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--- |
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license: apache-2.0 |
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datasets: |
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- Epiculous/SynthRP-Gens-v1-Filtered-n-Cleaned |
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- Epiculous/Synthstruct-Gens-v1-Filtered-n-Cleaned |
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language: |
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- en |
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- fr |
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- de |
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- es |
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- it |
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- pt |
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- ru |
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- zh |
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- ja |
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pipeline_tag: text-generation |
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--- |
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### exl2 quant (measurement.json in main branch) |
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--- |
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### check revisions for quants (3bpw,4bpw,5bpw,6bpw,8bpw) |
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--- |
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/64adfd277b5ff762771e4571/ijVNJF9HePkQCjejXZLcI.png) |
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Back from the dead! Hoping to make something cool to share with everyone! Introducing Crimson Dawn! Built atop the impressive [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407); Crimson Dawn was built with the idea that AI should not be a boring bland generic assistant, but something that you can connect with on a more personal level. Something that can be interesting in a Roleplay, but useful as an assistant too. |
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## Prompting |
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Crimson Dawn was trained with the Mistral Instruct template, therefore it should be prompted in the same way that you would prompt any other mistral model. |
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``` |
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"[INST] Prompt goes here [/INST]" |
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``` |
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### Current Top Sampler Settings |
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```json |
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{ |
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"temp": 1.25, |
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"temperature_last": true, |
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"top_p": 1, |
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"top_k": -1, |
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"top_a": 0, |
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"tfs": 1, |
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"epsilon_cutoff": 0, |
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"eta_cutoff": 0, |
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"typical_p": 1, |
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"min_p": 0.3, |
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"rep_pen": 1, |
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"rep_pen_range": 0, |
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"rep_pen_decay": 0, |
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"rep_pen_slope": 1, |
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"no_repeat_ngram_size": 0, |
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"penalty_alpha": 0, |
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"num_beams": 1, |
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"length_penalty": 1, |
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"min_length": 0, |
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"encoder_rep_pen": 1, |
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"freq_pen": 0, |
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"presence_pen": 0, |
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"skew": 0, |
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"do_sample": true, |
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"early_stopping": false, |
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"dynatemp": false, |
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"min_temp": 0, |
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"max_temp": 2, |
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"dynatemp_exponent": 1, |
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"smoothing_factor": 0, |
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"smoothing_curve": 1, |
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"dry_allowed_length": 2, |
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"dry_multiplier": 0, |
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"dry_base": 1.75, |
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"dry_sequence_breakers": "[\"\\n\", \":\", \"\\\"\", \"*\"]", |
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"dry_penalty_last_n": 0, |
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"add_bos_token": true, |
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"ban_eos_token": false, |
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"skip_special_tokens": true, |
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"mirostat_mode": 0, |
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"mirostat_tau": 5, |
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"mirostat_eta": 0.1, |
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"guidance_scale": 1, |
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"negative_prompt": "", |
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"grammar_string": "", |
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"json_schema": {}, |
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"banned_tokens": "", |
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"sampler_priority": [ |
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"temperature", |
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"dynamic_temperature", |
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"quadratic_sampling", |
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"top_k", |
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"top_p", |
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"typical_p", |
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"epsilon_cutoff", |
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"eta_cutoff", |
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"tfs", |
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"top_a", |
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"min_p", |
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"mirostat" |
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], |
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"samplers": [ |
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"top_k", |
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"tfs_z", |
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"typical_p", |
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"top_p", |
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"min_p", |
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"temperature" |
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], |
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"ignore_eos_token": false, |
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"spaces_between_special_tokens": true, |
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"speculative_ngram": false, |
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"sampler_order": [ |
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5, |
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6, |
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0, |
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1, |
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2, |
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3, |
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4 |
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], |
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"logit_bias": [], |
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"ignore_eos_token_aphrodite": false, |
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"spaces_between_special_tokens_aphrodite": true, |
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"rep_pen_size": 0, |
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"genamt": 1024, |
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"max_length": 16384 |
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} |
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``` |
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## Training |
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Training was done twice over 2 epochs each on two 2x [NVIDIA A6000 GPUs](https://www.nvidia.com/en-us/design-visualization/rtx-a6000/) using LoRA. A two-phased approach was used in which the base model was trained 2 epochs on RP data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on instruct, and the new instruct LoRA was applied to the modified base, resulting in what you see here. |
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl) |
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## Special Thanks |
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Special thanks to my friends over at Anthracite! Without their help and Kalomaze starting the synthetic data script, none of this would have been possible. |
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Also want to thank my friends in The Chaotic Neutrals for their friendship, support, and guidance. |