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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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+ - aaditya/Llama3-OpenBioLLM-8B
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+ - johnsnowlabs/JSL-MedLlama-3-8B-v2.0
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+ - Jayant9928/orpo_med_v3
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+ - skumar9/Llama-medx_v3
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+ base_model:
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+ - aaditya/Llama3-OpenBioLLM-8B
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+ - johnsnowlabs/JSL-MedLlama-3-8B-v2.0
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+ - Jayant9928/orpo_med_v3
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+ - skumar9/Llama-medx_v3
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+ ---
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+
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+ # Llama-3-OpenBioMed-8B-dare-ties-4x
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+
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+ Llama-3-OpenBioMed-8B-dare-ties-4x is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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+ * [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B)
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+ * [johnsnowlabs/JSL-MedLlama-3-8B-v2.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v2.0)
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+ * [Jayant9928/orpo_med_v3](https://huggingface.co/Jayant9928/orpo_med_v3)
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+ * [skumar9/Llama-medx_v3](https://huggingface.co/skumar9/Llama-medx_v3)
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+
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+ ## 🧩 Configuration
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+
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+ ```yaml
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+ models:
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+ - model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
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+ # No parameters necessary for base model
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+ - model: aaditya/Llama3-OpenBioLLM-8B
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+ parameters:
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+ density: 0.53
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+ weight: 0.2
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+ - model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
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+ parameters:
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+ density: 0.53
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+ weight: 0.3
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+ - model: Jayant9928/orpo_med_v3
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+ parameters:
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+ density: 0.53
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+ weight: 0.3
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+ - model: skumar9/Llama-medx_v3
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+ parameters:
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+ density: 0.53
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+ weight: 0.2
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+ merge_method: dare_ties
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+ base_model: meta-llama/Meta-Llama-3-8B-Instruct
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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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+
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+ ## 💻 Usage
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+
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+ ```python
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+ !pip install -qU transformers accelerate
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+
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+ from transformers import AutoTokenizer
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+ import transformers
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+ import torch
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+
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+ model = "abhinand/Llama-3-OpenBioMed-8B-dare-ties-4x"
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+ messages = [{"role": "user", "content": "What is a large language model?"}]
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+
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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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+
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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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+ ```