--- base_model: - SvalTek/ColdBrew-Aphid - SvalTek/ColdBrew-Aphid - SvalTek/ColdBrew-Aphid - SvalTek/ColdBrew-Aphid - SvalTek/ColdBrew-Aphid - SvalTek/ColdBrew-Aphid tags: - merge - mergekit - lazymergekit - SvalTek/ColdBrew-Aphid --- # ColdBrew-Indium ColdBrew-Indium is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid) * [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid) * [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid) * [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid) * [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid) * [SvalTek/ColdBrew-Aphid](https://huggingface.co./SvalTek/ColdBrew-Aphid) ## 🧩 Configuration ```yaml const_tag: &scale_factor 0.7071067812 # 1/sqrt(2) scaling for stability attenuate-env: &attenuated_env parameters: scale: - filter: q_proj value: *scale_factor - filter: k_proj value: *scale_factor - filter: v_proj value: *scale_factor - filter: o_proj value: *scale_factor - value: 1.0 slices: # Preserve input layers - sources: - model: SvalTek/ColdBrew-Aphid layer_range: [0, 8] # expand upper layers - sources: - model: SvalTek/ColdBrew-Aphid layer_range: [8, 16] - sources: - model: SvalTek/ColdBrew-Aphid layer_range: [8, 16] <<: *attenuated_env # expand upper layers - sources: - model: SvalTek/ColdBrew-Aphid layer_range: [16, 22] - sources: - model: SvalTek/ColdBrew-Aphid layer_range: [16, 22] <<: *attenuated_env # Preserve output layers - sources: - model: SvalTek/ColdBrew-Aphid layer_range: [22, 27] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "SvalTek/ColdBrew-Indium" 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"]) ```