YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
llama-3.2-1B-it-Procurtech-Assistant
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on Procurtech Assistant dataset.
Model description
A customer support model to help customers with their orders, incase they encounter any difficulty.
Intended uses & limitations
The training dataset can be modified, see original at customer support dataset .. I edited the system message with a bit of prompt engineering, included additional details about the eCommerce company.
You can decide what you want and further fine tune the model...
Training and evaluation data
Training data.
Used the complete dataset for training, no evaluation data, I evaluated with random prompts...
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 682
- num_epochs: 1
Training results
Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
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Model tree for Victorano/llama-3.2-1B-it-Procurtech-Assistant
Base model
meta-llama/Llama-3.2-1B-Instruct