| --- |
| tags: |
| - merge |
| - mergekit |
| - lazymergekit |
| - yam-peleg/Experiment26-7B |
| - Kukedlc/NeuralSirKrishna-7b |
| - automerger/YamShadow-7B |
| base_model: |
| - yam-peleg/Experiment26-7B |
| - Kukedlc/NeuralSirKrishna-7b |
| - automerger/YamShadow-7B |
| license: apache-2.0 |
| --- |
| |
| # NeuralContamination-7B-ties |
| |
|
|
|  |
|
|
| NeuralContamination-7B-ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
| * [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B) |
| * [Kukedlc/NeuralSirKrishna-7b](https://huggingface.co/Kukedlc/NeuralSirKrishna-7b) |
| * [automerger/YamShadow-7B](https://huggingface.co/automerger/YamShadow-7B) |
| |
| ## 🧩 Configuration |
|
|
| ```yaml |
| models: |
| - model: yam-peleg/Experiment26-7B |
| parameters: |
| density: [1, 0.7, 0.1] # density gradient |
| weight: 1.0 |
| - model: Kukedlc/NeuralSirKrishna-7b |
| parameters: |
| density: 0.5 |
| weight: [0, 0.3, 0.7, 1] # weight gradient |
| - model: automerger/YamShadow-7B |
| parameters: |
| density: 0.33 |
| weight: |
| - filter: mlp |
| value: 0.5 |
| - value: 0 |
| merge_method: ties |
| base_model: liminerity/M7-7b |
| parameters: |
| normalize: true |
| int8_mask: true |
| dtype: bfloat16 |
| |
| ``` |
|
|
| ## 💻 Usage |
|
|
| ```python |
| !pip install -qU transformers accelerate |
| |
| from transformers import AutoTokenizer |
| import transformers |
| import torch |
| |
| model = "Kukedlc/NeuralContamination-7B-ties" |
| 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"]) |
| ``` |
|
|
| ## Genetic |
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|  |
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|
