--- base_model: - johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16 - meta-llama/Meta-Llama-3.1-8B-Instruct tags: - merge - mergekit - lazymergekit - johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16 - meta-llama/Meta-Llama-3.1-8B-Instruct --- # johnpaulbin-e2-instruct-merge-fp16 johnpaulbin-e2-instruct-merge-fp16 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16](https://huggingface.co/johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16) * [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) ## 🧩 Configuration ```yaml models: - model: johnpaulbin/llama3.1-8b-e2-epoch3-merged-fp16 parameters: weight: 1 - model: meta-llama/Meta-Llama-3.1-8B-Instruct parameters: weight: 1 merge_method: ties base_model: meta-llama/Meta-Llama-3.1-8B parameters: normalize: true int8_mask: true dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "johnpaulbin/johnpaulbin-e2-instruct-merge-fp16" 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"]) ```