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

# Uploaded  model

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

## Model Details

This model is finetuned on [adeocybersecurity/DockerCommand](https://huggingface.co/datasets/adeocybersecurity/DockerCommand) dataset using the base [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) model. These are only the lora adapaters of the model, the base model is automatically downloaded.

## How to use

```
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
  model_name = "llama-3-docker-command-lora", # YOUR MODEL YOU USED FOR TRAINING
  max_seq_length = max_seq_length,
  dtype = dtype,
  load_in_4bit = load_in_4bit,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference

inputs = tokenizer(
[
    alpaca_prompt.format(
        "translate this sentence in docker command.", # instruction
        "Give me a list of all containers, indicating their status as well.", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)
tokenizer.batch_decode(outputs)

```

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)