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Model: uralstech/kcc-krishi-qwen3-1.7b Source: Original Platform
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cff-version: 1.2.0
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title: "KCC-Krishi Qwen3-1.7B"
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message: "If you use this model, please cite the KCC-Krishi model, the KCC-Krishi dataset, the underlying Government of India KCC source, and the Qwen base model."
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type: software
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authors:
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- name: "KCC-Krishi Project Team"
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affiliation: "URAV ADVANCED LEARNING SYSTEMS PRIVATE LIMITED"
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version: "1.0.0"
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date-released: "2026-07-16"
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url: "https://huggingface.co/uralstech/kcc-krishi-qwen3-1.7b"
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keywords:
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- agriculture
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- Kisan Call Centre
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- KCC
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- Qwen3
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- QLoRA
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- RAG
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- farmer advisory
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- offline inference
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- Jetson
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- safety routing
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- evidence grounding
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references:
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- type: model
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title: "Qwen/Qwen3-1.7B"
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url: "https://huggingface.co/Qwen/Qwen3-1.7B"
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- type: dataset
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title: "KCC-Krishi RAG/SFT Advisory Corpus"
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url: "https://huggingface.co/datasets/uralstech/kcc-krishi-rag-sft-advisory-corpus"
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- type: article
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title: "KCC-Krishi: A Quality-Controlled Data Pipeline for Evidence-Preserving Agricultural RAG and SFT Corpora from Kisan Call Centre Records"
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year: 2026
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doi: "10.5281/zenodo.21040992"
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- type: dataset
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title: "Kisan Call Centre (KCC) Transcripts of Farmers Queries and Answers"
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publisher: "Open Government Data Platform India / Data.gov.in"
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year: 2026
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url: "https://www.data.gov.in/resource/kisan-call-centre-kcc-transcripts-farmers-queries-answers"
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license: "other"
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LICENSE.md
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# KCC-Krishi Qwen3-1.7B — Licensing and Attribution Notes
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## License metadata
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The Hugging Face model repository uses:
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```yaml
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license: other
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```
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This is intentional because the release is a derived model combining:
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- the Qwen/Qwen3-1.7B base model;
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- KCC-derived training evidence;
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- KCC-Krishi fine-tuning;
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- project-specific model packaging;
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- project-specific runtime safety components;
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- source and derived-data attribution requirements.
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## Base model
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This model is derived from:
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```text
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Qwen/Qwen3-1.7B
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```
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Users must review and comply with the base model license and attribution terms before using, modifying, or redistributing this derived model.
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The base model license remains separate from the KCC-Krishi dataset and project-processing terms.
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## Dataset attribution
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The 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, translation, quality control, routing, fine-tuning, 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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## Derived model terms
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This model is provided for research, reproducibility, RAG, SFT, evaluation, and controlled offline-assistant prototyping.
|
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Users must:
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- preserve Qwen base-model attribution;
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- preserve KCC source attribution;
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- preserve KCC-Krishi attribution;
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- retain this licensing note;
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- comply with applicable source-data and model-license requirements;
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- review current agricultural regulations and official guidance;
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- use runtime safety and evidence-grounding components for deployment.
|
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## Prohibited interpretation
|
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This model must not be presented as:
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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 current weather or market information source;
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- a government-scheme status authority;
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- an autonomous pesticide or fertilizer recommendation system;
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- a substitute for product-label or local-agriculture-officer verification.
|
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|
|
||||||
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## Runtime requirement
|
||||||
|
|
||||||
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The intended deployment includes:
|
||||||
|
|
||||||
|
```text
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||||||
|
out-of-scope guard
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→ current-information guard
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→ regional RAG retrieval
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→ Qwen model
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→ agrochemical exactness guard
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→ final safety validation
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```
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Deployment without these controls is outside the intended project design.
|
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## No endorsement
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||||||
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No endorsement is implied by:
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- the Government of India;
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- Data.gov.in;
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- AIKosh;
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- the Ministry of Agriculture and Farmers Welfare;
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- Kisan Call Centre;
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- Qwen;
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- Hugging Face;
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- Microsoft Azure;
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- URAV Advanced Learning Systems Private Limited.
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## No warranty
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||||||
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The model is provided without a warranty that:
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- every generated answer is correct;
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- every source-derived recommendation is current;
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- every product, formulation, unit, or dose is correct;
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- every response is suitable for a particular crop or location;
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- the model is safe for unattended field deployment;
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- the model will not reproduce source-derived text.
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## Safety notice
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Pesticide, fertilizer, disease, weather, market, and government-scheme information must be independently checked against current official sources, current product labels, local regulations, and qualified agricultural advice.
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|
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## Legal notice
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||||||
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This document is not legal advice. Users are responsible for evaluating licensing, attribution, privacy, redistribution, safety, and regulatory obligations before using or distributing the model.
|
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README.md
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---
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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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|
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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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|
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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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|
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## Final model selection
|
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|
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The selected adapter originated from the exact complete-epoch checkpoint:
|
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|
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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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|
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The selected candidate performed better on several unsafe or adversarial cases, including:
|
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|
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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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|
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The model was then merged with the Qwen base model using float16 safe serialization.
|
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|
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## Merged model files
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|
|
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The merged release includes:
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||||||
|
|
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|
- `model.safetensors`;
|
||||||
|
- `config.json`;
|
||||||
|
- `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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|
|
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## Required runtime architecture
|
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|
|
||||||
|
The model should not be deployed alone.
|
||||||
|
|
||||||
|
The intended runtime is:
|
||||||
|
|
||||||
|
```text
|
||||||
|
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.
|
||||||
|
|
||||||
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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:
|
||||||
|
|
||||||
|
```text
|
||||||
|
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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## Intended use
|
||||||
|
|
||||||
|
Suitable uses include:
|
||||||
|
|
||||||
|
- agricultural RAG research;
|
||||||
|
- KCC evidence-grounding experiments;
|
||||||
|
- supervised fine-tuning research;
|
||||||
|
- offline farmer-assistant prototypes;
|
||||||
|
- Jetson and mobile inference experiments;
|
||||||
|
- safety-routing research;
|
||||||
|
- multilingual voice-assistant integration through external ASR, translation, and TTS services.
|
||||||
|
|
||||||
|
## Unsuitable use
|
||||||
|
|
||||||
|
Do not use this model alone for:
|
||||||
|
|
||||||
|
- direct pesticide or fertilizer recommendations;
|
||||||
|
- autonomous disease diagnosis;
|
||||||
|
- current weather forecasting;
|
||||||
|
- current mandi-price reporting;
|
||||||
|
- government-scheme status;
|
||||||
|
- financial or medical decisions;
|
||||||
|
- unrestricted general-purpose chat;
|
||||||
|
- unattended field deployment.
|
||||||
|
|
||||||
|
## Dataset
|
||||||
|
|
||||||
|
The final model was trained using the KCC-Krishi routing-aware corpus.
|
||||||
|
|
||||||
|
Dataset repository:
|
||||||
|
|
||||||
|
```text
|
||||||
|
uralstech/kcc-krishi-rag-sft-advisory-corpus
|
||||||
|
```
|
||||||
|
|
||||||
|
The Punjab/Wheat GPT-5.5 normalization demonstration subset was not used for final Qwen training.
|
||||||
|
|
||||||
|
## Dataset attribution
|
||||||
|
|
||||||
|
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:
|
||||||
|
|
||||||
|
```text
|
||||||
|
KCC-Krishi Project Team
|
||||||
|
URAV ADVANCED LEARNING SYSTEMS PRIVATE LIMITED
|
||||||
|
```
|
||||||
|
|
||||||
|
## Base model and licensing
|
||||||
|
|
||||||
|
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:
|
||||||
|
|
||||||
|
- the Qwen base model;
|
||||||
|
- KCC-derived training evidence;
|
||||||
|
- project-specific fine-tuning;
|
||||||
|
- project-specific model packaging;
|
||||||
|
- runtime safety requirements;
|
||||||
|
- source and derived-data attribution obligations.
|
||||||
|
|
||||||
|
See the included `LICENSE.md` for the project release notes.
|
||||||
|
|
||||||
|
## Limitations
|
||||||
|
|
||||||
|
Known limitations include:
|
||||||
|
|
||||||
|
- possible source-data noise and query/evidence mismatch;
|
||||||
|
- historical or stale KCC records;
|
||||||
|
- lack of agronomist field validation;
|
||||||
|
- lack of broad farmer-feedback validation;
|
||||||
|
- lack of RLHF or preference tuning;
|
||||||
|
- possible model memorization of source-derived records;
|
||||||
|
- occasional terse or evidence-report-like responses;
|
||||||
|
- possible multilingual or translation artifacts;
|
||||||
|
- possible exact agrochemical-name or formulation drift without the runtime guard;
|
||||||
|
- poor behavior on arbitrary non-agricultural questions without the runtime guard.
|
||||||
|
|
||||||
|
## Related resources
|
||||||
|
|
||||||
|
Dataset:
|
||||||
|
|
||||||
|
```text
|
||||||
|
https://huggingface.co/datasets/uralstech/kcc-krishi-rag-sft-advisory-corpus
|
||||||
|
```
|
||||||
|
|
||||||
|
Existing Punjab/Wheat demonstration dataset:
|
||||||
|
|
||||||
|
```text
|
||||||
|
https://huggingface.co/datasets/uralstech/kcc-krishi-punjab-wheat-demo
|
||||||
|
```
|
||||||
|
|
||||||
|
Related preprint DOI:
|
||||||
|
|
||||||
|
```text
|
||||||
|
10.5281/zenodo.21040992
|
||||||
|
```
|
||||||
|
|
||||||
|
## Disclaimer
|
||||||
|
|
||||||
|
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.
|
||||||
|
|
||||||
10
SHA256SUMS.txt
Normal file
10
SHA256SUMS.txt
Normal file
@@ -0,0 +1,10 @@
|
|||||||
|
a55ee1b1660128b7098723e0abcd92caa0788061051c62d51cbe87d9cf1974d8 chat_template.jinja
|
||||||
|
5c9fda21f43e5fed5180657fadae84b7fad5814f2c5659d2da734f028210690e CITATION.cff
|
||||||
|
580f9a1e5ac8c17ef5e17dbf9da2a1558b489863b274c92f34b33217bf8dbefd config.json
|
||||||
|
535ef5c50ad97889d648321c63a3b88349a3a2046bbf6e2fe430e7656fb13ba5 generation_config.json
|
||||||
|
a50c459f698d64af7e2a0bd948c21e77c1aa762a0bac9c86ad161f66c947a408 LICENSE.md
|
||||||
|
17ffc0d5e597655d7073735d6adf9cd1ebf8049ec7080104dcc86ffc038e7f66 merge_summary.json
|
||||||
|
2c54c2950a890a64079b791a4b5da452ff2e563765c18f3dbab83fd25a85740c model.safetensors
|
||||||
|
ea2d09e30921bd85d3d57814533edf753c5e4db1e096cbb51e739ac0749051be README.md
|
||||||
|
be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506 tokenizer.json
|
||||||
|
04b1682c59acbd057f4c9072297faa73d56fc9de053094c659cdb4c464f58f86 tokenizer_config.json
|
||||||
89
chat_template.jinja
Normal file
89
chat_template.jinja
Normal file
@@ -0,0 +1,89 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- messages[0].content + '\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||||
|
{%- for tool in tools %}
|
||||||
|
{{- "\n" }}
|
||||||
|
{{- tool | tojson }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||||
|
{%- else %}
|
||||||
|
{%- if messages[0].role == 'system' %}
|
||||||
|
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||||
|
{%- for message in messages[::-1] %}
|
||||||
|
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||||
|
{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||||
|
{%- set ns.multi_step_tool = false %}
|
||||||
|
{%- set ns.last_query_index = index %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if message.content is string %}
|
||||||
|
{%- set content = message.content %}
|
||||||
|
{%- else %}
|
||||||
|
{%- set content = '' %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{%- set reasoning_content = '' %}
|
||||||
|
{%- if message.reasoning_content is string %}
|
||||||
|
{%- set reasoning_content = message.reasoning_content %}
|
||||||
|
{%- else %}
|
||||||
|
{%- if '</think>' in content %}
|
||||||
|
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||||
|
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if loop.index0 > ns.last_query_index %}
|
||||||
|
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if message.tool_calls %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if (loop.first and content) or (not loop.first) %}
|
||||||
|
{{- '\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- if tool_call.function %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{%- if tool_call.arguments is string %}
|
||||||
|
{{- tool_call.arguments }}
|
||||||
|
{%- else %}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||||
|
{{- '<|im_start|>user' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_response>\n' }}
|
||||||
|
{{- content }}
|
||||||
|
{{- '\n</tool_response>' }}
|
||||||
|
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
||||||
|
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||||
|
{{- '<think>\n\n</think>\n\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
63
config.json
Normal file
63
config.json
Normal file
@@ -0,0 +1,63 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen3ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_bias": false,
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"dtype": "float16",
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"head_dim": 128,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 2048,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 6144,
|
||||||
|
"layer_types": [
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention"
|
||||||
|
],
|
||||||
|
"max_position_embeddings": 40960,
|
||||||
|
"max_window_layers": 28,
|
||||||
|
"model_type": "qwen3",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 28,
|
||||||
|
"num_key_value_heads": 8,
|
||||||
|
"pad_token_id": null,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_parameters": {
|
||||||
|
"rope_theta": 1000000,
|
||||||
|
"rope_type": "default"
|
||||||
|
},
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"transformers_version": "5.12.1",
|
||||||
|
"use_cache": true,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
13
generation_config.json
Normal file
13
generation_config.json
Normal file
@@ -0,0 +1,13 @@
|
|||||||
|
{
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
151645,
|
||||||
|
151643
|
||||||
|
],
|
||||||
|
"pad_token_id": 151643,
|
||||||
|
"temperature": 0.6,
|
||||||
|
"top_k": 20,
|
||||||
|
"top_p": 0.95,
|
||||||
|
"transformers_version": "5.12.1"
|
||||||
|
}
|
||||||
47
merge_summary.json
Normal file
47
merge_summary.json
Normal file
@@ -0,0 +1,47 @@
|
|||||||
|
{
|
||||||
|
"status": "complete",
|
||||||
|
"start_utc": "2026-07-13T19:13:23.185384+00:00",
|
||||||
|
"end_utc": "2026-07-13T19:13:36.707631+00:00",
|
||||||
|
"elapsed_seconds": 13.522244930267334,
|
||||||
|
"elapsed_minutes": 0.2253707488377889,
|
||||||
|
"config": "configs/qwen3_qlora_training.yaml",
|
||||||
|
"base_model": "Qwen/Qwen3-1.7B",
|
||||||
|
"adapter": "models/lora_adapters/final_selected/qwen3_1p7b_kcc_final_selected",
|
||||||
|
"output": "models/merged/qwen3_1p7b_kcc_merged_final",
|
||||||
|
"torch_dtype": "float16",
|
||||||
|
"device_map": "auto",
|
||||||
|
"safe_serialization": true,
|
||||||
|
"environment": {
|
||||||
|
"timestamp_utc": "2026-07-13T19:13:23.195533+00:00",
|
||||||
|
"python": "3.10.12 (main, Jun 22 2026, 18:55:27) [GCC 11.4.0]",
|
||||||
|
"platform": "Linux-6.8.0-1059-azure-x86_64-with-glibc2.35",
|
||||||
|
"machine": "x86_64",
|
||||||
|
"processor": "x86_64",
|
||||||
|
"cwd": "/home/azureuser/kcc-krishi",
|
||||||
|
"disk_free": {
|
||||||
|
"total": 266206101504,
|
||||||
|
"used": 156047970304,
|
||||||
|
"free": 110141353984
|
||||||
|
},
|
||||||
|
"nvidia_smi": {
|
||||||
|
"cmd": [
|
||||||
|
"nvidia-smi"
|
||||||
|
],
|
||||||
|
"returncode": 0,
|
||||||
|
"stdout": "Mon Jul 13 19:13:23 2026 \n+---------------------------------------------------------------------------------------+\n| NVIDIA-SMI 535.309.01 Driver Version: 535.309.01 CUDA Version: 12.2 |\n|-----------------------------------------+----------------------+----------------------+\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|=========================================+======================+======================|\n| 0 Tesla T4 Off | 00000001:00:00.0 Off | Off |\n| N/A 30C P8 9W / 70W | 2MiB / 16384MiB | 0% Default |\n| | | N/A |\n+-----------------------------------------+----------------------+----------------------+\n \n+---------------------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=======================================================================================|\n| No running processes found |\n+---------------------------------------------------------------------------------------+",
|
||||||
|
"stderr": ""
|
||||||
|
},
|
||||||
|
"torch_version": "2.6.0+cu124",
|
||||||
|
"cuda_available": true,
|
||||||
|
"cuda_device_count": 1,
|
||||||
|
"cuda_devices": [
|
||||||
|
{
|
||||||
|
"index": 0,
|
||||||
|
"name": "Tesla T4",
|
||||||
|
"total_memory_gb": 15.575,
|
||||||
|
"major": 7,
|
||||||
|
"minor": 5
|
||||||
|
}
|
||||||
|
]
|
||||||
|
}
|
||||||
|
}
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:2c54c2950a890a64079b791a4b5da452ff2e563765c18f3dbab83fd25a85740c
|
||||||
|
size 3441185296
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||||
|
size 11422650
|
||||||
30
tokenizer_config.json
Normal file
30
tokenizer_config.json
Normal file
@@ -0,0 +1,30 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"is_local": false,
|
||||||
|
"local_files_only": false,
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user