Steps to try the model:
prompt Template
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:
{}"""
load the model
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM ,AutoTokenizer
config = PeftConfig.from_pretrained("damerajee/Tinyllama-sft-small")
model = AutoModelForCausalLM.from_pretrained("unsloth/tinyllama")
tokenizer=AutoTokenizer.from_pretrained("damerajee/Tinyllama-sft-small")
model = PeftModel.from_pretrained(model, "damerajee/Tinyllama-sft-small")l")
Inference
inputs = tokenizer(
[
alpaca_prompt.format(
"i want to learn machine learning help me",
"", # input
"", # output
)
]*1, return_tensors = "pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens = 312, use_cache = True)
tokenizer.batch_decode(outputs)
Model Information
The base model unsloth/tinyllama-bnb-4bitwas Instruct finetuned using Unsloth
Training Details
The model was trained for 1 epoch on a free goggle colab which took about 1 hour and 30 mins approximately
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Model tree for damerajee/Tinyllama-sft-small
Base model
unsloth/tinyllama-bnb-4bit