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

README.md

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+---
+license: cc-by-nc-2.0
+---
+
+
+# lzlv_70B 
+## A Mythomax/MLewd_13B-style merge of selected 70B models
+
+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.
+
+Did it work? Probably, maybe. It seemed subjectively better than each of the individual models in my tests.
+
+
+
+GGUF 4_K_M + 5_K_M can be found here: https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf/settings
+
+
+## Procedure:
+
+Models used:
+- **NousResearch/Nous-Hermes-Llama2-70b** - A great model for roleplaying, but not the best at following complex instructions.
+- **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.
+- **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. 
+
+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. 
+
+
+The merging process was heavily inspired by Undi95's approach in Undi95/MXLewdMini-L2-13B. To be specific, the ratios are:
+
+Component 1: Merge of Mythospice x Xwin with SLERP gradient [0.25, 0.3, 0.5].
+Component 2: Merge Xwin x Hermes with SLERP gradient [0.4, 0.3, 0.25].
+
+Finally, both Component 1 and Component 2 were merged with SLERP using weight 0.5.
+
+## Peformance
+
+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. 
+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.
+So, is it better? Feels like it to me, subjectively. Is it really better? No clue, test it. 
+
+## Prompt format:
+Vicuna
+USER: [Prompt]
+ASSISTANT: 

config.json

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+{
+  "_name_or_path": "output_a_mythoxwin/",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 8192,
+  "initializer_range": 0.02,
+  "intermediate_size": 28672,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 64,
+  "num_hidden_layers": 80,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.33.3",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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