README.md

---
license: apache-2.0
tags:
- unsloth
- Agriculture
- QA
- LLM
datasets:
- KisanVaani/agriculture-qa-english-only
language:
- en
base_model:
- unsloth/Llama-3.2-3B-Instruct
new_version: ShuklaShreyansh/Agro-QA
pipeline_tag: question-answering
library_name: transformers
---



# Model Card for Agro-QA

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.

## Model Details

### Model Description

- **Developed by:** Shukla Shreyansh
- **Model type:** Question Answering (QA)
- **Language(s) (NLP):** English
- **License:** Apache-2.0
- **Finetuned from model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)

---

## Uses

### Direct Use

The model is intended for question-answering applications specific to agriculture. It provides insights into farming techniques, crop choices, pest management, and related topics.

### Out-of-Scope Use

The model is not designed for non-agriculture-related questions or tasks requiring specialized domain knowledge outside of agriculture.

---

## Training Details

### Training Data

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.

### Training Procedure

- **Training regime:** Mixed precision (FP16)
- **Batch size:** 2 (per device)
- **Epochs:** 1
- **Learning rate:** 2e-4
- **Optimizer:** AdamW with 8-bit precision

---

## Evaluation

### Testing Data

The model is evaluated on a subset of the training dataset to measure its performance in answering agriculture-related questions.

### Metrics

- **Accuracy:** [More Information Needed]
- **F1 Score:** [More Information Needed]

---

## How to Get Started with the Model

Use the code below to load and use the model:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("ShuklaShreyansh/Agro-QA")

# Load model
model = AutoModelForCausalLM.from_pretrained("ShuklaShreyansh/Agro-QA").to("cuda")

# Example usage
messages = [{"role": "user", "content": "What are the best rabi crops to grow?"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt").to("cuda")
output = model.generate(input_ids=inputs['input_ids'], max_new_tokens=128)
print(tokenizer.decode(output[0]))
```
# 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:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## 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. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

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

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.

[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 -->

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

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]