gemma-dolly-agriculture

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

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