Gemma 4 Agriculture Qa
A domain-specific instruction-tuned version of Google Gemma 4 E4B Instruct, fine-tuned using QLoRA for agriculture, livestock, veterinary, and dairy farming question answering.
Model source
- Kind
- Other
- File size
- 14.8 GB
- Base model
- google/gemma-4-E4B-it
- Version
- vRevision cf509e1
- Updated
- Oct 20, 2026
- Downloads
- 27
- Source
- Hugging Face
Source description
Gemma 4 Agriculture Assistant
A domain-specific instruction-tuned version of Google Gemma 4 E4B Instruct, fine-tuned using QLoRA for agriculture, livestock, veterinary, and dairy farming question answering.
Model Details
Model Description
This model is built on google/gemma-4-E4B-it and fine-tuned for agricultural question answering using supervised instruction tuning.
It is designed to assist with:
- 🌾 Crop management
- 🐄 Livestock health
- 💉 Animal vaccination
- 🌱 Soil health
- 💧 Irrigation
- 🐃 Dairy farming
- 🐐 Goat & sheep farming
- 🦆 Poultry management
- 🌿 Pest and disease management
Developed by
Kamal Jaiswal
Model Type
Instruction-tuned Causal Language Model
Base Model
google/gemma-4-E4B-it
Fine-tuned From
google/gemma-4-E4B-it
Languages
- English
License
Gemma License
Training Details
Fine-tuning Method
- QLoRA
- LoRA Adapters
- 4-bit Quantization
- Supervised Fine-Tuning (SFT)
Training Metrics
| Metric | Value |
|---|---|
| Base Model | Gemma 4 E4B IT |
| Epochs | 1 |
| Runtime | 937.6 seconds |
| Training Steps | 48 |
| Validation Loss | 0.9112 |
| Mean Token Accuracy | 79.73% |
| Train Samples/sec | 0.818 |
| Train Steps/sec | 0.051 |
Evaluation
The model was evaluated on a held-out validation dataset after one epoch of supervised fine-tuning.
Evaluation Metrics
| Metric | Value |
|---|---|
| Validation Loss | 0.9112 |
| Perplexity | 2.49 |
| Mean Token Accuracy | 79.73% |
| Entropy | 0.8914 |
| Validation Tokens | 769,406 |
Results
The model successfully learned domain-specific agricultural knowledge while maintaining stable validation performance.
Key observations:
- Validation Loss: 0.9112
- Perplexity: 2.49, indicating the model predicts validation tokens with relatively high confidence.
- Mean Token Accuracy: 79.73%, showing strong token-level prediction performance.
- The model completed one epoch of QLoRA fine-tuning without signs of instability.
Training Summary
| Metric | Value |
|---|---|
| Base Model | google/gemma-4-E4B-it |
| Fine-tuning Method | QLoRA |
| Epochs | 1 |
| Training Steps | 48 |
| Runtime | 937.6 seconds (~15.6 min) |
| Train Loss | 1.6427 |
| Validation Loss | 0.9112 |
| Perplexity | 2.49 |
| Mean Token Accuracy | 79.73% |
Notes
These metrics represent an initial proof-of-concept fine-tuning run. Future versions of the model will be evaluated on domain-specific agriculture and livestock question-answering benchmarks, multilingual datasets (English and Hindi), and expert human evaluations.
Intended Uses
This model is suitable for:
- Agriculture assistants
- Livestock advisory
- Veterinary Q&A
- Dairy farming support
- Educational applications
- AI-powered agricultural chatbots
Future Roadmap
- ✅ Agriculture instruction tuning
- 🔄 Hindi fine-tuning
- 🔄 Hinglish support
- 🔄 Multilingual agriculture assistant
- 🔄 Image-based crop disease diagnosis
- 🔄 Livestock image analysis
- 🔄 RAG with government agriculture and veterinary knowledge
Sources
1 source
Sources
1 sourceVerified Oct 20
Model artifacts
1 artifact
Model artifacts
1 artifactSource excerpts
3 excerpts
Source excerpts
3 excerpts--- library_name: transformers license: gemma base_model: google/gemma-4-E4B-it pipeline_tag: text-generation language: - en tags: - gemma - gemma-4 - agriculture - livestock - veterinary - dairy - qlora - lora - instruction-tuning - transformers - pytorch --- # Gemma 4 Agriculture Assistant A domain-specific instruction-tuned version of **Google Gemma 4 E4B Instruct**, fine-tuned using **QLoRA** for agriculture, livestock, veterinary, and dairy farming question answering. ## Model Details ### Model Description This model is built on **google/gemma-4-E4B-it** and fine-tuned for agricultural question answering using supervised instruction tuning. It is designed to assist with: - 🌾 Crop management - 🐄 Livestock health - 💉 Animal vaccination - 🌱 Soil health - 💧 Irrigation - 🐃 Dairy farming - 🐐 Goat & sheep farming - 🦆 Poultry management - 🌿 Pest and disease management ### Developed by Kamal Jaiswal ### Model Type Instruction-tuned Causal Language Model ### Base Model `google/gemma-4-E4B-it` ### Fine-tuned From `google/gemma-4-E4B-it` ### Languages - English ### License Gemma License ## Training Details ### Fine-tuning Method - QLoRA - LoRA Adapters - 4-bit Quantization - Supervised Fine-Tuning (SFT) ### Training Metrics | Metric | Value | |---------|------:| | Base Model | Gemma 4 E4B IT | | Epochs | 1 | | Runtime | 937.6 seconds | | Training Steps | 48 | | Validation Loss | 0.9112 | | Mean Token Accuracy | 79.73% | | Train Samples/sec | 0.818 | | Train Steps/sec | 0.051 | ## Evaluation The model was evaluated on a held-out validation dataset after one epoch of supervised fine-tuning. ### Evaluation Metrics | Metric | Value | |---------|-------:| | Validation Loss | **0.9112** | | Perplexity | **2.49** | | Mean Token Accuracy | **79.73%** | | Entropy | **0.8914** | | Validation Tokens | **769,406** | ### Results The model successfully learned domain-specific agricultural knowledge while maintaining stable validation performance. Key observations: - Validation Loss: **0.9112** - Perplexity: **2.49**, indicating the model predicts validation tokens with relatively high confidence. - Mean Token Accuracy: **79.73%**, showing strong token-level prediction performance. - The model completed one epoch of QLoRA fine-tuning without signs of instability. ### Training Summary | Metric | Value | |---------|-------:| | Base Mode...
Source context: 27 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo kamal3501/gemma-4-agriculture-qa
Source context: 92 downloads · 0 likes · Pipeline text-generation · Library transformers · Repo kamal3501/gemma-4-agriculture-qa
