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Crystalcareai/CrystalMistral-26b
CrystalMistral-26b is a text generation model from Crystalcareai. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
CrystalMistral-26b is a Mixure of Experts (MoE) made with the following models using LazyMergekit: Crystalcareai/CrystalMistral Crystalcareai/CrystalMistral
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From the Hugging Face model README
CrystalMistral-26b is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
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."
!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"])