---
license: apache-2.0
language:
- id
tags:
- mistral
- text-generation-inference
---
### mistral-indo-7b

[Mistral 7b v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) fine-tuned on [Indonesian's instructions dataset](https://huggingface.co/datasets/sarahlintang/Alpaca_indo_instruct).


### Prompt template:
```
### Human: {Instruction}### Assistant: {response}
```

### Example of Usage
```
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer, GenerationConfig

model_id = "sarahlintang/mistral-indo-7b"

model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)

def create_instruction(instruction):
    prompt = f"### Human: {instruction} ### Assistant: "
    return prompt


def generate(
        instruction,
        max_new_tokens=128,
        temperature=0.1,
        top_p=0.75,
        top_k=40,
        num_beams=4,
        **kwargs
):
    
    prompt = create_instruction(instruction)
    inputs = tokenizer(prompt, return_tensors="pt")
    input_ids = inputs["input_ids"].to("cuda")
    attention_mask = inputs["attention_mask"].to("cuda")
    generation_config = GenerationConfig(
        temperature=temperature,
        top_p=top_p,
        top_k=top_k,
        num_beams=num_beams,
        **kwargs,
    )
    with torch.no_grad():
        generation_output = model.generate(
            input_ids=input_ids,
            attention_mask=attention_mask,
            generation_config=generation_config,
            return_dict_in_generate=True,
            output_scores=True,
            max_new_tokens=max_new_tokens,
            early_stopping=True
        )
    s = generation_output.sequences[0]
    output = tokenizer.decode(s)
    return output.split("### Assistant:")[1].strip()

instruction = "Sebutkan lima macam makanan khas Indonesia."
print(generate(instruction))

```