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vince62s/phi-2-psy
phi-2-psy is a text generation model from vince62s. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Phi-2-psy is a merge of the following models: rhysjones/phi-2-orange cognitivecomputations/dolphin-26-phi-2
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From the Hugging Face model README
Phi-2-psy is a merge of the following models:
The evaluation was performed using LLM AutoEval on Nous suite.
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |
|---|---|---|---|---|---|
| phi-2-psy | 34.4 | 71.4 | 48.2 | 38.1 | 48.02 |
| phixtral-2x2_8 | 34.1 | 70.4 | 48.8 | 37.8 | 47.78 |
| dolphin-2_6-phi-2 | 33.1 | 69.9 | 47.4 | 37.2 | 46.89 |
| phi-2-orange | 33.4 | 71.3 | 49.9 | 37.3 | 47.97 |
| phi-2 | 28.0 | 70.8 | 44.4 | 35.2 | 44.61 |
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
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)
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 62.80 |
| AI2 Reasoning Challenge (25-Shot) | 60.84 |
| HellaSwag (10-Shot) | 75.52 |
| MMLU (5-Shot) | 57.57 |
| TruthfulQA (0-shot) | 48.22 |
| Winogrande (5-shot) | 75.45 |
| GSM8k (5-shot) | 59.21 |