CrystalMistral-24B

CrystalMistral-24B

CrystalMistral-24B is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

• eren23/dpo-binarized-NeuralTrix-7B
• macadeliccc/WestLake-7B-v2-laser-truthy-dpo
• Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp
• cognitivecomputations/WestLake-7B-v2-laser

🧩 Configuration

base_model: eren23/dpo-binarized-NeuralTrix-7B
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: eren23/dpo-binarized-NeuralTrix-7B
    positive_prompts:
      - "Generate a response to a given situation"
      - "Explain the concept of climate change"
  - source_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
    positive_prompts:
      - "What is the capital of France?"
      - "Who wrote the novel 'Pride and Prejudice'?"
  - source_model: Weyaxi/OpenHermes-2.5-neural-chat-v3-2-Slerp
    positive_prompts:
      - "Write a short poem about spring"
      - "Design a logo for a tech startup called 'GreenLeaf'"
  - source_model: cognitivecomputations/WestLake-7B-v2-laser
    positive_prompts:
      - "Solve the equation x^2 +  3x -  10 =  0"
      - "Calculate the area of a circle with radius  5 units"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Crystalcareai/CrystalMistral-24B"

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"])