Instructions to use Namkoy/Chatbot_math_vi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Namkoy/Chatbot_math_vi with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("vilm/vinallama-7b-chat") model = PeftModel.from_pretrained(base_model, "Namkoy/Chatbot_math_vi") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Namkoy/Chatbot_math_vi: direct link, hf CLI and curl.
- Browser
- Download file 1.28 kB
-
https://huggingface.co/Namkoy/Chatbot_math_vi/resolve/main/README.md
- Command line
-
hf download hf://Namkoy/Chatbot_math_vi/README.md
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curl -L -o README.md https://huggingface.co/Namkoy/Chatbot_math_vi/resolve/main/README.md
1.28 kB
metadata
language:
- vi
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
datasets:
- hllj/vi_grade_school_math_mcq
base_model: vilm/vinallama-7b-chat
model-index:
- name: vilm/vinallama-7b-chat
results: []
whisper_vi_nam
This model is a fine-tuned version of [vilm/vinallama-7b-chat] on the hllj/vi_grade_school_math_mcq dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2