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README.md
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---
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datasets:
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- vicgalle/worldsim-claude-opus
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- macadeliccc/opus_samantha
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-9.5K-ShareGPT
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- lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K
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- QuietImpostor/Sao10K-Claude-3-Opus-Instruct-15K-ShareGPT
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- ChaoticNeutrals/Luminous_Opus
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- kalomaze/Opus_Instruct_3k
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- kalomaze/Opus_Instruct_25k
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language:
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- en
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base_model:
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- codellama/CodeLlama-13b-Instruct-hf
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pipeline_tag: text-generation
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license: llama2
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---
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# CL-13B-Fabula
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CL-13B-Fabula is a fine-tuned version of Facebook's CodeLlama 13B Instruct model, specifically optimized for roleplay and general knowledge tasks while maintaining its chat understanding capabilities.
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This model is basically a bigger version of [L3.1-8B-Fabula](https://hf.co/BusRune/L3.1-8B-Fabula), since I wanted to make something a bit bigger but my VPS storage can't handle a 70B models so 13B model it is.
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## Model Details
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- **Base Model**: [CodeLlama-13b-Instruct-hf](https://hf.co/codellama/CodeLlama-13b-Instruct-hf)
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- **Chat Template**: ChatML
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- **Max Input Tokens**: 16,384
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- **Datasets Used In Fine-tuning:**
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* [vicgalle/worldsim-claude-opus](https://hf.co/datasets/vicgalle/worldsim-claude-opus)
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* [macadeliccc/opus_samantha](https://hf.co/datasets/macadeliccc/opus_samantha)
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* [anthracite-org/kalo-opus-instruct-22k-no-refusal](https://hf.co/datasets/anthracite-org/kalo-opus-instruct-22k-no-refusal)
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* [lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-9.5K-ShareGPT](https://hf.co/datasets/lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-9.5K-ShareGPT)
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* [lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K](https://hf.co/datasets/lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K)
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* [QuietImpostor/Sao10K-Claude-3-Opus-Instruct-15K-ShareGPT](https://hf.co/datasets/QuietImpostor/Sao10K-Claude-3-Opus-Instruct-15K-ShareGPT)
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* [ChaoticNeutrals/Luminous_Opus](https://hf.co/datasets/ChaoticNeutrals/Luminous_Opus)
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* [kalomaze/Opus_Instruct_3k](https://hf.co/datasets/kalomaze/Opus_Instruct_3k)
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* [kalomaze/Opus_Instruct_25k](https://hf.co/datasets/kalomaze/Opus_Instruct_25k)
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## Chat Template
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- In the finetuning ChatML were used.
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```js
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function chatml2(messages) {
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/**
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* @param {Array<{role: string, name: string, content: string}>} messages
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* @returns {{prompt: string, stop: string}}
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* @description Formats messages into ChatML template format
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*/
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const isLastMessageAssistant = messages[messages.length - 1]?.role === "assistant";
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return {
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prompt: messages.map((message, index) => {
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const nameStr = message.name ? ` [${message.name}]` : "";
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const isLast = index === messages.length - 1;
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const needsEndTag = !isLastMessageAssistant || !isLast;
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return `<|im_start|>${message.role.toLowerCase()}${nameStr}\n${message.content}${needsEndTag ? "<|im_end|>" : ""}`;
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}).join("\n") + (isLastMessageAssistant ? "" : "\n<|im_start|>assistant\n"),
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stop: "<|im_end|>"
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};
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}
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```
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I would highly recommend you add rules as assistant role before sending to generation like this below:
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```md
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<rules for="{{char}}'s responses">
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1. I will write a response as {{char}} in a short manner and will keep it detailed (I will try to keep it under 300 characters).
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2. Response formatting:
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"This is for talking"
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*This is for doing an action/ or self-reflection if I decide to write {{char}}'s response in first-person*
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ex: "Hello, there!" *{name} waves,* "How are you doing today?"
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3. When I feel like it is needed for {{user}} to talk, I will not act as {{user}} or for them, I will simply stop generating more text via executing my EOS (end-of-string) token "<|im_end|>", to let the user write their response as {{user}}
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4. I will use my past messages as an example of how {{char}} speaks
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</rules>
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**{{char}}'s response:**
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```
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