Instructions to use mohar07/qwen3-0.6b-kg-triplets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohar07/qwen3-0.6b-kg-triplets with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mohar07/qwen3-0.6b-kg-triplets")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mohar07/qwen3-0.6b-kg-triplets")
model = AutoModelForCausalLM.from_pretrained("mohar07/qwen3-0.6b-kg-triplets", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mohar07/qwen3-0.6b-kg-triplets with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mohar07/qwen3-0.6b-kg-triplets:F16
# Run inference directly in the terminal:
llama cli -hf mohar07/qwen3-0.6b-kg-triplets:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mohar07/qwen3-0.6b-kg-triplets:F16
# Run inference directly in the terminal:
llama cli -hf mohar07/qwen3-0.6b-kg-triplets:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf mohar07/qwen3-0.6b-kg-triplets:F16
# Run inference directly in the terminal:
./llama-cli -hf mohar07/qwen3-0.6b-kg-triplets:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf mohar07/qwen3-0.6b-kg-triplets:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mohar07/qwen3-0.6b-kg-triplets:F16

Use Docker

docker model run hf.co/mohar07/qwen3-0.6b-kg-triplets:F16

LM Studio
Jan

vLLM

How to use mohar07/qwen3-0.6b-kg-triplets with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohar07/qwen3-0.6b-kg-triplets"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohar07/qwen3-0.6b-kg-triplets",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mohar07/qwen3-0.6b-kg-triplets:F16

SGLang

How to use mohar07/qwen3-0.6b-kg-triplets with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mohar07/qwen3-0.6b-kg-triplets" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohar07/qwen3-0.6b-kg-triplets",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mohar07/qwen3-0.6b-kg-triplets" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohar07/qwen3-0.6b-kg-triplets",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mohar07/qwen3-0.6b-kg-triplets with Ollama:
```
ollama run hf.co/mohar07/qwen3-0.6b-kg-triplets:F16
```
Unsloth Desktop

How to use mohar07/qwen3-0.6b-kg-triplets with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mohar07/qwen3-0.6b-kg-triplets:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mohar07/qwen3-0.6b-kg-triplets:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mohar07/qwen3-0.6b-kg-triplets with Docker Model Runner:
```
docker model run hf.co/mohar07/qwen3-0.6b-kg-triplets:F16
```

Lemonade

How to use mohar07/qwen3-0.6b-kg-triplets with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mohar07/qwen3-0.6b-kg-triplets:F16

Run and chat with the model

lemonade run user.qwen3-0.6b-kg-triplets-F16

List all available models

lemonade list

Hermes Agent

How to use mohar07/qwen3-0.6b-kg-triplets with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mohar07/qwen3-0.6b-kg-triplets:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mohar07/qwen3-0.6b-kg-triplets:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mohar07/qwen3-0.6b-kg-triplets with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mohar07/qwen3-0.6b-kg-triplets:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mohar07/qwen3-0.6b-kg-triplets:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

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:

Corpus collection from Wikipedia and arXiv
Language and quality filtering
MinHash deduplication
LLM triplet generation using DeepSeek V4-Flash
Schema validation
Semantic validation
Hard negative generation
Curriculum ordering
Entity-level train/test decontamination
Train / Validation / Test split

Training Example

Input:

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

Output:

[
  {
    "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

@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}
}

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Safetensors

Model size

0.6B params

Tensor type

BF16