transformers safetensors mixtral text-generation moe frankenmoe merge mergekit lazymergekit Crystalcareai/CrystalMistral conversational base_model:Crystalcareai/CrystalMistral license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- Crystalcareai/CrystalMistral
- Crystalcareai/CrystalMistral
- Crystalcareai/CrystalMistral
- Crystalcareai/CrystalMistral
base_model:
- Crystalcareai/CrystalMistral
- Crystalcareai/CrystalMistral
- Crystalcareai/CrystalMistral
- Crystalcareai/CrystalMistral
---

# CrystalMistral-26b

CrystalMistral-26b is a Mixure of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Crystalcareai/CrystalMistral](https://huggingface.co/Crystalcareai/CrystalMistral)
* [Crystalcareai/CrystalMistral](https://huggingface.co/Crystalcareai/CrystalMistral)
* [Crystalcareai/CrystalMistral](https://huggingface.co/Crystalcareai/CrystalMistral)
* [Crystalcareai/CrystalMistral](https://huggingface.co/Crystalcareai/CrystalMistral)

## 🧩 Configuration

```yaml
base_model: Crystalcareai/CrystalMistral-13b
gate_mode: random
dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
experts_per_token: 2
experts:
  - source_model: Crystalcareai/CrystalMistral
    positive_prompts:
      - You are an helpful general-purpose assistant"
  - source_model: Crystalcareai/CrystalMistral
    positive_prompts:
      - "You are an expert in providing detailed technical explanations."
  - source_model: Crystalcareai/CrystalMistral
    positive_prompts:
      - "You are an expert in providing detailed code."
  - source_model: Crystalcareai/CrystalMistral
    positive_prompts:
      - "You are an expert in providing emotionally emotional support."
      
```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

model = "Crystalcareai/CrystalMistral-26b"

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"])
```