---
base_model: Qwen/Qwen3-1.7B
library_name: transformers
pipeline_tag: text-generation
language:
- en
tags:
- kanha
- qwen3
- continual-pretraining
---

# Kanha Qwen3 PIT checkpoint

## Run identity

- Run ID: `kanha.ai-1.7b-pit-quality-v1`
- Base model: `Qwen/Qwen3-1.7B`
- Base model revision: `70d244cc86ccca08cf5af4e1e306ecf908b1ad5e`
- Tokenizer revision: `70d244cc86ccca08cf5af4e1e306ecf908b1ad5e`
- Training method: PIT document continuation plus Q&A
- Final merged dtype: `bfloat16`
- Source site: https://kanha.ai
- Training identity hash: `16baaea4d1c5a89dce8f3cdb27922bd9dbe51a1891b1b8bf5d90a3364406a7f7`
- Train documents: 17
- Evaluation documents: 0
- Train Q&A pairs: 170
- Evaluation Q&A pairs: 0

## Hyperparameters

- Max length: `2048`
- Epochs: `20.0`
- Learning rate: `1e-05`
- Batch size: `8`
- Grad accum: `2`
- Seed: `42`
- System prompt: `"You are a helpful assistant. Answer the user's question accurately and concisely."`
- Weight decay: `0.1`
- Adam beta1: `0.9`
- Adam beta2: `0.95`
- Adam epsilon: `1e-08`
- Warmup steps: `0`
- Max grad norm: `1.0`
- Bf16: `true`
- Tf32: `true`
- Gradient checkpointing: `true`
- Learning rate schedule: `"cosine_with_0.1_minimum"`
- Optimizer: `"adamw_torch"`
- Logging steps: `1`
- Save strategy: `"epoch"`
- Save total limit: `3`
- Data seed: `42`
- Report to: `"none"`
- Remove unused columns: `false`

## Evaluation

- dates_recall: `1.0`
- deterministic_pass_rate: `0.0`
- list_recall: `0.07884615384615384`
- numbers_recall: `0.7307692307692307`
- refusal_rate: `0.0`
- total: `26`
- unsupported_value_rate: `0.6538461538461539`
- urls_recall: `1.0`

The external evaluation suite qualifies server-side behavior only. The exact converted artifact still requires browser and target-device validation.

## MLC availability

No validated MLC artifact is included in this publication.

## Limitations

The checkpoint can produce incorrect, incomplete, stale, or memorized content. The private training corpus is website-specific, and evaluation results do not establish general capability or production safety.

## Provenance artifacts

- `research/run-manifest.json`
- `research/training-config.json`
- `research/publication-inventory.json`
- `research/evaluation/metrics.json`
- `research/evaluation/evaluation-manifest.json`