--- base_model: - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - mlabonne/Hermes-3-Llama-3.1-8B-lorablated - Solshine/reflection-llama-3.1-8B - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - Solshine/reflection-llama-3.1-8B - mlabonne/Hermes-3-Llama-3.1-8B-lorablated tags: - merge - mergekit - lazymergekit - Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - mlabonne/Hermes-3-Llama-3.1-8B-lorablated - Solshine/reflection-llama-3.1-8B --- # Bloslain-8B-v0.1 Bloslain-8B-v0.1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder) * [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated) * [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B) * [Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder](https://huggingface.co/Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder) * [Solshine/reflection-llama-3.1-8B](https://huggingface.co/Solshine/reflection-llama-3.1-8B) * [mlabonne/Hermes-3-Llama-3.1-8B-lorablated](https://huggingface.co/mlabonne/Hermes-3-Llama-3.1-8B-lorablated) ## 🧩 Configuration ```yaml slices: - sources: - layer_range: [0, 8] model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - sources: - layer_range: [0, 16] model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated - sources: - layer_range: [4, 20] model: Solshine/reflection-llama-3.1-8B - sources: - layer_range: [8, 24] model: Solshine/Meta-Llama-3.1-8B-Instruct-Python-Coder - sources: - layer_range: [12, 28] model: Solshine/reflection-llama-3.1-8B - sources: - layer_range: [16, 32] model: mlabonne/Hermes-3-Llama-3.1-8B-lorablated merge_method: passthrough dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "BlackBeenie/Bloslain-8B-v0.1" 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"]) ```