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Model: chargoddard/Chronorctypus-Limarobormes-13b
Source: Original Platform

.gitattributes

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README.md

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+---
+tags:
+- llama
+- merge
+---
+Five different instruction-tuned models (which I'm sure are intuitively obvious from the name) merged using the methodology described in [Resolving Interference When Merging Models](https://arxiv.org/abs/2306.01708).
+
+In theory this should retain more of the capabilites of the constituent models than a straight linear merge would. In my testing, it feels quite capable.
+
+Base model used for the merge: [TheBloke/Llama-2-13B-fp16](https://huggingface.co/TheBloke/Llama-2-13B-fp16)
+
+Models merged in:
+* [OpenOrca-Platypus2-13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B)
+* [limarp-13b-merged](https://huggingface.co/Oniichat/limarp-13b-merged)
+* [Nous-Hermes-Llama2-13b](https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b)
+* [chronos-13b-v2](https://huggingface.co/elinas/chronos-13b-v2)
+* [airoboros-l2-13b-gpt4-1.4.1](https://huggingface.co/jondurbin/airoboros-l2-13b-gpt4-1.4.1)
+
+Works quite well with Alpaca-style prompts:
+```
+### Instruction:
+
+...
+
+### Response:
+```
+
+The script I used to perform the merge is available [here](https://github.com/cg123/ties-merge).
+
+The command that produced this model:
+```
+python ties_merge.py TheBloke/Llama-2-13B-fp16 ./Chronorctypus-Limarobormes-13b --merge elinas/chronos-13b-v2 --merge Open-Orca/OpenOrca-Platypus2-13B --merge Oniichat/limarp-13b-merged --merge jondurbin/airoboros-l2-13b-gpt4-1.4.1 --merge NousResearch/Nous-Hermes-Llama2-13b --cuda
+```
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_chargoddard__Chronorctypus-Limarobormes-13b)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 49.88   |
+| ARC (25-shot)         | 59.9          |
+| HellaSwag (10-shot)   | 82.75    |
+| MMLU (5-shot)         | 58.45         |
+| TruthfulQA (0-shot)   | 51.9   |
+| Winogrande (5-shot)   | 74.43   |
+| GSM8K (5-shot)        | 3.87        |
+| DROP (3-shot)         | 17.89         |

config.json

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+{
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+    "LlamaForCausalLM"
+  ],
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+  "hidden_act": "silu",
+  "hidden_size": 5120,
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+  "intermediate_size": 13824,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-05,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.30.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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