r0b0tlab/Hermes-OmniForge-Qwen36-27B-full-v0.3.0-unsloth

Hermes OmniForge Qwen3.6-27B Dataset v0.3.0

This package contains the Hermes OmniForge Qwen3.6-27B v0.3.0 synthetic SFT dataset and Unsloth-ready exports.

• data/final/train.jsonl
• data/final/validation.jsonl
• data/final/test.jsonl
• data/final/*_unsloth_text.jsonl
• data/final/*_unsloth_vision.jsonl
• scripts/export_unsloth.py
• scripts/validate_dataset.py
• scripts/train_unsloth_text_example.py
• scripts/train_unsloth_vision_example.py
• reports/dataset_report.json

Dataset Shape

Total rows:

• Train: 150,000
• Validation: 5,000
• Test: 5,000

Train component distribution:

• lambda_hermes_agent_reasoning_traces: 18%
• gpt55_hermes_synthetic: 14%
• repo_coding_terminal: 13%
• glm_reasoning_stabilizer: 10%
• general_multimodal_instruction: 14%
• multimodal_reasoning_stem_math: 9%
• ocr_document_chart_table: 9%
• video_understanding: 5%
• gui_visual_agent: 6%
• behavior_safety_stabilizers: 2%

Canonical Rows

Canonical rows use this schema:

{
  "id": "...",
  "component": "...",
  "source_dataset": "...",
  "source_license": "...",
  "source_url": "...",
  "task_family": "...",
  "benchmark_targets": [],
  "tools": [],
  "messages": [],
  "media": [],
  "loss_mask": [],
  "quality": {
    "cot_removed_or_masked": true,
    "benchmark_leakage_checked": true,
    "dedupe_hash": "...",
    "synthetic_generated": true,
    "contains_private_hidden_cot": false,
    "media_placeholder": false
  }
}

The gpt55_hermes_synthetic component contains 21,000 train rows with visible Hermes-style traces only. It includes tool calls, tool responses, grounded final answers, error recovery, direct no-tool answers, clarifications, refusal or permission-boundary cases, terminal/coding examples, GUI examples, and multimodal placeholder examples. No private hidden chain-of-thought is included.

Using with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files={
        "train": "data/final/train.jsonl",
        "validation": "data/final/validation.jsonl",
        "test": "data/final/test.jsonl",
    },
)

Using with Unsloth

Text training:

from datasets import load_dataset

dataset = load_dataset("json", data_files="data/final/train_unsloth_text.jsonl", split="train")

Vision training:

from datasets import load_dataset
from unsloth.trainer import UnslothVisionDataCollator

dataset = load_dataset("json", data_files="data/final/train_unsloth_vision.jsonl", split="train")
data_collator = UnslothVisionDataCollator(model, tokenizer)

Use these exports as follows:

• Text-only Qwen SFT: use data/final/*_unsloth_text.jsonl.
• Hermes/tool-use SFT: use data/final/*_unsloth_text.jsonl; tool calls and tool responses are flattened into assistant-visible Hermes tags.
• Qwen-VL / multimodal SFT: use data/final/*_unsloth_vision.jsonl with UnslothVisionDataCollator.
• Mixed training: load both text and vision exports separately and use the collator/model path appropriate to each training phase. For mixed-modal curriculum training, run text SFT first, then VLM SFT, or concatenate only after confirming your trainer/collator supports the combined schema.

Unsloth references used for this layout:

• Unsloth dataset guide: https://docs.unsloth.ai/basics/datasets-guide
• Unsloth vision fine-tuning guide: https://unsloth.ai/docs/basics/vision-fine-tuning
• TRL SFTTrainer guide: https://huggingface.co/docs/trl/en/sft_trainer

Tool Serialization

Canonical tool rows may contain role: "tool" messages and structured tool_calls. The Unsloth text export converts them into assistant-visible Hermes-compatible tags:

<tool_call>
{"name":"search_files","arguments":{"query":"..."}}
</tool_call>

<tool_response>
{"name":"search_files","content":{"matches":["..."],"count":1}}
</tool_response>

This keeps the export compatible with standard chat-template SFT flows that do not support role: "tool".

Vision Media

Real source images were not available in this workspace, so multimodal rows use real tiny PNG files as placeholders under data/media/placeholders/. Each canonical vision row includes:

• media[].path
• media[].type
• media[].sha256
• quality.media_placeholder = true

The Unsloth vision exports remain valid messages rows with content lists containing {"type": "text"} and {"type": "image"} objects.

Validation

Run:

python scripts/validate_dataset.py

The validator checks JSONL parsing, canonical fields, Unsloth message fields, vision content list shape, assistant outputs, absence of <think> blocks, tool-call JSON, source metadata, split counts, and train component distribution within +/-0.5%.

The validation report is written to reports/dataset_report.json.

Licensing And Attribution Notes

All rows generated in this package are synthetic and include source metadata for attribution and auditability. Benchmark leakage is marked checked, and benchmark test rows are not copied into this release.

badlogicgames/pi-mono should be treated as MIT based on the upstream badlogic/pi-mono GitHub repository, while preserving that Hugging Face metadata for badlogicgames/pi-mono says license:other. This package records that note for downstream attribution handling; no benchmark test rows or private hidden chain-of-thought are included.