transformers mixtral text-generation moe frankenmoe merge mergekit lazymergekit stabilityai/stablecode-instruct-alpha-3b stabilityai/stable-code-3b custom_code base_model:stabilityai/stablecode-instruct-alpha-3b base_model:stabilityai/stable-code-3b license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- stabilityai/stablecode-instruct-alpha-3b
- stabilityai/stable-code-3b
base_model:
- stabilityai/stablecode-instruct-alpha-3b
- stabilityai/stable-code-3b
---

# FrankenStable-3Bx3

FrankenStable-3Bx3 is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [stabilityai/stablecode-instruct-alpha-3b](https://huggingface.co/stabilityai/stablecode-instruct-alpha-3b)
* [stabilityai/stable-code-3b](https://huggingface.co/stabilityai/stable-code-3b)

## 🧩 Configuration

```yaml
base_model: stabilityai/stablelm-zephyr-3b
experts:
  - source_model: stabilityai/stablecode-instruct-alpha-3b
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
  - source_model: stabilityai/stable-code-3b
    positive_prompts:
    - "code"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
```

## 💻 Usage

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "ssands1979/FrankenStable-3Bx3"

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"])
```