73 lines
1.7 KiB
Markdown
73 lines
1.7 KiB
Markdown
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---
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base_model:
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- BioMistral/BioMistral-7B
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- HuggingFaceH4/zephyr-7b-beta
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tags:
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- mergekit
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- merge
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license: apache-2.0
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---
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# BioMistral-Zephyr-Beta-SLERP
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BioMistral-Zephyr-Beta-SLERP is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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This model was merged using the SLERP merge method.
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### 🤖💬 Models Merged
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The following models were included in the merge:
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* [BioMistral/BioMistral-7B](https://huggingface.co/BioMistral/BioMistral-7B)
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* [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
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### 🧩 Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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slices:
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- sources:
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- model: BioMistral/BioMistral-7B
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layer_range: [0, 32]
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- model: HuggingFaceH4/zephyr-7b-beta
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layer_range: [0, 32]
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merge_method: slerp
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base_model: BioMistral/BioMistral-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 = "Kabster/BioMistral-Zephyr-Beta-SLERP"
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messages = [{"role": "user", "content": "Can bisoprolol cause insomnia?"}]
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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=0.2, top_k=100, top_p=0.95)
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print(outputs[0]["generated_text"])
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```
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