LoneStriker
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Browse files- .gitattributes +1 -0
- README.md +42 -0
- Snow_Lotus_Logo.png +3 -0
- config.json +28 -0
- mergekit_config.yml +7 -0
- model.safetensors.index.json +1 -0
- output-00001-of-00002.safetensors +3 -0
- output-00002-of-00002.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +0 -0
- tokenizer.model +3 -0
- tokenizer_config.json +44 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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Snow_Lotus_Logo.png filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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tags:
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- Roleplay
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- Solar
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- Mistral
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- Text Generation
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---
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![SnowLotus Logo](https://cdn-uploads.huggingface.co/production/uploads/64bb1109aaccfd28b023bcec/gTQtPK46laLIFg0RTAv73.png)
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### Premise
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So this is a basic slerp merge between a smart model and a good prose model. Prose and smarts. What we all want in an uncensored RP model right? I feel like Solar has untapped potential, in any case.
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Sao10K's Frostwind finetune is a key component of the mixture, its smarts are impressive. NyxKrage's Frostmaid experiment, which merges Frostwind with a frankenmerge of Noromaid and a mystery medical model, delivers quite impressive prose. His model creatively incorporates long-range context and instructions too, despite being slightly incoherent due to the fraken merging.
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So those are the main ingredients. Thanks to Nyx for sorting out the pytorch files btw.
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### Recipe
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So, the recipe. I basically just gradient SLERP'd Frostwind into Frostmaid with these params:
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- filter: self_attn
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value: [0.9, 0.6, 0.3, 0, 0]
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- filter: mlp
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value: [0.3, 0.6]
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- value: 0.5 # fallback for rest of tensors
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### Tentative Dozen or So Test Conclusion
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This made a model that was actually pretty much everything I was looking for - NEARLY as smart as Frostwind but with MOST of Frostmaids punchy prose. I tried doing TIES merges and DARE ties merges, but they actually came out worse, because both models have major weaknesses - one is very dry and gpt-ish, the other is a little loose with what's going on. The ties merges tended to bring out those qualities, dare even worse. So I stuck with this. It's not AS smart as Frostwind, so you maybe have to regen a little, but it's pretty smart, and quite creative. A sweet spot hopefully. Maybe someone merge wiser than I can do more with this recipe, but I'm very pleased with it, it did what I was hoping for - a smaller model I can mobile dgpu and produces pretty outsized quality responses (it's fairly zealous tho, be warned). I've only played with it a TINY bit, so there may be qualities or flaws I've missed.
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Cheers to all the finetuners, mergers and developers without which open source models wouldn't be half of what they are.
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Resources used:
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https://huggingface.co/NyxKrage/FrostMaid-10.7B-TESTING-pt
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https://huggingface.co/Sao10K/Frostwind-10.7B-v1
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https://github.com/cg123/mergekit/tree/main
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Snow_Lotus_Logo.png
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Git LFS Details
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config.json
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{
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"_name_or_path": "./FrostNymph",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 4096,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 48,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.36.2",
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"use_cache": false,
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"vocab_size": 32000
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}
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mergekit_config.yml
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models:
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- model: ./FrostNymph
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merge_method: passthrough
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parameters:
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normalize: true
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int8_mask: true
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dtype: float16
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model.safetensors.index.json
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