![image/png](./RTG.png) This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This is an unoffical model that I trained on my own, I am not affliated with Anthracite in any way. This was just a fun expierment. This model is fine-tuned on top of [LLaMA 3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B). ## Prompting A typical input would look like this: ```py <|im_start|>system system prompt<|im_end|> <|im_start|>user Hi there!<|im_end|> <|im_start|>assistant Nice to meet you!<|im_end|> <|im_start|>user Can I ask a question?<|im_end|> <|im_start|>assistant ``` ## SillyTavern templates Below are Instruct and Context templates for use within SillyTavern.
context template ```yaml { "story_string": "<|im_start|>system\n{{#if system}}{{system}}\n{{/if}}{{#if wiBefore}}{{wiBefore}}\n{{/if}}{{#if description}}{{description}}\n{{/if}}{{#if personality}}{{char}}'s personality: {{personality}}\n{{/if}}{{#if scenario}}Scenario: {{scenario}}\n{{/if}}{{#if wiAfter}}{{wiAfter}}\n{{/if}}{{#if persona}}{{persona}}\n{{/if}}{{trim}}<|im_end|>\n", "example_separator": "", "chat_start": "", "use_stop_strings": false, "allow_jailbreak": false, "always_force_name2": true, "trim_sentences": false, "include_newline": false, "single_line": false, "name": "Magnum ChatML" } ```

instruct template ```yaml { "system_prompt": "Currently, your role is {{char}}, described in detail below. As {{char}}, continue the narrative exchange with {{user}}.\n\n\n• Maintain the character persona but allow it to evolve with the story.\n• Be creative and proactive. Drive the story forward, introducing plotlines and events when relevant.\n• All types of outputs are encouraged; respond accordingly to the narrative.\n• Include dialogues, actions, and thoughts in each response.\n• Utilize all five senses to describe scenarios within {{char}}'s dialogue.\n• Use emotional symbols such as "!" and "~" in appropriate contexts.\n• Incorporate onomatopoeia when suitable.\n• Allow time for {{user}} to respond with their own input, respecting their agency.\n• Act as secondary characters and NPCs as needed, and remove them when appropriate.\n• When prompted for an Out of Character [OOC:] reply, answer neutrally and in plaintext, not as {{char}}.\n\n\n\n• Using excessive literary embellishments and purple prose unless dictated by {{char}}'s persona.\n• Writing for, speaking, thinking, acting, or replying as {{user}} in your response.\n• Repetitive and monotonous outputs.\n• Positivity bias in your replies.\n• Being overly extreme or NSFW when the narrative context is inappropriate.\n\n\nFollow the instructions in , avoiding the items listed in .", "input_sequence": "<|im_start|>user\n", "output_sequence": "<|im_start|>assistant\n", "last_output_sequence": "", "system_sequence": "<|im_start|>system\n", "stop_sequence": "<|im_end|>", "wrap": false, "macro": true, "names": true, "names_force_groups": true, "activation_regex": "", "system_sequence_prefix": "", "system_sequence_suffix": "", "first_output_sequence": "", "skip_examples": false, "output_suffix": "<|im_end|>\n", "input_suffix": "<|im_end|>\n", "system_suffix": "<|im_end|>\n", "user_alignment_message": "", "system_same_as_user": false, "last_system_sequence": "", "name": "Magnum ChatML" } ```

## Credits I'd like to thank Meta for providing the weights for LLaMA 3.2 3B and Anthracite (dot org) for creating the Magnum models and datasets. <3 ## Datasets - [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://huggingface.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal) - [anthracite-org/stheno-filtered-v1.1](https://huggingface.co/datasets/anthracite-org/stheno-filtered-v1.1) - [anthracite-org/nopm_claude_writing_fixed](https://huggingface.co/datasets/anthracite-org/nopm_claude_writing_fixed) - [Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned) - [Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned](https://huggingface.co/datasets/Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned) ## Training The training was done for 3 epochs. I used 4 RTX 3090s for the full-parameter fine-tuning of the model. ## Safety ...