Kybalion-1B

Kybalion-1B is a 1B-parameter language model built on top of Llama 3.2 1B through a full Continued Pre-Training (CPT) → Supervised Fine-Tuning (SFT) pipeline, trained entirely on Google Colab A100.

Why "Kybalion"? The model was originally developed under the internal codename Prometheus-1B, but was renamed to Kybalion-1B before public release to avoid confusion with an existing model of the same name on HuggingFace. Kybalion refers to the ancient hermetic text symbolizing hidden knowledge — fitting for a model focused on education, mathematics, science, and code.

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🏆 Key Highlights

• Beats Llama-3.2-1B-Instruct on HellaSwag (63.8% vs 61.1%) and ties on WinoGrande (62.4%)
• 4.5× GSM8K improvement over TinyLlama-1.1B (10.8% vs 2.4%) — math pretraining works
• Outperforms TinyLlama-1.1B on all 6 benchmarks
• Trained by a single undergraduate student on consumer cloud hardware

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🔬 Key Contributions

• Demonstrates that domain-balanced continued pretraining on curated multi-domain data (education, math, code, science) yields consistent improvements across commonsense reasoning benchmarks in 1B-scale models
• Suggests that multi-step mathematical reasoning remains a fundamental bottleneck for 1B-scale models, even when combining math-focused pretraining (OpenWebMath) with instruction tuning (MetaMathQA)
• Provides a fully reproducible, compute-efficient training recipe (CPT → LoRA SFT) built and executed by a single undergraduate student in under one week, demonstrating that meaningful LLM research is achievable without institutional resources or large teams

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📊 Benchmark Results

All scores measured with lm-evaluation-harness under identical conditions (same prompts, same few-shot settings, same hardware).

Benchmark	TinyLlama-1.1B	Llama-3.2-1B-Instruct	Kybalion-1B
MMLU	25.0%	46.1%	32.0%
ARC-C	37.2%	41.5%	37.6%
GSM8K	2.4%	33.5%	10.8%
HellaSwag	61.2%	61.1%	63.8% 🏆
WinoGrande	61.8%	62.4%	62.4% 🏆
TruthfulQA	37.4%	43.3%	40.0%

🏆 = outperforms Llama-3.2-1B-Instruct All evaluations run with lm_eval.simple_evaluate(), bfloat16, batch_size=8, A100 GPU.

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🔧 Training Pipeline

Phase 1: Continued Pre-Training (CPT)

Fine-tuned the base weights of meta-llama/Llama-3.2-1B on ~3.5B tokens of curated multi-domain data.

Domain	Dataset	Ratio	Purpose
Education	FineWeb-Edu (score ≥ 3.0)	35%	General knowledge & reasoning
Mathematics	OpenWebMath	20%	Mathematical reasoning
Code	StarCoderData (Python)	15%	Code generation
Textbook	Cosmopedia web_samples_v2	15%	Structured knowledge
Science	Cosmopedia stanford	10%	Scientific reasoning
Story	Cosmopedia stories	5%	Language fluency

Training config:

• Hardware: Google Colab A100 80GB
• Optimizer: AdamW, LR = 2e-5, Cosine decay, Warmup = 1000 steps
• Precision: BF16
• Effective batch size: 32 (4 × 8 grad accum)
• Sequence length: 2048 (packed)
• Framework: HuggingFace transformers.Trainer (no Unsloth)

Phase 2: Supervised Fine-Tuning (SFT)

Applied LoRA adapters to teach instruction-following, then merged into base weights.

Dataset	Size	Purpose
OpenHermes 2.5	100K	General instruction following
MetaMathQA	50K	Mathematical reasoning (GSM8K boost)
CodeAlpaca	20K	Code generation

SFT config:

• Method: LoRA (r=64, α=128, dropout=0.05)
• Target modules: q/k/v/o/gate/up/down proj (all linear layers)
• LR = 1e-4, Epochs = 3, Cosine decay
• Merged with PeftModel.merge_and_unload() for standalone deployment

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💻 Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("devwoo/Kybalion-1B")
model = AutoModelForCausalLM.from_pretrained(
    "devwoo/Kybalion-1B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

def chat(user_message, system="You are a helpful and knowledgeable AI assistant."):
    prompt = (
        f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n"
        f"{system}<|eot_id|>"
        f"<|start_header_id|>user<|end_header_id|>\n\n"
        f"{user_message}<|eot_id|>"
        f"<|start_header_id|>assistant<|end_header_id|>\n\n"
    )
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=512,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            eos_token_id=tokenizer.convert_tokens_to_ids("<|eot_id|>"),
        )
    return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

print(chat("Explain the Pythagorean theorem and give an example."))
print(chat("Write a Python function to check if a number is prime."))

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📦 GGUF Version

A quantized GGUF q4_k_m version is available at devwoo/Kybalion-1B-GGUF for CPU/mobile inference with llama.cpp or Ollama.

# With llama.cpp
./llama-cli -m Kybalion-1B-q4_k_m.gguf -p "Explain quantum computing." -n 256

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⚠️ Limitations

• 1B parameters — smaller than most production models; may struggle with complex multi-step reasoning
• Not RLHF-aligned; may occasionally produce unhelpful or inconsistent responses
• English-only training data
• GSM8K score (10.8%) reflects room for improvement in math reasoning compared to larger models

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📄 License

This model is derived from meta-llama/Llama-3.2-1B and follows the Llama 3.2 Community License. Training datasets are used under their respective open licenses.