---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
inference:
  parameters:
    temperature: 0.01
---

A Mistral7B Instruct (https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) 
Finetune using QLoRA on the docs available in https://docs.modular.com/mojo/

The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.

## Instruction format
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch

device = "cuda" # the device to load the model onto

model_name = "mcysqrd/MODULARMOJO_Mistral_V1"
model = AutoModelForCausalLM.from_pretrained(model_name, 
                                             use_flash_attention_2=True,
                                             max_memory={0: "24GB"},
                                             device_map="auto", 
                                             trust_remote_code=True,
                                             low_cpu_mem_usage=True,
                                             return_dict=True,
                                             torch_dtype=torch.bfloat16,
                                            )
                                            
tokenizer = AutoTokenizer.from_pretrained(model_name,add_bos_token=True,trust_remote_code=True)

model.config.use_cache = True
def stream(user_prompt):
    runtimeFlag = "cuda:0"
    system_prompt = 'MODULAR_MOJO'
    B_INST, E_INST = "[INST]", "[/INST]"
    prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n{E_INST}"
    inputs = tokenizer([prompt], return_tensors="pt").to(runtimeFlag)
    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
    _ = model.generate(**inputs, streamer=streamer, max_new_tokens=1600)

stream("""can you translate this python code to mojo to make more performant making T as struct?
    class T():
        self.init(v:float):
            self.value=v
    
    def sum_objects(a:T,b:T)->T:
        return T(a.v+b.v)""")
```