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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-v2.5
Qwen2-4x1.5B-v2.5 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
gate_mode: hidden
architecture: qwen
dtype: bfloat16
experts_per_token: 2
base_model: cognitivecomputations/dolphin-2.9.3-qwen2-1.5b
experts:
- source_model: cognitivecomputations/dolphin-2.9.3-qwen2-1.5b
positive_prompts:
- "explain"
- "describe"
- "define"
- "help"
- "assist"
- source_model: Replete-AI/Qwen2-1.5b-Instruct-Replete-Adapted
positive_prompts:
- "code"
- "algorithm"
- "programming"
- "development"
- "software"
- "framework"
- source_model: M4-ai/Hercules-5.0-Qwen2-1.5B
positive_prompts:
- "rewrite"
- "paraphrase"
- "translate"
- "reword"
- source_model: d-llm/Qwen2-1.5B-Instruct-orpo
positive_prompts:
- "summarize"
- "shorten"
- "condense"
- "tldr"
shared_experts:
- source_model: M4-ai/Hercules-5.0-Qwen2-1.5B
positive_prompts: # required by Qwen MoE for "hidden" gate mode, otherwise not allowed
- "assistant"
- "chat"
# (optional, but recommended:)
residual_scale: 0.1
```
## 💻 Usage
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer, pipeline
import torch
model = "djuna/Qwen2-4x1.5B-v2.5"
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"])
``` |