File size: 2,436 Bytes
3d40050 d7568e1 a4aa002 8007d81 18b67aa 8007d81 eabd9a9 d7568e1 3d40050 d7568e1 3d40050 7c23eef 3d40050 7c23eef 3d40050 7c23eef 3d40050 7c23eef 3d40050 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 |
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- cognitivecomputations/dolphin-2_6-phi-2
- rhysjones/phi-2-orange
base_model:
- cognitivecomputations/dolphin-2_6-phi-2
- rhysjones/phi-2-orange
---
# PhiMiX-2x2B
## Code is work in progress
## This is meant to be finetuned
<p align="center">
<img src="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F11201acc-4089-416d-921b-cbd71fbf8ddb_1024x1024.jpeg" width="500" class="center"/>
</p>
PhiMiX-2x2B is a Mixure of Experts (MoE) made with the following models using mergekit:
* [cognitivecomputations/dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)
* [rhysjones/phi-2-orange](https://huggingface.co/rhysjones/phi-2-orange)
## ©️ Credits
* [mlabonne's phixtral](https://huggingface.co/mlabonne/phixtral-4x2_8) for the PhiConfig and inference code.
* [mergekit](https://github.com/cg123/mergekit) code which I tweaked (you can find the PhiConfig [here](https://github.com/cg123/mergekit/blob/508348ae34be17ea0a95d0a288a6e34491a2558a/mergekit/architecture.py#L289))
by mainly adding the config in the `moe_mixtral.py` script from `mixtral` branch.
## 🧩 Configuration
```yaml
base_model: rhysjones/phi-2-orange
gate_mode: random
dtype: float16
experts:
- source_model: cognitivecomputations/dolphin-2_6-phi-2
positive_prompts: [""]
- source_model: rhysjones/phi-2-orange
positive_prompts: [""]
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "paulilioaica/PhiMiX-2x2B"
torch.set_default_device("cuda")
config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_config(config, trust_remote_code=True)
instruction = '''
def print_prime(n):
"""
Print all primes between 1 and n
"""
'''
tokenizer = AutoTokenizer.from_pretrained(
f"{model_name}",
trust_remote_code=True
)
# Tokenize the input string
inputs = tokenizer(
instruction,
return_tensors="pt",
return_attention_mask=False
)
# Generate text using the model
outputs = model.generate(**inputs, max_length=200)
# Decode and print the output
text = tokenizer.batch_decode(outputs)[0]
print(text)
``` |