Qwen2-4x1.5B-v2 / README.md
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---
base_model:
- cognitivecomputations/dolphin-2.9.3-qwen2-1.5b
- Replete-AI/Qwen2-1.5b-Instruct-Replete-Adapted
- M4-ai/Hercules-5.0-Qwen2-1.5B
- d-llm/Qwen2-1.5B-Instruct-orpo
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- cognitivecomputations/dolphin-2.9.3-qwen2-1.5b
- Replete-AI/Qwen2-1.5b-Instruct-Replete-Adapted
- M4-ai/Hercules-5.0-Qwen2-1.5B
- d-llm/Qwen2-1.5B-Instruct-orpo
---
# Qwen2-4x1.5B-v2
Qwen2-4x1.5B-v2 is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cognitivecomputations/dolphin-2.9.3-qwen2-1.5b](https://huggingface.co./cognitivecomputations/dolphin-2.9.3-qwen2-1.5b)
* [Replete-AI/Qwen2-1.5b-Instruct-Replete-Adapted](https://huggingface.co./Replete-AI/Qwen2-1.5b-Instruct-Replete-Adapted)
* [M4-ai/Hercules-5.0-Qwen2-1.5B](https://huggingface.co./M4-ai/Hercules-5.0-Qwen2-1.5B)
* [d-llm/Qwen2-1.5B-Instruct-orpo](https://huggingface.co./d-llm/Qwen2-1.5B-Instruct-orpo)
## 🧩 Configuration
```yaml
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer, pipeline
import torch
model = "djuna/Qwen2-4x1.5B-v2"
tokenizer = AutoTokenizer.from_pretrained(model)
generator = 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 = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = generator(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```