---
library_name: transformers
tags:
- unsloth
---

# Model Card for Model ID

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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## Uses

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### Direct Use

```from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="sharad461/OpenWiseyak-0.1-MixedSFT-Adapter",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

FastLanguageModel.for_inference(model) 

prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}

### Response:
{}"""

question = ["यो वाक्यमा सर्वनाम कुन हो?", "श्यामको सुनको औठी हरायो र उ रोयो।", ""]

inputs = tokenizer(prompt.format(
    question[0],
    question[1],
    question[2],
), return_tensors="pt").to("cuda")


outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True) 

decoded_outputs = tokenizer.batch_decode(outputs)

response = decoded_outputs[0].split("### Response:")[1].split("<|end_of_text|>")[0].strip()

print(response)

>>> वाक्यमा सर्वनाम "उ" हो, किनकि यो एक व्यक्तिगत सर्वनाम हो जसले श्यामलाई जनाउँछ।
```

### Citation Information

```
@misc{duwal2024domainadaptativecontinuallearninglowresource,
  title={Domain-adaptative Continual Learning for Low-resource Tasks: Evaluation on Nepali}, 
  author={Sharad Duwal and Suraj Prasai and Suresh Manandhar},
  year={2024},
  eprint={2412.13860},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2412.13860}, 
}

```

### 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

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## Training Details

### Training Data

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### Training Procedure

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#### Preprocessing [optional]

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#### Training Hyperparameters

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## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Summary



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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).

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## Technical Specifications [optional]

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## Glossary [optional]

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