68 lines
1.9 KiB
Markdown
68 lines
1.9 KiB
Markdown
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---
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license: apache-2.0
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tags:
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- merge
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- mergekit
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- lazymergekit
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- argilla/distilabeled-Marcoro14-7B-slerp
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- mlabonne/NeuralMarcoro14-7B
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datasets:
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- mlabonne/chatml_dpo_pairs
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- argilla/distilabel-intel-orca-dpo-pairs
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---
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# NeuDist-Ro-7B
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NeuDist-Ro-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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* [argilla/distilabeled-Marcoro14-7B-slerp](https://huggingface.co/argilla/distilabeled-Marcoro14-7B-slerp)
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* [mlabonne/NeuralMarcoro14-7B](https://huggingface.co/mlabonne/NeuralMarcoro14-7B)
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As an experiment to find the best base merge to further fine-tuning, expect a lot of experiments named using parts of the component models until a clear winner emerges in the benchmarks
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In this case merging 2 DPOs of the same model
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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: argilla/distilabeled-Marcoro14-7B-slerp
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layer_range: [0, 32]
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- model: mlabonne/NeuralMarcoro14-7B
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layer_range: [0, 32]
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merge_method: slerp
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base_model: mlabonne/NeuralMarcoro14-7B
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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 = "flemmingmiguel/NeuDist-Ro-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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```
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