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---
license: apache-2.0
language:
- vi
- en
---
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/63905e87df447b438817b2cd/QFhLKQlWeyO9XumtyghVo.jpeg" alt="Image" style="width: 400px; height: auto; border-radius: 10px;" />
</p>
## Model Details
- **Developed by:** Tuan Pham (FPTU HCM Student)
- **Model type:** Llama2-7B Decoder-only
- **Finetuned from model :**
* meta-llama/Llama-2-7b
* bkai-foundation-models/vietnamese-llama2-7b-120GB
* yeen214/llama2_7b_merge_orcafamily.
- **Bilingual support :** English and Vietnamese
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model is a proof of effort that one man can fine-tune his own model to reach SOTA.
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:**
* Training: https://github.com/vTuanpham/Vietnamese_QA_System
* Data: https://github.com/vTuanpham/Large_dataset_translator
- **Paper:** ...
- **Demo:** ...
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Prompt template
```
[SYSTEM_PROMPT]
####### Instruction:
[INPUT]
%%%%%%% Response:
[RESPONSE]
```
Recommend keeping the system prompt in english.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from torch.cuda.amp import autocast
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, pipeline
model_name = "1TuanPham/T-Llama"
model = AutoModelForCausalLM.from_pretrained(model_name,
torch_dtype=torch.bfloat16,
use_cache=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
streamer = TextStreamer(tokenizer, skip_special_tokens=True)
pipe = pipeline("text-generation", model=base_model, tokenizer=tokenizer, streamer=streamer)
with autocast():
output_default = pipe("Phạm Nhật Vượng là ", pad_token_id=50256, max_new_tokens=128)
```
## Training Details
**Hardware Type:**
* GPU: VGA NVIDIA Tesla P100 16GB
* SYSTEM RAM: 29GB
**Hours used:** ~47.5 days Approx*
### Training Data
* BactrianX
* OpenOrca_translated
* WizardLM_70k_translated
* TigerLabMathInstruct_translated_vi
* GradeSchoolMathInstruct_translated
* vilm_lima-vi
* MTEngVietnamese
* databricks_dolly15k_translated
* AlpacaCleaned_translated
* databricks_dolly15k
* OpenOrca
* GradeSchoolMathInstruct
* AlpacaCleaned
* WebglmQA
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
* Learning rate: 2e-5 cosine
* Optimizer: PagedLion8bit
* QLora: rank: 64 /Q: 4-bit
- 250k examples of 70% Vietnamese 30% English for 3.37 epoch
- 350k examples of 60% Vietnamese 40% English for 1.4 epoch
### Training loss
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Our model currently sits at TOP-5 on the VMLU benchmark
## Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
## Model Card Authors
## Model Card Contact
[More Information Needed] |