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--- |
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license: mit |
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tags: |
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- merge |
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- mergekit |
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- lazymergekit |
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- rhysjones/phi-2-orange |
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- cognitivecomputations/dolphin-2_6-phi-2 |
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base_model: |
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- rhysjones/phi-2-orange |
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- cognitivecomputations/dolphin-2_6-phi-2 |
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--- |
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# Phi-2-psy |
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Phi-2-psy is a merge of the following models: |
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* [rhysjones/phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange) |
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* [cognitivecomputations/dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2) |
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## π Evaluation |
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The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite. |
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |
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|----------------------------------------------------------------|------:|------:|---------:|-------:|------:| |
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|[**phi-2-psy**](https://huggingface.co/vince62s/phi-2-psy)| **34.4**| **71.4**| **48.2**| **38.1**| **48.02**| |
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|[phixtral-2x2_8](https://huggingface.co/mlabonne/phixtral-2x2_8)| 34.1| 70.4| 48.8| 37.8| 47.78| |
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|[dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)| 33.1| 69.9| 47.4| 37.2| 46.89| |
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|[phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange)| 33.4| 71.3| 49.9| 37.3| 47.97| |
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|[phi-2](https://huggingface.co/microsoft/phi-2)| 28.0| 70.8| 44.4| 35.2| 44.61| |
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## 𧩠Configuration |
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```yaml |
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slices: |
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- sources: |
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- model: rhysjones/phi-2-orange |
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layer_range: [0, 32] |
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- model: cognitivecomputations/dolphin-2_6-phi-2 |
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layer_range: [0, 32] |
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merge_method: slerp |
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base_model: rhysjones/phi-2-orange |
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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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## π» Usage |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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torch.set_default_device("cuda") |
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model = AutoModelForCausalLM.from_pretrained("vince62s/phi-2-psy", torch_dtype="auto", trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained("vince62s/phi-2-psy", trust_remote_code=True) |
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inputs = tokenizer('''def print_prime(n): |
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""" |
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Print all primes between 1 and n |
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"""''', return_tensors="pt", return_attention_mask=False) |
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outputs = model.generate(**inputs, max_length=200) |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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