---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
license: apache-2.0
language:
- zh
- en
pipeline_tag: text-generation
datasets:
- jslin09/Themis_data_TW
---

# Model Card for Model ID

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## Model Details

### Model Description

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- **Developed by:** [Chun-Hsien Lin (a.k.a. J.S. Lin)](https://huggingface.co/jslin09)
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [Causal Language Modeling](https://huggingface.co/docs/transformers/tasks/language_modeling)
- **Language(s) (NLP):** Traditional Chinese, English
- **License:** [Meta Llama 2](https://ai.meta.com/llama/license/)
- **Finetuned from model [optional]:** [Meta LlaMA 2](https://huggingface.co/meta-llama/Llama-2-7b-hf)

### Model Sources [optional]

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- **Repository:** [Meta LlaMA 2](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

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## Training Details

### Training Data

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[台灣法律諮詢常見問答資料集](https://huggingface.co/datasets/jslin09/Themis_data_TW)

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### Training Procedure 

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#### Preprocessing [optional]

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#### 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 -->

#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### Framework versions


- PEFT 0.6.0.dev0