---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---

# Uploaded  model

- **Developed by:** jjsprockel
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit

**Código para descarga:**
El siguiente es el código sugerido para descargar el modelo usando Unslot:

```
import torch
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "jjsprockel/Patologia_lora_model1", # YOUR MODEL YOU USED FOR TRAINING
    max_seq_length = 2048, # Choose any! Llama 3 is up to 8k
    dtype = None,
    load_in_4bit = True,
    )

FastLanguageModel.for_inference(model)

alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

```

**Código para la inferencia:**

# Get the instruction from the console
El siguiente codigo demuestra como se puede llevar a cabo la inferencia.

```
instruction = input("Ingresa la pregunta que tengas de Patología: ")

inputs = tokenizer(
[
    alpaca_prompt.format(
        instruction, # instruction
        "", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)

```

This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)