85 lines
2.4 KiB
Markdown
85 lines
2.4 KiB
Markdown
---
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tags:
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- merge
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- mergekit
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- lazymergekit
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- yam-peleg/Experiment21-7B
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- CultriX/NeuralTrix-bf16
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- louisgrc/Montebello_7B_SLERP
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- CorticalStack/pastiche-crown-clown-7b-dare-dpo
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- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
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base_model:
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- yam-peleg/Experiment21-7B
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- CultriX/NeuralTrix-bf16
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- louisgrc/Montebello_7B_SLERP
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- CorticalStack/pastiche-crown-clown-7b-dare-dpo
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- chihoonlee10/T3Q-Mistral-Orca-Math-DPO
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license: apache-2.0
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---
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# Neural-4-QA-7b
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Neural-4-QA-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [yam-peleg/Experiment21-7B](https://huggingface.co/yam-peleg/Experiment21-7B)
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* [CultriX/NeuralTrix-bf16](https://huggingface.co/CultriX/NeuralTrix-bf16)
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* [louisgrc/Montebello_7B_SLERP](https://huggingface.co/louisgrc/Montebello_7B_SLERP)
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* [CorticalStack/pastiche-crown-clown-7b-dare-dpo](https://huggingface.co/CorticalStack/pastiche-crown-clown-7b-dare-dpo)
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* [chihoonlee10/T3Q-Mistral-Orca-Math-DPO](https://huggingface.co/chihoonlee10/T3Q-Mistral-Orca-Math-DPO)
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## 🧩 Configuration
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```yaml
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models:
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- model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
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# No parameters necessary for base model
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- model: yam-peleg/Experiment21-7B
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parameters:
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density: 0.66
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weight: 0.2
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- model: CultriX/NeuralTrix-bf16
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parameters:
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density: 0.55
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weight: 0.2
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- model: louisgrc/Montebello_7B_SLERP
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parameters:
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density: 0.55
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weight: 0.2
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- model: CorticalStack/pastiche-crown-clown-7b-dare-dpo
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parameters:
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density: 0.44
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weight: 0.2
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- model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
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parameters:
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density: 0.66
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weight: 0.2
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merge_method: dare_ties
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base_model: chihoonlee10/T3Q-Mistral-Orca-Math-DPO
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parameters:
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int8_mask: true
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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 = "Kukedlc/Neural-4-QA-7b"
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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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``` |