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The_Scamper-opt70bqt

Table Question Answering
Transformers
Safetensors
Thai
English
llama
text-generation
text-generation-inference
Inference Endpoints
4-bit precision
bitsandbytes
Model card Files Community
5
The_Scamper-opt70bqt
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---
license: apache-2.0
language:
- th
- en
metrics:
- accuracy
datasets:
- AIAT/The_Scamper-train
pipeline_tag: table-question-answering


---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **Developed by:** The Scamper
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Transformer
- **Language(s) (NLP):** Thai, English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** OpenThaiGPT-1.0.0 70B (https://huggingface.co/openthaigpt/openthaigpt-1.0.0-70b-chat) 


## 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. -->

The Tubular Question Answering Large Language Model is based on OpenThaiGPT and fine-tuned for converting natural language questions into SQL queries. It learns to map the nuances of Thai language to SQL structures, enabling efficient retrieval of information from databases.

model2_path ="AIAT/The_Scamper-opt70bqt"
tokenizer = AutoTokenizer.from_pretrained(model2_path, padding_side="right",use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model2_path,
                                             device_map="auto")


### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->



## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->