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---
base_model: vince62s/phi-2-psy
inference: false
license: mit
model_creator: vince62s
model_name: phi-2-psy
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- merge
- mergekit
- lazymergekit
- rhysjones/phi-2-orange
- cognitivecomputations/dolphin-2_6-phi-2
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# vince62s/phi-2-psy-GGUF
Quantized GGUF model files for [phi-2-psy](https://huggingface.co/vince62s/phi-2-psy) from [vince62s](https://huggingface.co/vince62s)
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [phi-2-psy.fp16.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.fp16.gguf) | fp16 | 5.56 GB |
| [phi-2-psy.q2_k.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q2_k.gguf) | q2_k | 1.11 GB |
| [phi-2-psy.q3_k_m.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q3_k_m.gguf) | q3_k_m | 1.43 GB |
| [phi-2-psy.q4_k_m.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q4_k_m.gguf) | q4_k_m | 1.74 GB |
| [phi-2-psy.q5_k_m.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q5_k_m.gguf) | q5_k_m | 2.00 GB |
| [phi-2-psy.q6_k.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q6_k.gguf) | q6_k | 2.29 GB |
| [phi-2-psy.q8_0.gguf](https://huggingface.co/afrideva/phi-2-psy-GGUF/resolve/main/phi-2-psy.q8_0.gguf) | q8_0 | 2.96 GB |
## Original Model Card:
# 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)
``` |