84 lines
2.5 KiB
Markdown
84 lines
2.5 KiB
Markdown
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---
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tags:
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- merge
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- mergekit
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- lazymergekit
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- ChaoticNeutrals/Eris_Remix_7B
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- Virt-io/Erebus-Holodeck-7B
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base_model:
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- ChaoticNeutrals/Eris_Remix_7B
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- Virt-io/Erebus-Holodeck-7B
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license: cc-by-nc-4.0
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---
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# OxytocinErosEngineeringF1-7B-slerp
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OxytocinErosEngineeringF1-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [ChaoticNeutrals/Eris_Remix_7B](https://huggingface.co/ChaoticNeutrals/Eris_Remix_7B)
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* [Virt-io/Erebus-Holodeck-7B](https://huggingface.co/Virt-io/Erebus-Holodeck-7B)
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Thanks to MraderMarcher for providing GGUF quants-> [mradermacher/OxytocinErosEngineeringF1-7B-slerp-GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineeringF1-7B-slerp-GGUF)
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_weezywitasneezy__OxytocinErosEngineeringF1-7B-slerp)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |69.22|
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|AI2 Reasoning Challenge (25-Shot)|67.15|
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|HellaSwag (10-Shot) |86|
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|MMLU (5-Shot) |64.73|
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|TruthfulQA (0-shot) |54.54|
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|Winogrande (5-shot) |81.14|
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|GSM8k (5-shot) |61.79|
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: ChaoticNeutrals/Eris_Remix_7B
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layer_range: [0, 32]
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- model: Virt-io/Erebus-Holodeck-7B
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layer_range: [0, 32]
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merge_method: slerp
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base_model: ChaoticNeutrals/Eris_Remix_7B
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5
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dtype: bfloat16
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```
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## 💻 Usage
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```python
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!pip install -qU transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "weezywitasneezy/OxytocinErosEngineeringF1-7B-slerp"
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messages = [{"role": "user", "content": "What is a large language model?"}]
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tokenizer = AutoTokenizer.from_pretrained(model)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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