gemma-dolly-agriculture

This model is based on google/gemma-2b, fine tuned with the dolly-qa dataset and some specific examples of agricultural disease descriptions. It achieves the following results on the evaluation set:

• Loss: 2.0198

How to Run Inference

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

model_id = "google/gemma-2b"
peft_model_id = "apfurman/gemma-dolly-agriculture"

# make sure you have access to gemma-2b as well
model = AutoModelForCausalLM.from_pretrained(model_id, token="YOUR_TOKEN_HERE")
model.load_adapter(peft_model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id, token="YOUR_TOKEN_HERE")

def ask(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").input_ids
    with torch.inference_mode():
        tokens = model.generate(
            inputs,
            pad_token_id=128001,
            eos_token_id=128001,
            max_new_tokens=200,
            repetition_penalty=1.5,
        )
        
    return tokenizer.decode(tokens[0], skip_special_tokens=True)

Intended uses & limitations

Created for prompting an AI about agricultural info, but more fine-tuning is needed as current results are not great.

Training and evaluation data

Training procedure

Trained on Intel Data Center GPU Max Series with Intel Developer Cloud running a jupyter notebook.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 16
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.05
• training_steps: 1480

Training results

Training Loss	Epoch	Step	Validation Loss
2.918	1.6393	100	2.5702
2.4342	3.2787	200	2.2747
2.2482	4.9180	300	2.1601
2.1554	6.5574	400	2.0971
2.1022	8.1967	500	2.0698
2.0806	9.8361	600	2.0544
2.0651	11.4754	700	2.0437
2.0439	13.1148	800	2.0359
2.0369	14.7541	900	2.0302
2.034	16.3934	1000	2.0263
2.0249	18.0328	1100	2.0236
2.0174	19.6721	1200	2.0218
2.0154	21.3115	1300	2.0203
2.0145	22.9508	1400	2.0198

Framework versions

• PEFT 0.10.0
• Transformers 4.40.1
• Pytorch 2.1.0.post0+cxx11.abi
• Datasets 2.19.0
• Tokenizers 0.19.1