AmeerH_-_Mistral-Math-2x7b-mix-gguf

Quantization made by Richard Erkhov.

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Mistral-Math-2x7b-mix - GGUF

• Model creator: https://huggingface.co/AmeerH/
• Original model: https://huggingface.co/AmeerH/Mistral-Math-2x7b-mix/

Original model description:

license: apache-2.0 tags:

• moe
• frankenmoe
• merge
• mergekit
• lazymergekit
• mistralai/Mistral-7B-Instruct-v0.2
• meta-math/MetaMath-Mistral-7B base_model:
• mistralai/Mistral-7B-Instruct-v0.2
• meta-math/MetaMath-Mistral-7B

---

Mistral-Math-2x7b-mix

Mistral-Math-2x7b-mix is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

• mistralai/Mistral-7B-Instruct-v0.2
• meta-math/MetaMath-Mistral-7B

🧩 Configuration

base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: float16
gate_mode: cheap_embed
experts:
  - source_model: mistralai/Mistral-7B-Instruct-v0.2
    positive_prompts: ["You are helpful assistant."]
  - source_model: meta-math/MetaMath-Mistral-7B
    positive_prompts: ["You are an assistant good at math."]

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "AmeerH/Mistral-Math-2x7b-mix"

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"])