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 dataset using the base 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",
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 and Huggingface's TRL library.
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for junelegend/llama-3-docker-command-lora
Base model
meta-llama/Meta-Llama-3-8B
Quantized
unsloth/llama-3-8b-bnb-4bit