--- base_model: - DewEfresh/neo_7b - m-a-p/neo_7b tags: - merge - mergekit - lazymergekit - DewEfresh/neo_7b - m-a-p/neo_7b --- # Neo_7b-merge15 Neo_7b-merge15 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [DewEfresh/neo_7b](https://huggingface.co/DewEfresh/neo_7b) * [m-a-p/neo_7b](https://huggingface.co/m-a-p/neo_7b) ## 🧩 Configuration ```yaml # Define the slices for the model merging process slices: - sources: # First part: merge layer 0 with layer 3 - model: DewEfresh/neo_7b layer_range: [0, 0] - model: m-a-p/neo_7b layer_range: [3, 3] - sources: # Second part: merge layer 1 with layer 3 - model: DewEfresh/neo_7b layer_range: [1, 1] - model: m-a-p/neo_7b layer_range: [3, 3] - sources: # Third part: merge layer 2 with layer 3 - model: DewEfresh/neo_7b layer_range: [2, 2] - model: m-a-p/neo_7b layer_range: [3, 3] - sources: # Fourth part: merge layer 4 with layer 7 - model: DewEfresh/neo_7b layer_range: [4, 4] - model: m-a-p/neo_7b layer_range: [7, 7] - sources: # Fifth part: merge layer 5 with layer 7 - model: DewEfresh/neo_7b layer_range: [5, 5] - model: m-a-p/neo_7b layer_range: [7, 7] - sources: # Sixth part: merge layer 6 with layer 7 - model: DewEfresh/neo_7b layer_range: [6, 6] - model: m-a-p/neo_7b layer_range: [7, 7] - sources: # Seventh part: merge layer 8 with layer 11 - model: DewEfresh/neo_7b layer_range: [8, 8] - model: m-a-p/neo_7b layer_range: [11, 11] - sources: # Eighth part: merge layer 9 with layer 11 - model: DewEfresh/neo_7b layer_range: [9, 9] - model: m-a-p/neo_7b layer_range: [11, 11] - sources: # Ninth part: merge layer 10 with layer 11 - model: DewEfresh/neo_7b layer_range: [10, 10] - model: m-a-p/neo_7b layer_range: [11, 11] - sources: # Tenth part: merge layer 12 with layer 15 - model: DewEfresh/neo_7b layer_range: [12, 12] - model: m-a-p/neo_7b layer_range: [15, 15] - sources: # Eleventh part: merge layer 13 with layer 15 - model: DewEfresh/neo_7b layer_range: [13, 13] - model: m-a-p/neo_7b layer_range: [15, 15] - sources: # Twelfth part: merge layer 14 with layer 15 - model: DewEfresh/neo_7b layer_range: [14, 14] - model: m-a-p/neo_7b layer_range: [15, 15] - sources: # Thirteenth part: merge layer 16 with layer 19 - model: DewEfresh/neo_7b layer_range: [16, 16] - model: m-a-p/neo_7b layer_range: [19, 19] - sources: # Fourteenth part: merge layer 17 with layer 19 - model: DewEfresh/neo_7b layer_range: [17, 17] - model: m-a-p/neo_7b layer_range: [19, 19] - sources: # Fifteenth part: merge layer 18 with layer 19 - model: DewEfresh/neo_7b layer_range: [18, 18] - model: m-a-p/neo_7b layer_range: [19, 19] - sources: # Sixteenth part: merge layer 20 with layer 23 - model: DewEfresh/neo_7b layer_range: [20, 20] - model: m-a-p/neo_7b layer_range: [23, 23] - sources: # Seventeenth part: merge layer 21 with layer 23 - model: DewEfresh/neo_7b layer_range: [21, 21] - model: m-a-p/neo_7b layer_range: [23, 23] - sources: # Eighteenth part: merge layer 22 with layer 23 - model: DewEfresh/neo_7b layer_range: [22, 22] - model: m-a-p/neo_7b layer_range: [23, 23] - sources: # Nineteenth part: merge layer 24 with layer 27 - model: DewEfresh/neo_7b layer_range: [24, 24] - model: m-a-p/neo_7b layer_range: [27, 27] - sources: # Twentieth part: merge layer 25 with layer 27 - model: DewEfresh/neo_7b layer_range: [25, 25] - model: m-a-p/neo_7b layer_range: [27, 27] - sources: # Twenty-first part: merge layer 26 with layer 27 - model: DewEfresh/neo_7b layer_range: [26, 26] - model: m-a-p/neo_7b layer_range: [27, 27] # Specify the merging method for the slices merge_method: slerp base_model: DewEfresh/neo_7b parameters: t: 0.3333 # Set global interpolation value to 33.33% dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DewEfresh/Neo_7b-merge15" 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"]) ```