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ExLLaMA V2 quant of lzlv_70b_fp16_hf-4.65bpw-h6-exl2

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README.md ADDED
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+ ---
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+ license: cc-by-nc-2.0
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+ ---
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+
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+
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+ # lzlv_70B
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+ ## A Mythomax/MLewd_13B-style merge of selected 70B models
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+
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+ A multi-model merge of several LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience.
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+
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+ Did it work? Probably, maybe. It seemed subjectively better than each of the individual models in my tests.
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+
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+
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+
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+ GGUF 4_K_M + 5_K_M can be found here: https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf/settings
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+
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+
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+ ## Procedure:
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+
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+ Models used:
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+ - **NousResearch/Nous-Hermes-Llama2-70b** - A great model for roleplaying, but not the best at following complex instructions.
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+ - **Xwin-LM/Xwin-LM-7B-V0.1** - Excellent at following instructions and quite creative out of the box, so it seemed like the best available model to act as the base for the merge.
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+ - **Doctor-Shotgun/Mythospice-70b** - The wildcard of the three. I was looking for a creative, NSFW-oriented model and came across this while digging through hf. I hadn't heard of it before and apparently no one had bothered to release a quantized version of this model. So I downloaded it and did it myself to test it. It turned out to be more or less what I was looking for as my third component, so I used it here.
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+
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+ A big thank you to the creators of the models above. If you look up Mythospice, you will notice that it also includes Nous-Hermes so it's technically present twice in this mix. This is apparently common practice amongst the cool kids who do 13B models so I don't think this hurts the model.
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+
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+
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+ The merging process was heavily inspired by Undi95's approach in Undi95/MXLewdMini-L2-13B. To be specific, the ratios are:
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+
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+ Component 1: Merge of Mythospice x Xwin with SLERP gradient [0.25, 0.3, 0.5].
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+ Component 2: Merge Xwin x Hermes with SLERP gradient [0.4, 0.3, 0.25].
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+
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+ Finally, both Component 1 and Component 2 were merged with SLERP using weight 0.5.
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+
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+ ## Peformance
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+
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+ I tested this model for a few days before publishing it. It seems to more or less retain the instruction-following capabilities of Xwin-70B, while seeming to have adopted a lot of the creativity of the other two models.
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+ It handled my more complex scenarios that creative models otherwise tend to struggle with quite well. At the same time, its outputs felt more creative and possibly a bit more nsfw-inclined than Xwin-70b.
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+ So, is it better? Feels like it to me, subjectively. Is it really better? No clue, test it.
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+
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+ ## Prompt format:
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+ Vicuna
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+ USER: [Prompt]
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+ ASSISTANT:
config.json ADDED
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+ {
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+ "_name_or_path": "output_a_mythoxwin/",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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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": 8192,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 28672,
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+ "max_position_embeddings": 4096,
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+ "model_type": "llama",
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+ "num_attention_heads": 64,
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+ "num_hidden_layers": 80,
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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": "4.33.3",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
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