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uralstech
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Model Details
Model Provider
uralstech
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
This model is a research and controlled-prototyping artifact.
It is not:
Base model:
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Qwen/Qwen3-1.7B
Training method:
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QLoRA / LoRA
Training stack:
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Hugging Face TransformersTRL SFTTrainerPEFTbitsandbytes
Training hardware:
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Azure Standard_NC8as_T4_v3NVIDIA Tesla T4 16GB8 vCPUs56 GiB system RAM
Final training configuration:
paged_adamw_32bit;1e-5;0.05;0.3;float16;Training duration:
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approximately 85.8 hours
The full training run completed without NaN loss, Inf loss, loss explosion, CUDA out-of-memory failure, or hard-stop activation.
The selected adapter originated from the exact complete-epoch checkpoint:
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checkpoint-22815
The selected adapter was compared against the validation-selected checkpoint-18000 using normal RAG, OOD, and adversarial behavioral probes.
The selected candidate performed better on several unsafe or adversarial cases, including:
The model was then merged with the Qwen base model using float16 safe serialization.
The merged release includes:
model.safetensors;config.json;generation_config.json;tokenizer.json;tokenizer_config.json;chat_template.jinja;merge_summary.json;SHA256SUMS.txt.The model should not be deployed alone.
The intended runtime is:
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out-of-scope guard→ current-information guard→ regional KCC RAG retrieval→ fine-tuned Qwen model→ agrochemical exactness guard→ final safety validation
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.
The model may alter exact product names, formulations, percentages, or dose expressions when generating from retrieved evidence.
Therefore, every generated answer containing pesticide, herbicide, fungicide, fertilizer, formulation, percentage, or dose-like information must be checked against verified retrieved evidence.
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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Use only a locally recommended product at the current label dose. Confirm the crop, pest, crop stage, formulation, and local agricultural recommendation before application.
Suitable uses include:
Do not use this model alone for:
The final model was trained using the KCC-Krishi routing-aware corpus.
Dataset repository:
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uralstech/kcc-krishi-rag-sft-advisory-corpus
The Punjab/Wheat GPT-5.5 normalization demonstration subset was not used for final Qwen training.
The underlying training evidence is derived from:
Kisan Call Centre (KCC) Transcripts of Farmers Queries and Answers
Open Government Data Platform India / Data.gov.in Ministry of Agriculture and Farmers Welfare, Government of India Accessed through AIKosh/Data.gov.in
The KCC-Krishi processing, routing, training, packaging, and runtime safeguards were created by:
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KCC-Krishi Project TeamURAV ADVANCED LEARNING SYSTEMS PRIVATE LIMITED
This model is derived from Qwen/Qwen3-1.7B.
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.
This model repository uses license: other because the release combines:
See the included LICENSE.md for the project release notes.
Known limitations include:
Dataset:
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https://huggingface.co/datasets/uralstech/kcc-krishi-rag-sft-advisory-corpus
Existing Punjab/Wheat demonstration dataset:
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https://huggingface.co/datasets/uralstech/kcc-krishi-punjab-wheat-demo
Related preprint DOI:
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10.5281/zenodo.21040992
This model is released for research, reproducibility, RAG, SFT, and controlled offline-assistant prototyping.
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.