README.md

---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- code
- QA
- reasoning
---


# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
A powerfull MOE 4x7b mixtral of mistral models build using 
HuggingFaceH4/zephyr-7b-beta,
mistralai/Mistral-7B-Instruct-v0.2,
teknium/OpenHermes-2.5-Mistral-7B,
Intel/neural-chat-7b-v3-3
for more accuracy and precision in general reasoning, QA and code.

- **Developed by:** NEXT AI
- **Funded by :** Zpay Labs Pvt Ltd.
- **Model type:** Mixtral of Mistral 4x7b
- **Language(s) (NLP):** Code-Reasoning-QA
-

### Model Sources

https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2
https://huggingface.co/Intel/neural-chat-7b-v3-3
https://huggingface.co/HuggingFaceH4/zephyr-7b-beta
https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B

### Instructions to run the model


from transformers import AutoTokenizer
import transformers
import torch

model = "nextai-team/Moe-4x7b-reason-code-qa" 

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)

def generate_resposne(query):
    messages = [{"role": "user", "content": query}]
    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)
    return outputs[0]['generated_text']

response = generate_resposne("How to start learning GenAI")
print(response)


<!-- Provide the basic links for the model. -->

- **Demo :** Https://nextai.co.in