metadata

tags:
  - merge
  - mergekit
  - lazymergekit
  - aaditya/Llama3-OpenBioLLM-8B
  - johnsnowlabs/JSL-MedLlama-3-8B-v2.0
  - Jayant9928/orpo_med_v3
  - skumar9/Llama-medx_v3
base_model:
  - aaditya/Llama3-OpenBioLLM-8B
  - johnsnowlabs/JSL-MedLlama-3-8B-v2.0
  - Jayant9928/orpo_med_v3
  - skumar9/Llama-medx_v3

Llama-3-OpenBioMed-8B-dare-ties-4x

Llama-3-OpenBioMed-8B-dare-ties-4x is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
    # No parameters necessary for base model
  - model: aaditya/Llama3-OpenBioLLM-8B
    parameters:
      density: 0.53
      weight: 0.2
  - model: johnsnowlabs/JSL-MedLlama-3-8B-v2.0
    parameters:
      density: 0.53
      weight: 0.3
  - model: Jayant9928/orpo_med_v3
    parameters:
      density: 0.53
      weight: 0.3
  - model: skumar9/Llama-medx_v3
    parameters:
        density: 0.53
        weight: 0.2
merge_method: dare_ties
base_model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "abhinand/Llama-3-OpenBioMed-8B-dare-ties-4x"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])