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README.md

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----
-license: llama3.2
----
+---
+language:
+- en
+license: llama3.2
+base_model: meta-llama/Llama-3.2-1B
+tags:
+- llama
+- continued-pretraining
+- sft
+- lora
+- 1b
+- math
+- code
+- education
+datasets:
+- HuggingFaceFW/fineweb-edu
+- open-web-math/open-web-math
+- bigcode/starcoderdata
+- HuggingFaceTB/cosmopedia
+- teknium/OpenHermes-2.5
+- meta-math/MetaMathQA
+- sahil2801/CodeAlpaca-20k
+---
+
+# Kybalion-1B
+
+**Kybalion-1B** is a 1B-parameter language model built on top of [Llama 3.2 1B](https://huggingface.co/meta-llama/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.
+
+---
+
+## 🏆 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
+
+---
+
+## 📊 Benchmark Results
+
+All scores measured with [lm-evaluation-harness](https://github.com/EleutherAI/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.
+
+---
+
+## 🔧 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
+
+---
+
+## 💻 Usage
+
+```python
+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."))
+```
+
+---
+
+## 📦 GGUF Version
+
+A quantized **GGUF q4_k_m** version is available at [devwoo/Kybalion-1B-GGUF](https://huggingface.co/devwoo/Kybalion-1B-GGUF) for CPU/mobile inference with [llama.cpp](https://github.com/ggerganov/llama.cpp) or [Ollama](https://ollama.com).
+
+```bash
+# With llama.cpp
+./llama-cli -m Kybalion-1B-q4_k_m.gguf -p "Explain quantum computing." -n 256
+```
+
+---
+
+## ⚠️ 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
+
+---
+
+## 📄 License
+
+This model is derived from `meta-llama/Llama-3.2-1B` and follows the [Llama 3.2 Community License](https://ai.meta.com/llama/license/).
+Training datasets are used under their respective open licenses.