--- base_model: - m-a-p/neo_7b - m-a-p/neo_7b tags: - merge - mergekit - lazymergekit - m-a-p/neo_7b --- # neo_7b-slerp neo_7b-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [m-a-p/neo_7b](https://huggingface.co/m-a-p/neo_7b) * [m-a-p/neo_7b](https://huggingface.co/m-a-p/neo_7b) ## 🧩 Configuration ```yaml slices: - sources: - model: m-a-p/neo_7b layer_range: [0, 1] - model: m-a-p/neo_7b layer_range: [1, 2] - sources: - model: m-a-p/neo_7b layer_range: [2, 3] - model: m-a-p/neo_7b layer_range: [3, 4] - sources: - model: m-a-p/neo_7b layer_range: [4, 5] - model: m-a-p/neo_7b layer_range: [5,6] - sources: - model: m-a-p/neo_7b layer_range: [6, 7] - model: m-a-p/neo_7b layer_range: [7, 8] - sources: - model: m-a-p/neo_7b layer_range: [8, 9] - model: m-a-p/neo_7b layer_range: [9, 10] - sources: - model: m-a-p/neo_7b layer_range: [10, 11] - model: m-a-p/neo_7b layer_range: [11, 12] - sources: - model: m-a-p/neo_7b layer_range: [12, 13] - model: m-a-p/neo_7b layer_range: [13, 14] - sources: - model: m-a-p/neo_7b layer_range: [14, 15] - model: m-a-p/neo_7b layer_range: [15, 16] - sources: - model: m-a-p/neo_7b layer_range: [16, 17] - model: m-a-p/neo_7b layer_range: [17, 18] - sources: - model: m-a-p/neo_7b layer_range: [18, 19] - model: m-a-p/neo_7b layer_range: [19, 20] - sources: - model: m-a-p/neo_7b layer_range: [20, 21] - model: m-a-p/neo_7b layer_range: [21, 22] - sources: - model: m-a-p/neo_7b layer_range: [22, 23] - model: m-a-p/neo_7b layer_range: [23, 24] - sources: - model: m-a-p/neo_7b layer_range: [24, 25] - model: m-a-p/neo_7b layer_range: [25, 26] - sources: - model: m-a-p/neo_7b layer_range: [26, 27] - model: m-a-p/neo_7b layer_range: [27, 28] merge_method: slerp base_model: m-a-p/neo_7b parameters: t: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DewEfresh/neo_7b-slerp" 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"]) ```