mistral3instruct-4x7MOE

mistral3instruct-4x7MOE is a Mixture of Experts (MoE) made with the following models using LazyMergekit:

• AlSamCur123/Mistral3InstructContinuedFine
• AlSamCur123/Mistral3InstructLogicContinuedFine
• AlSamCur123/mistral-7b2ContinuedFine
• AlSamCur123/Mistral3InstructLogicContinuedFine

🧩 Configuration

base_model: AlSamCur123/Mistral3InstructContinuedFine
experts:
  - source_model: AlSamCur123/Mistral3InstructContinuedFine
    positive_prompts:
    - "chat"
    - "assistant"
    - "tell me"
    - "explain"
    - "I want"
  - source_model: AlSamCur123/Mistral3InstructLogicContinuedFine
    positive_prompts:
    - "code"
    - "python"
    - "javascript"
    - "programming"
    - "algorithm"
  - source_model: AlSamCur123/mistral-7b2ContinuedFine
    positive_prompts:
    - "storywriting"
    - "write"
    - "scene"
    - "story"
    - "character"
  - source_model: AlSamCur123/Mistral3InstructLogicContinuedFine
    positive_prompts:
    - "reason"
    - "math"
    - "mathematics"
    - "solve"
    - "count"

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "AlSamCur123/mistral3instruct-4x7MOE"

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"])