likely be poor.
  • Versatility: While it is a Wings of Fire tuned model, it is very capable of performing normal roleplay with other settings and characters.
  • The model may "hallucinate" or generate plausible but non-canonical information, especially when pushed outside the established "what-if" scenarios.
  • Content: The training data includes mature and darker themes from the Wings of Fire series. The model is capable of generating content reflecting these themes.
  • Safety: This model has not undergone additional safety alignment beyond what was included in its base model. Standard responsible AI practices should be followed.

Acknowledgements

• Credit to Meta for the Llama-3.1 base models.
• Credit to Google for the Gemini Pro model, used in dataset generation.
• Credit to Anthropic for sonnet 4.5, used in dataset generation.
• Credit to Hangzhou DeepSeek Artificial Intelligence for the deepseek model, used in dataset generation.
• Credit to Moonshot AI for the Kimi K2 model, used in dataset generation.