286 lines
7.7 KiB
Markdown
286 lines
7.7 KiB
Markdown
---
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language:
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- en
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license: other
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base_model:
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- Qwen/Qwen3-1.7B
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library_name: transformers
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tags:
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- agriculture
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- india
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- kisan-call-centre
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- farmer-advisory
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- qwen3
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- qlora
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- rag
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- safety-routing
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- jetson
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- offline-inference
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pipeline_tag: text-generation
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---
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# KCC-Krishi Qwen3-1.7B
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KCC-Krishi Qwen3-1.7B is a compact English-primary agricultural advisory model fine-tuned from `Qwen/Qwen3-1.7B` using QLoRA on a routing-aware Kisan Call Centre research corpus.
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The model is designed for a regional offline farmer-assistance architecture:
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```text
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regional speech
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→ local ASR
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→ translation to English
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→ runtime guard
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→ regional KCC RAG retrieval
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→ KCC-Krishi Qwen3-1.7B
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→ agrochemical exactness validation
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→ translation to farmer language
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→ local TTS
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```
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## Important status
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This model is a research and controlled-prototyping artifact.
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It is not:
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- field-validated agronomic ground truth;
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- an unrestricted agricultural authority;
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- a replacement for agronomists;
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- a replacement for current official advisories;
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- a replacement for product-label verification;
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- a replacement for farmer feedback or field validation;
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- a general-purpose assistant for arbitrary non-agricultural questions.
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## Training details
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Base model:
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```text
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Qwen/Qwen3-1.7B
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```
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Training method:
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```text
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QLoRA / LoRA
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```
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Training stack:
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```text
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Hugging Face Transformers
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TRL SFTTrainer
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PEFT
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bitsandbytes
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```
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Training hardware:
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```text
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Azure Standard_NC8as_T4_v3
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NVIDIA Tesla T4 16GB
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8 vCPUs
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56 GiB system RAM
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```
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Final training configuration:
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- training examples: 365,037;
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- validation examples used during training: 5,000;
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- one complete training epoch;
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- optimizer: `paged_adamw_32bit`;
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- learning rate: `1e-5`;
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- warmup ratio: `0.05`;
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- maximum gradient norm: `0.3`;
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- QLoRA compute dtype: `float16`;
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- Trainer FP16: disabled;
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- Trainer BF16: disabled;
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- packing: disabled;
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- assistant-only loss: disabled;
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- maximum sequence length: 1,024;
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- manual Qwen ChatML rendering;
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- hard-stop monitoring enabled.
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Training duration:
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```text
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approximately 85.8 hours
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```
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The full training run completed without NaN loss, Inf loss, loss explosion, CUDA out-of-memory failure, or hard-stop activation.
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## Final model selection
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The selected adapter originated from the exact complete-epoch checkpoint:
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```text
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checkpoint-22815
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```
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The selected adapter was compared against the validation-selected checkpoint-18000 using normal RAG, OOD, and adversarial behavioral probes.
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The selected candidate performed better on several unsafe or adversarial cases, including:
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- pesticide mixing;
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- excessive fertilizer use;
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- double pesticide dose requests;
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- harmful pesticide framing;
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- out-of-distribution agricultural questions.
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The model was then merged with the Qwen base model using float16 safe serialization.
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## Merged model files
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The merged release includes:
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- `model.safetensors`;
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- `config.json`;
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- `generation_config.json`;
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- `tokenizer.json`;
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- `tokenizer_config.json`;
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- `chat_template.jinja`;
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- `merge_summary.json`;
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- `SHA256SUMS.txt`.
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## Required runtime architecture
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The model should not be deployed alone.
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The intended runtime is:
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```text
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out-of-scope guard
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→ current-information guard
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→ regional KCC RAG retrieval
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→ fine-tuned Qwen model
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→ agrochemical exactness guard
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→ final safety validation
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```
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The runtime guard is required because the model is not intended to answer arbitrary non-agricultural questions. The guard should block or safely handle general mathematics, politics, fictional premises, nonsense prompts, and unrelated requests.
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## Agrochemical exactness requirement
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The model may alter exact product names, formulations, percentages, or dose expressions when generating from retrieved evidence.
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Therefore, every generated answer containing pesticide, herbicide, fungicide, fertilizer, formulation, percentage, or dose-like information must be checked against verified retrieved evidence.
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If the generated agrochemical information is not supported exactly by retrieved evidence, the runtime should replace it with generic wording such as:
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```text
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Use only a locally recommended product at the current label dose. Confirm the crop, pest, crop stage, formulation, and local agricultural recommendation before application.
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```
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## Intended use
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Suitable uses include:
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- agricultural RAG research;
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- KCC evidence-grounding experiments;
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- supervised fine-tuning research;
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- offline farmer-assistant prototypes;
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- Jetson and mobile inference experiments;
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- safety-routing research;
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- multilingual voice-assistant integration through external ASR, translation, and TTS services.
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## Unsuitable use
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Do not use this model alone for:
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- direct pesticide or fertilizer recommendations;
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- autonomous disease diagnosis;
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- current weather forecasting;
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- current mandi-price reporting;
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- government-scheme status;
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- financial or medical decisions;
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- unrestricted general-purpose chat;
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- unattended field deployment.
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## Dataset
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The final model was trained using the KCC-Krishi routing-aware corpus.
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Dataset repository:
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```text
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uralstech/kcc-krishi-rag-sft-advisory-corpus
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```
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The Punjab/Wheat GPT-5.5 normalization demonstration subset was not used for final Qwen training.
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## Dataset attribution
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The underlying training evidence is derived from:
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**Kisan Call Centre (KCC) Transcripts of Farmers Queries and Answers**
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Open Government Data Platform India / Data.gov.in
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Ministry of Agriculture and Farmers Welfare, Government of India
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Accessed through AIKosh/Data.gov.in
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The KCC-Krishi processing, routing, training, packaging, and runtime safeguards were created by:
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```text
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KCC-Krishi Project Team
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URAV ADVANCED LEARNING SYSTEMS PRIVATE LIMITED
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```
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## Base model and licensing
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This model is derived from `Qwen/Qwen3-1.7B`.
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The Qwen base model repository provides its own license and attribution terms. Users must review and comply with those terms before using or redistributing this derived model.
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This model repository uses `license: other` because the release combines:
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- the Qwen base model;
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- KCC-derived training evidence;
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- project-specific fine-tuning;
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- project-specific model packaging;
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- runtime safety requirements;
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- source and derived-data attribution obligations.
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See the included `LICENSE.md` for the project release notes.
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## Limitations
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Known limitations include:
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- possible source-data noise and query/evidence mismatch;
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- historical or stale KCC records;
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- lack of agronomist field validation;
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- lack of broad farmer-feedback validation;
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- lack of RLHF or preference tuning;
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- possible model memorization of source-derived records;
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- occasional terse or evidence-report-like responses;
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- possible multilingual or translation artifacts;
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- possible exact agrochemical-name or formulation drift without the runtime guard;
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- poor behavior on arbitrary non-agricultural questions without the runtime guard.
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## Related resources
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Dataset:
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```text
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https://huggingface.co/datasets/uralstech/kcc-krishi-rag-sft-advisory-corpus
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```
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Existing Punjab/Wheat demonstration dataset:
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```text
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https://huggingface.co/datasets/uralstech/kcc-krishi-punjab-wheat-demo
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```
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Related preprint DOI:
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```text
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10.5281/zenodo.21040992
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```
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## Disclaimer
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This model is released for research, reproducibility, RAG, SFT, and controlled offline-assistant prototyping.
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Do not rely on this model as current or authoritative agricultural advice. Verify all consequential decisions with qualified agricultural experts, current official sources, current product labels, and applicable local regulations.
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