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--- |
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license: apache-2.0 |
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tags: |
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- unsloth |
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- Agriculture |
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- QA |
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- LLM |
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datasets: |
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- KisanVaani/agriculture-qa-english-only |
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language: |
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- en |
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base_model: |
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- unsloth/Llama-3.2-3B-Instruct |
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new_version: ShuklaShreyansh/Agro-QA |
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pipeline_tag: question-answering |
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library_name: transformers |
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--- |
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# Model Card for Agro-QA |
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This model is fine-tuned for agricultural question-answering tasks. It leverages the Llama-3.2-3B-Instruct model to address a variety of topics in agriculture, such as crop selection, pest management, irrigation, and farming best practices. |
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## Model Details |
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### Model Description |
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- **Developed by:** Shukla Shreyansh |
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- **Model type:** Question Answering (QA) |
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- **Language(s) (NLP):** English |
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- **License:** Apache-2.0 |
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- **Finetuned from model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct) |
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--- |
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## Uses |
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### Direct Use |
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The model is intended for question-answering applications specific to agriculture. It provides insights into farming techniques, crop choices, pest management, and related topics. |
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### Out-of-Scope Use |
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The model is not designed for non-agriculture-related questions or tasks requiring specialized domain knowledge outside of agriculture. |
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--- |
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## Training Details |
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### Training Data |
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The model is fine-tuned on the [KisanVaani/agriculture-qa-english-only](https://huggingface.co/datasets/KisanVaani/agriculture-qa-english-only) dataset, a curated collection of questions and answers focused on agricultural topics. |
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### Training Procedure |
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- **Training regime:** Mixed precision (FP16) |
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- **Batch size:** 2 (per device) |
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- **Epochs:** 1 |
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- **Learning rate:** 2e-4 |
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- **Optimizer:** AdamW with 8-bit precision |
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--- |
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## Evaluation |
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### Testing Data |
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The model is evaluated on a subset of the training dataset to measure its performance in answering agriculture-related questions. |
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### Metrics |
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- **Accuracy:** [More Information Needed] |
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- **F1 Score:** [More Information Needed] |
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--- |
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## How to Get Started with the Model |
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Use the code below to load and use the model: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# Load tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("ShuklaShreyansh/Agro-QA") |
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# Load model |
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model = AutoModelForCausalLM.from_pretrained("ShuklaShreyansh/Agro-QA").to("cuda") |
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# Example usage |
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messages = [{"role": "user", "content": "What are the best rabi crops to grow?"}] |
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inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt").to("cuda") |
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output = model.generate(input_ids=inputs['input_ids'], max_new_tokens=128) |
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print(tokenizer.decode(output[0])) |
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``` |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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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). |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- 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. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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[More Information Needed] |
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## Model Card Contact |
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[More Information Needed] |
