Instructions to use iamrahulreddy/Quintus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use iamrahulreddy/Quintus with Transformers:

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

pipe = pipeline("text-generation", model="iamrahulreddy/Quintus")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("iamrahulreddy/Quintus")
model = AutoModelForCausalLM.from_pretrained("iamrahulreddy/Quintus")
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

vLLM

How to use iamrahulreddy/Quintus with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/iamrahulreddy/Quintus

SGLang

How to use iamrahulreddy/Quintus 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 "iamrahulreddy/Quintus" \
    --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": "iamrahulreddy/Quintus",
		"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 "iamrahulreddy/Quintus" \
        --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": "iamrahulreddy/Quintus",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use iamrahulreddy/Quintus with Docker Model Runner:
```
docker model run hf.co/iamrahulreddy/Quintus
```

Quintus / docs /index.md

iamrahulreddy

release: publish Quintus project files

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	# Quintus Documentation

	Quintus-1.7B is a compact assistant built from the Qwen3-1.7B-Base architecture. The project uses online full-vocabulary knowledge distillation from a Qwen3-8B teacher, followed by targeted SFT for instruction style, identity grounding, and generation stability.

	This documentation summarizes the public architecture, training decisions, evaluation controls, and release artifacts for the showcase branch.

	## Reading Order

	- [Architecture](architecture.md): End-to-end pipeline, modules, data flow, and training phases.
	- [Experiment Timeline](experiment_timeline.md): How the project moved from offline top-k KD to final online full-vocabulary KD.
	- [Training Playbook](training_playbook.md): Practical training choices, memory rules, packing, kernels, and checkpointing.
	- [Pipeline Hardening](pipeline_hardening.md): Silent-failure classes and the safeguards added around artifacts, provenance, and runtime.
	- [Evaluation Methodology](evaluation_methodology.md): Benchmark controls, parser traps, raw/chat comparisons, and qualitative evaluation rules.
	- [Engineering Insights](engineering_insights.md): Condensed technical lessons and design decisions.
	- [Benchmarks](benchmarks.md): Verified evaluation results and interpretation.
	- [Weight Audit](weight_audit.md): Structural checkpoint verification and what the audit means.
	- [Hugging Face Model Card](huggingface_model_card.md): Release-page text for the public model card.

	## Project Summary

	The core thesis is simple: a small base model can absorb useful reasoning behavior from a larger instruction model if the distillation signal is dense enough and the evaluation controls are fair.

	The project initially explored sparse offline top-k distillation, but that approach hit a ceiling because the student only saw a tiny fraction of the teacher vocabulary distribution. The final pipeline pivots to online KD, where teacher and student are run together and the student receives the teacher's full-vocabulary probability distribution during training.

	After KD, a small SFT stage teaches the model how to expose that knowledge in a conversational interface. This separation matters: KD transfers capability; SFT and later preference training improve behavior, style, and confidence calibration.

	## Repository Map

	```text
	configs/ Training configuration and DeepSpeed template.
	src/ Online KD, data loading, losses, checkpointing, and packing.
	sft/ Post-KD supervised fine-tuning, chat, and consolidated evaluation runner.
	weight_audit/ Checkpoint structure and weight-divergence audit.
	docs/ Public architecture, training, evaluation, and release notes.
	```

	## Main Public Artifact

	The final model weights are available at: [Quintus](https://huggingface.co/iamrahulreddy/Quintus)

	The Colab quickstart is available at: [Colab Quick Chat](https://colab.research.google.com/drive/1TdMSN5HzD1mToCFVf_qQoj10NGZLy2V0?usp=sharing)