Upload README.md

README.md

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+---
+license: apache-2.0
+language:
+- vi
+- en
+---
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **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 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]
+```
+
+## 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/InstructEnVi_llama2-bkai-120GB-Orcafamily_250kx3.37_350kx1.1"
+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:** ~42.5 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 1.1 epoch
+
+### Training loss
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/63905e87df447b438817b2cd/3e7Ep0KQ6qNAMqnL6bmyE.png)
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Results
+
+[More Information Needed]
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+## 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]