| | --- |
| | tags: |
| | - merge |
| | - mergekit |
| | - lazymergekit |
| | - mlabonne/NeuralBeagle14-7B |
| | - FelixChao/WestSeverus-7B-DPO-v2 |
| | - CultriX/MergeTrix-7B-v2 |
| | base_model: |
| | - mlabonne/NeuralBeagle14-7B |
| | - FelixChao/WestSeverus-7B-DPO-v2 |
| | - CultriX/MergeTrix-7B-v2 |
| | license: apache-2.0 |
| | --- |
| | |
| | # EDIT: |
| | Always check my space for the latest benchmark results for my models! |
| | * https://huggingface.co/spaces/CultriX/Yet_Another_LLM_Leaderboard |
| | |
| | # OmniTrixAI |
| | |
| | OmniTrixAI is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
| | * [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) |
| | * [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2) |
| | * [CultriX/MergeTrix-7B-v2](https://huggingface.co/CultriX/MergeTrix-7B-v2) |
| | |
| | ## 🧩 Configuration |
| | |
| | ```yaml |
| | models: |
| | - model: senseable/WestLake-7B-v2 |
| | # No parameters necessary for base model |
| | - model: mlabonne/NeuralBeagle14-7B |
| | parameters: |
| | density: 0.65 |
| | weight: 0.40 |
| | - model: FelixChao/WestSeverus-7B-DPO-v2 |
| | parameters: |
| | density: 0.45 |
| | weight: 0.26 |
| | - model: CultriX/MergeTrix-7B-v2 |
| | parameters: |
| | density: 0.55 |
| | weight: 0.34 |
| | merge_method: dare_ties |
| | base_model: senseable/WestLake-7B-v2 |
| | parameters: |
| | int8_mask: true |
| | dtype: float16 |
| | ``` |
| | |
| | ## 💻 Usage |
| | |
| | ```python |
| | !pip install -qU transformers accelerate |
| | |
| | from transformers import AutoTokenizer |
| | import transformers |
| | import torch |
| | |
| | model = "CultriX/OmniTrixAI" |
| | 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"]) |
| | ``` |