Downloads · 30 days
0
gemma-2b-finetuning-on-cybersec
Daxuxu36/gemma-2b-finetuning-on-cybersec
gemma-2b-finetuning-on-cybersec is a machine learning model from Daxuxu36. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Gemma-2b-finetuning-on-cyberse is amodel based on gemma-2b, Finetuning data set is "Daxuxu36/gemma-2b-finetuning-on-cybersec".
Access
Public
Updated Mar 21, 2024
Repo size
135 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt78.6 MB · 58%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Mar 21, 2024
- Updated
- Mar 21, 2024
- SHA
- c9adb429
Model card
From the Hugging Face model README
Introduction
Gemma-2b-finetuning-on-cyberse is amodel based on gemma-2b, Finetuning data set is "Daxuxu36/gemma-2b-finetuning-on-cybersec".
How to use it
GPU version, load the model with 4bit quant tech
model_id = "Daxuxu36/gemma-2b-finetuning-on-cybersec"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map = "auto", max_length = 1024, quantization_config=bnb_config)
# prompt
instruction = "Explain the definition of Cookie"
input_text = f"Answer the following question:\n### Question: {instruction}\n ### Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))
- License
- apache-2.0
- Created
- Mar 21, 2024
- Updated
- Mar 21, 2024