kth8/gemma-3-270m-it-JSON-Fixer

A fine-tune of unsloth/gemma-3-270m-it on the kth8/json-fix-25000x dataset.

Usage example

System prompt

You are a JSON formatting specialist. Convert the provided JSON data into valid JSON format with 2 line indent and no additional commentary.

User prompt

The JSON is:\n[{\"name\":\"John Doe\", \"jobTitle\":Software Engineer, \"department\": \"Research and Development\"],, {\"name\"\"Jane Smith\", \"jobTitle\":\"Data Analyst', \"department\":\"Marketing and Sales\"}, ]    //\" comment\n-- end --

Assistant response

[
  {
    "name": "John Doe",
    "jobTitle": "Software Engineer",
    "department": "Research and Development"
  },
  {
    "name": "Jane Smith",
    "jobTitle": "Data Analyst",
    "department": "Marketing and Sales"
  }
]

Model Details

• —Base Model: unsloth/gemma-3-270m-it
• —Parameter Count: 268,098,176
• —Precision: torch.bfloat16

Hardware

• —GPU: NVIDIA RTX PRO 6000 Blackwell Server Edition
• —Announced: Mar 17th, 2025
• —Release Date: Mar 18th, 2025
• —Memory Type: GDDR7
• —Bandwidth: 1.79 TB/s
• —Memory Size: 96 GB
• —Memory Bus: 512 bit
• —Shading Units: 24064
• —TDP: 600W
• —FP16 (half): 126.0 TFLOPS (1:1)

Training Settings

PEFT

• —Rank: 32
• —LoRA alpha: 64
• —Modules: qproj, kproj, vproj, oproj, gateproj, upproj, down_proj
• —Gradient checkpointing: unsloth

SFT

• —Epoch: 2
• —Batch size: 32
• —Gradient Accumulation steps: 1
• —Warmup ratio: 0.05
• —Learning rate: 0.0004
• —Optimizer: adamwtorchfused
• —Learning rate scheduler: cosine
• —Max seq length: 2048

Training stats

• —Date: 2026-03-23T04:39:38.019077
• —Peak VRAM usage: 64.5 GB
• —Global step: 1538
• —Training runtime (seconds): 1142.9274
• —Average training loss: 0.004019292104312295
• —Final validation loss: 0.0014343492221087217

Framework versions

• —Unsloth: 2026.3.10
• —TRL: 0.22.2
• —Transformers: 4.56.2
• —Pytorch: 2.10.0+cu128
• —Datasets: 4.8.3
• —Tokenizers: 0.22.2

License

This model is released under the Gemma license. See the Gemma Terms of Use and Prohibited Use Policy regarding the use of Gemma-generated content.