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Model: SanjiWatsuki/neural-chat-7b-v3-3-wizardmath-dare-me
Source: Original Platform

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LICENSE

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README.md

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+---
+license: other
+license_name: microsoft-research-license
+license_link: LICENSE
+tags:
+- merge
+---
+
+**Update: Yeah, this strategy doesn't work. This ended up really devastating the model's performance.**
+
+This model is an experiment involving mixing DARE TIE merger with a task arithmetic merger to attempt to merge models with less loss.
+
+DARE TIE mergers are [very strong at transferring strengths](https://medium.com/@minh.hoque/paper-explained-language-models-are-super-mario-2ebce6c2cf35) while merging a minimal part of the model. For larger models, 90-99% of delta parameters from SFT models can be dropped while retaining most of the benefits if they are rescaled and consensus merged back into the model.
+
+For 7B models, we can't drop as many of the parameters and retain the model's strengths. In the original paper, the WizardMath model showed transferrable skills when 90% of the parameters were dropped but showed more strength when 70% were dropped. Experimentally, it appears that [even lower drop rates like 40%](https://github.com/cg123/mergekit/issues/26) have performed the best even for larger 34B models. In some instances, [even densities as high as 80% create an unstable merger](https://huggingface.co/jan-hq/supermario-v1), making DARE TIES unsuitable for merging models.
+
+This is an experiment utilizing two merger techniques together to try and transfer skills between finetuned models. If we were to DARE TIE a low density merger onto the base Mistral model and then task arithmetic merge those low density delta weights onto a finetune, could we still achieve skill transfer?
+
+```
+models: # mistral-wizardmath-dare-0.7-density
+  - model: mistralai/Mistral-7B-v0.1
+    # no parameters necessary for base model
+  - model: WizardLM/WizardMath-7B-V1.1
+    parameters:
+      weight: 1
+      density: 0.3
+merge_method: dare_ties
+base_model: mistralai/Mistral-7B-v0.1
+parameters:
+  normalize: true
+  int8_mask: true
+dtype: bfloat16
+
+merge_method: task_arithmetic
+base_model: mistralai/Mistral-7B-v0.1
+models:
+  - model: mistral-wizardmath-dare-0.7-density
+  - model: Intel/neural-chat-7b-v3-3
+parameters:
+  weight: 1.0
+dtype: bfloat16
+```
+
+WizardMath is under the Microsoft Research License, Intel is Apache 2.0.

config.json

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+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

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tokenizer_config.json

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+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}