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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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- microsoft/codebert-base |
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- EleutherAI/gpt-neo-x-20b |
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- openai/codex |
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- bigscience/bloom |
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- google/jurassic-1-jumbo |
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- google/t5-v1_1-large |
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- facebook/bart-large |
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base_model: |
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- microsoft/codebert-base |
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- EleutherAI/gpt-neo-x-20b |
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- openai/codex |
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- bigscience/bloom |
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- google/jurassic-1-jumbo |
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- google/t5-v1_1-large |
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- facebook/bart-large |
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--- |
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# code-slerp |
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code-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): |
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* [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) |
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* [EleutherAI/gpt-neo-x-20b](https://huggingface.co/EleutherAI/gpt-neo-x-20b) |
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* [openai/codex](https://huggingface.co/openai/codex) |
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* [bigscience/bloom](https://huggingface.co/bigscience/bloom) |
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* [google/jurassic-1-jumbo](https://huggingface.co/google/jurassic-1-jumbo) |
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* [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) |
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* [facebook/bart-large](https://huggingface.co/facebook/bart-large) |
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## 🧩 Configuration |
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```yaml |
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slices: |
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- sources: |
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- model: microsoft/codebert-base |
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layer_range: [0, 32] |
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- model: EleutherAI/gpt-neo-x-20b |
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layer_range: [0, 32] |
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- model: openai/codex |
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layer_range: [0, 32] |
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- model: bigscience/bloom |
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layer_range: [0, 32] |
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- model: google/jurassic-1-jumbo |
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layer_range: [0, 32] |
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- model: google/t5-v1_1-large |
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layer_range: [0, 32] |
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- model: facebook/bart-large |
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layer_range: [0, 32] |
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merge_method: slerp |
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base_model: microsoft/codebert-base |
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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: bfloat1 |
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``` |
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## 💻 Usage |
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```python |
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!pip install -qU transformers accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "Or4cl3-1/code-slerp" |
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messages = [{"role": "user", "content": "What is a large language model?"}] |
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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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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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``` |
