--- base_model: - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b - Replete-AI/WizardLM-2-7b tags: - merge - mergekit - lazymergekit - Replete-AI/WizardLM-2-7b --- # BiggerWizardLM-2-7B-Extended BiggerWizardLM-2-7B-Extended is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) * [Replete-AI/WizardLM-2-7b](https://huggingface.co/Replete-AI/WizardLM-2-7b) ## 🧩 Configuration ```yaml slices: - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 0 - 4 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 3 - 4 parameters: scale: - filter: o_proj value: 0 - filter: down_proj value: 0 - value: 1 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 4 - 8 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 7 - 8 parameters: scale: - filter: o_proj value: 0 - filter: down_proj value: 0 - value: 1 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 8 - 12 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 11 - 12 parameters: scale: - filter: o_proj value: 0 - filter: down_proj value: 0 - value: 1 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 12 - 16 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 15 - 16 parameters: scale: - filter: o_proj value: 0 - filter: down_proj value: 0 - value: 1 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 16 - 20 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 19 - 20 parameters: scale: - filter: o_proj value: 0 - filter: down_proj value: 0 - value: 1 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 20 - 24 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 23 - 24 parameters: scale: - filter: o_proj value: 0 - filter: down_proj value: 0 - value: 1 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 24 - 28 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 27 - 28 parameters: scale: - filter: o_proj value: 0 - filter: down_proj value: 0 - value: 1 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 28 - 32 - sources: - model: Replete-AI/WizardLM-2-7b layer_range: - 31 - 32 parameters: scale: - filter: o_proj value: 0 - filter: down_proj value: 0 - value: 1 merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Gille/BiggerWizardLM-2-7B-Extended" 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"]) ```