--- license: mit tags: - merge - mergekit - lazymergekit - rhysjones/phi-2-orange - cognitivecomputations/dolphin-2_6-phi-2 base_model: - rhysjones/phi-2-orange - cognitivecomputations/dolphin-2_6-phi-2 --- # Phi-2-psy Phi-2-psy is a merge of the following models: * [rhysjones/phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange) * [cognitivecomputations/dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2) ## 🏆 Evaluation The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite. | Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |----------------------------------------------------------------|------:|------:|---------:|-------:|------:| |[**phi-2-psy**](https://huggingface.co/vince62s/phi-2-psy)| **34.4**| **71.4**| **48.2**| **38.1**| **48.02**| |[phixtral-2x2_8](https://huggingface.co/mlabonne/phixtral-2x2_8)| 34.1| 70.4| 48.8| 37.8| 47.78| |[dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)| 33.1| 69.9| 47.4| 37.2| 46.89| |[phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange)| 33.4| 71.3| 49.9| 37.3| 47.97| |[phi-2](https://huggingface.co/microsoft/phi-2)| 28.0| 70.8| 44.4| 35.2| 44.61| ## 🧩 Configuration ```yaml slices: - sources: - model: rhysjones/phi-2-orange layer_range: [0, 32] - model: cognitivecomputations/dolphin-2_6-phi-2 layer_range: [0, 32] merge_method: slerp base_model: rhysjones/phi-2-orange parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer torch.set_default_device("cuda") model = AutoModelForCausalLM.from_pretrained("vince62s/phi-2-psy", torch_dtype="auto", trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained("vince62s/phi-2-psy", trust_remote_code=True) inputs = tokenizer('''def print_prime(n): """ Print all primes between 1 and n """''', return_tensors="pt", return_attention_mask=False) outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ```