qwen3-0.6b-kg-triplets/README.md

---
license: mit
language:
- en
base_model: unsloth/qwen3-0.6b
tags:
- qwen3
- lora
- knowledge-graph
- relation-extraction
- information-extraction
- graph-rag
- triplet-extraction
- structured-generation
pipeline_tag: text-generation
library_name: transformers
---

# Qwen3-0.6B-KG-Triplets

Qwen3-0.6B-KG-Triplets is a LoRA finetuned version of Qwen3-0.6B specialized for ontology-constrained knowledge graph extraction.

Given a passage of text, the model generates structured triplets in the form:

```
source -> relation -> target
```

where:

- `"source"` contains an entity title and type
- `"relation"` contains a relation type and confidence weight
- `"target"` contains an entity title and type

The output is designed for direct ingestion into graph databases and GraphRAG pipelines with minimal post-processing.

---

## Motivation

Most instruction-tuned LLMs can extract entities and relations, but their outputs are difficult to ingest directly into graph databases because of:

- inconsistent entity naming
- out-of-schema relations
- poorly calibrated confidence scores
- inconsistent JSON formatting

This model was finetuned specifically to produce:

- ontology-constrained outputs
- normalized entity names
- calibrated relation confidence weights
- graph-ingestable JSON

---

## Model Details

| Property | Value |
|---|---|
| Base Model | unsloth/qwen3-0.6b |
| Finetuning | LoRA |
| Rank (r) | 32 |
| Alpha | 32 |
| Context Length | 2048 |
| Epochs | 5 |
| Optimizer | AdamW 8-bit |
| Framework | Unsloth + TRL |
| Training Type | Instruction Finetuning |
| License | MIT |

---

## Training Configuration

```python
model = FastLanguageModel.get_peft_model(
    model,
    r=32,
    target_modules=[
        "q_proj",
        "k_proj",
        "v_proj",
        "o_proj",
        "gate_proj",
        "up_proj",
        "down_proj",
    ],
    lora_alpha=32,
    lora_dropout=0,
    bias="none",
    use_gradient_checkpointing="unsloth",
)
```

| Parameter | Value |
|---|---|
| Batch size | 2 |
| Gradient accumulation | 4 |
| Learning rate | 5e-5 |
| Epochs | 5 |
| Warmup steps | 50 |
| Max sequence length | 2048 |
| Optimizer | AdamW 8-bit |
| Seed | 42 |

---

## Dataset

The model was trained on a custom instruction dataset for structured knowledge graph extraction.

### Corpus Sources

- Wikipedia
- arXiv papers

### Dataset Statistics

| Split | Examples |
|---|---|
| Train | 2575 |
| Validation | 75 |
| Test | 700 |
| **Total** | **3350** |

Additional properties:

- 20 ontology relations
- 15% hard negatives in training
- Entity-level train/test decontamination
- Curriculum ordering (easy → hard)
- Zero schema errors

---

## Relation Schema

The model predicts only the following ontology:

```
implements
trained_on
evaluates
part_of
introduces
extends
depends_on
contrasts_with
applied_to
measured_by
founded_by
developed_by
defined_as
consists_of
is_type_of
based_on
used_for
created_by
located_in
predecessor_of
```

Relations outside this ontology are intentionally not generated.

---

## Dataset Creation Pipeline

The training corpus was built using a multi-stage pipeline:

1. Corpus collection from Wikipedia and arXiv
2. Language and quality filtering
3. MinHash deduplication
4. LLM triplet generation using DeepSeek V4-Flash
5. Schema validation
6. Semantic validation
7. Hard negative generation
8. Curriculum ordering
9. Entity-level train/test decontamination
10. Train / Validation / Test split

---

## Training Example

Input:

```json
{
  "role": "user",
  "content": "Extract knowledge graph triplets..."
}
```

Output:

```json
[
  {
    "source": {
      "title": "September",
      "type": "entity"
    },
    "relation": {
      "type": "part_of",
      "weight": 0.92
    },
    "target": {
      "title": "Gregorian calendar",
      "type": "entity"
    }
  },
  {
    "source": {
      "title": "September",
      "type": "entity"
    },
    "relation": {
      "type": "defined_as",
      "weight": 0.92
    },
    "target": {
      "title": "ninth month",
      "type": "concept"
    }
  }
]
```

---

## Evaluation

Evaluation was performed using a custom triplet extraction benchmark with Hungarian bipartite matching alignment on 700 held-out entries.

### Metrics

| Metric | Score | Weight |
|---|---|---|
| Schema score | 1.000 | 0.30 |
| Entity F1 | 0.179 | 0.25 |
| Relation accuracy | 0.680 | 0.20 |
| Grounding | 0.969 | 0.15 |
| Weight score | 0.526 | 0.10 |
| Triplet F1 *(info only)* | 0.122 | — |
| Type agreement *(info only)* | 0.854 | — |
| Hallucination rate | 0.033 | — |

**Composite Score: 0.6583**

---

## What Finetuning Fixed

Finetuning addressed three major failure modes of the base model.

### 1. Entity Normalization

Input passage:

> *Studies of the Cambrian period document the rapid diversification of animal life and the emergence of most major animal phyla, with some researchers proposing that a celestial body impact may have triggered the extinction events that preceded this radiation.*

Base entity title extracted:

```
After a thorough research on the circumstantial changes and the great evolution of life in the Cambrian period
```

Finetuned entity title extracted:

```
Celestial body impact hypothesis
```

The finetuned model learns reusable and atomic graph nodes rather than copying passage fragments.

---

### 2. Schema Adherence

Base relations generated:

```
released
benefited_from
```

Finetuned relations generated:

```
based_on
used_for
applied_to
introduces
```

All generated relations belong to the predefined ontology.

---

### 3. Confidence Calibration

Base weights:

```
0.8
0.8
0.8
0.8
```

Finetuned weights:

```
0.23
0.41
0.59
0.77
```

The model learns meaningful confidence distributions where stronger relations receive higher scores.

---

## Intended Use

This model is intended for:

- Knowledge Graph Construction
- GraphRAG pipelines
- Structured Information Extraction
- Entity-Relation Extraction
- Automated KG population
- Document-to-Graph conversion

---

## Limitations

While the model demonstrates strong schema adherence and grounding, several limitations remain.

**Shallow Entity Abstraction**

The model favors concise and reusable entities but may miss deeper semantic abstractions or hierarchical entity relationships.

**Limited Recall**

The model prioritizes schema correctness and grounded extraction over exhaustive triplet recall. Entity F1 of 0.179 reflects strict Hungarian-matching alignment on a 20-relation ontology-constrained task; recall is intentionally traded for precision and schema adherence.

**English-Centric Training**

Training was primarily conducted on English Wikipedia and arXiv passages.

**Ontology Constrained**

Only the predefined 20 relation types are supported.

**Model Size Constraints**

Despite the relatively small size (0.6B parameters) and a modest training corpus (~3K examples), the model learns stable ontology-constrained extraction behavior. Larger models may achieve deeper entity understanding and broader relation coverage.

---

## Repository

Evaluation Pipeline: https://github.com/mohar-xe/HGR-finetuned-model-evaluation-pipeline

Model: https://huggingface.co/mohar07/qwen3-0.6b-kg-triplets

---

## Citation

```bibtex
@misc{das2026qwenkgtriplets,
  title={Qwen3-0.6B-KG-Triplets},
  author={Mohar Das},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/mohar07/qwen3-0.6b-kg-triplets}
}
```