File size: 1,851 Bytes
c4ad4e3 |
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 |
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- DeepMount00/Minerva-3B-base-RAG
- FairMind/Minerva-3B-Instruct-v1.0
base_model:
- DeepMount00/Minerva-3B-base-RAG
- FairMind/Minerva-3B-Instruct-v1.0
---
# Minerva-MoE-3x3B
Minerva-MoE-3x3B is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [DeepMount00/Minerva-3B-base-RAG](https://huggingface.co/DeepMount00/Minerva-3B-base-RAG)
* [FairMind/Minerva-3B-Instruct-v1.0](https://huggingface.co/FairMind/Minerva-3B-Instruct-v1.0)
## 🧩 Configuration
```yaml
base_model: sapienzanlp/Minerva-3B-base-v1.0
experts:
- source_model: DeepMount00/Minerva-3B-base-RAG
positive_prompts:
- "rispondi a domande"
- "cosa è"
- "chi è"
- "dove è"
- "come si"
- "spiegami"
- "definisci"
- source_model: FairMind/Minerva-3B-Instruct-v1.0
positive_prompts:
- "istruzione"
- "input"
- "risposta"
- "scrivi"
- "sequenza"
- "istruzioni"
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ludocomito/Minerva-MoE-3x3B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |