| --- |
| license: mit |
| datasets: |
| - marcomaccarini/ds_robot_33_large |
| language: |
| - en |
| - it |
| metrics: |
| - accuracy |
| base_model: |
| - meta-llama/Meta-Llama-3.1-8B |
| library_name: peft |
| --- |
| |
| ## IMPORTS |
| ```bash |
| pip install trl peft torch datasets transformers jupyterlab accelerate tiktoken matplotlib bitsandbytes evaluate scikit-learn |
| ``` |
|
|
| ## CODE |
|
|
| ```python |
| from huggingface_hub import login |
| access_token = "secret-token" |
| login(token=access_token) |
| |
| import torch |
| import datasets |
| from peft import PeftModel |
| from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, AutoModel, TFBertForQuestionAnswering,TFAutoModelWithLMHead |
| |
| GPU_use = 0 |
| st = "cuda:"+str(GPU_use) |
| torch.cuda.set_device(GPU_use) |
| |
| ds = datasets.load_dataset('marcomaccarini/ds_robot_33_large') |
| |
| trn = ds['train'] |
| |
| base_model = 'meta-llama/Meta-Llama-3-8B' |
| tokr = AutoTokenizer.from_pretrained(base_model) |
| model = AutoModelForCausalLM.from_pretrained("marcomaccarini/SynthLA", torch_dtype=torch.bfloat16, device_map=GPU_use,token=access_token) |
| |
| fmt = """ |
| Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. |
| USER: {} |
| === |
| {} |
| ASSISTANT:""" |
| |
| def sql_prompt(d): |
| return fmt.format(d["context"], d["question"]) |
| def question(table, quest): |
| tst = dict(**trn[8]) |
| tst['context'] = table |
| tst['question'] = quest |
| return sql_prompt(tst) |
| |
| t = 'table([ eof x: 85 y: 179 z: 548, gripper: open , black-cup x: -54 y: -27 z: 80, white-cup x: -5 y: 59 z: 60, box x: -30 y: 34 z: 100, green-cylinder x: 25 y: -3 z: 80 or: 142, green-cube x: -390 y: -490 z: 80 or: 83, grey-cube x: 56 y: -22 z: 80 or: 96, red-cube x: -32 y: 58 z: 80 or: 157, yellow-ball x: -21 y: 30 z: 20 or: 41, banana x: 2 y: 53 z: 20 or: 9, remote x: -48 y: 31 z: 30 or: 69, pen x: -53 y: -59 z: 10 or: 174 ])' |
| q = 'pick green-cube and place to black-cup' |
| |
| test = question(t,q) |
| toks = tokr(test, return_tensors="pt") |
| res = model.generate(**toks.to(st), max_new_tokens=100, top_p = 0).to('cpu') |
| |
| print(tokr.batch_decode(res)[0].replace("*","\n")) |
| |
| |
| |
| ``` |
|
|
