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README.md ADDED
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+ ---
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+ tags:
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+ - merge
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+ - mergekit
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+ - lazymergekit
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+ - OmnicromsBrain/StoryFusion-7B
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+ - jdqwoi/TooManyMixRolePlay-7B
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+ base_model:
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+ - OmnicromsBrain/StoryFusion-7B
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+ - jdqwoi/TooManyMixRolePlay-7B
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+ ---
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+
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+
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+ # EXL2 quants of [jdqwoi/TooManyMixRolePlay-7B-Story](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B-Story)
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+
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+ [4.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story-4.0bpw-exl2)
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+ [5.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story-5.0bpw-exl2)
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+ [6.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story-6.0bpw-exl2)
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+ [7.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story-7.0bpw-exl2)
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+ [8.00 bits per weight](https://huggingface.co/kim512/TooManyMixRolePlay-7B-Story-8.0bpw-exl2)
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+
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+ # TooManyMixRolePlay-7B-Story
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+
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+ TooManyMixRolePlay-7B-Story is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
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+ * [OmnicromsBrain/StoryFusion-7B](https://huggingface.co/OmnicromsBrain/StoryFusion-7B)
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+ * [jdqwoi/TooManyMixRolePlay-7B](https://huggingface.co/jdqwoi/TooManyMixRolePlay-7B)
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+
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+ ## 🧩 Configuration
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+
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+ ```yaml
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+ slices:
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+ - sources:
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+ - model: OmnicromsBrain/StoryFusion-7B
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+ layer_range: [0, 32]
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+ - model: jdqwoi/TooManyMixRolePlay-7B
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+ layer_range: [0, 32]
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+ merge_method: slerp
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+ base_model: OmnicromsBrain/StoryFusion-7B
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+ parameters:
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+ t:
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+ - filter: self_attn
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+ value: [0, 0.5, 0.3, 0.7, 1]
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+ - filter: mlp
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+ value: [1, 0.5, 0.7, 0.3, 0]
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+ - value: 0.5
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+ dtype: bfloat16
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+ ```
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+
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+ ## 💻 Usage
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+
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+ ```python
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+ !pip install -qU transformers accelerate
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+
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+ from transformers import AutoTokenizer
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+ import transformers
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+ import torch
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+
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+ model = "jdqwoi/TooManyMixRolePlay-7B-Story"
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+ messages = [{"role": "user", "content": "What is a large language model?"}]
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model)
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+ prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ pipeline = transformers.pipeline(
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+ "text-generation",
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+ model=model,
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+ torch_dtype=torch.float16,
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+ device_map="auto",
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+ )
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+
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+ outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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+ print(outputs[0]["generated_text"])
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+ ```
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