174 lines
6.4 KiB
Markdown
174 lines
6.4 KiB
Markdown
---
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language:
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- en
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license: llama3.2
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base_model: meta-llama/Llama-3.2-1B
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tags:
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- llama
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- continued-pretraining
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- sft
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- lora
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- 1b
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- math
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- code
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- education
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- small-llm
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datasets:
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- HuggingFaceFW/fineweb-edu
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- open-web-math/open-web-math
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- bigcode/starcoderdata
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- HuggingFaceTB/cosmopedia
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- teknium/OpenHermes-2.5
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- meta-math/MetaMathQA
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- sahil2801/CodeAlpaca-20k
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---
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# Kybalion-1B
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**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.
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> **Why "Kybalion"?**
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> 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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---
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## 🏆 Key Highlights
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- **Beats Llama-3.2-1B-Instruct** on HellaSwag (63.8% vs 61.1%) and ties on WinoGrande (62.4%)
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- **4.5× GSM8K improvement** over TinyLlama-1.1B (10.8% vs 2.4%) — math pretraining works
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- Outperforms TinyLlama-1.1B on **all 6 benchmarks**
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- Trained by a single undergraduate student on consumer cloud hardware
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---
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## 🔬 Key Contributions
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- 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
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- 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)
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- 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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---
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## 📊 Benchmark Results
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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).
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| Benchmark | TinyLlama-1.1B | Llama-3.2-1B-Instruct | **Kybalion-1B** |
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|-----------|:--------------:|:---------------------:|:---------------:|
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| MMLU | 25.0% | 46.1% | **32.0%** |
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| ARC-C | 37.2% | 41.5% | **37.6%** |
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| GSM8K | 2.4% | 33.5% | **10.8%** |
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| HellaSwag | 61.2% | 61.1% | **63.8%** 🏆 |
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| WinoGrande | 61.8% | 62.4% | **62.4%** 🏆 |
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| TruthfulQA | 37.4% | 43.3% | **40.0%** |
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> 🏆 = outperforms Llama-3.2-1B-Instruct
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> All evaluations run with `lm_eval.simple_evaluate()`, bfloat16, batch_size=8, A100 GPU.
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---
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## 🔧 Training Pipeline
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### Phase 1: Continued Pre-Training (CPT)
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Fine-tuned the base weights of `meta-llama/Llama-3.2-1B` on ~3.5B tokens of curated multi-domain data.
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| Domain | Dataset | Ratio | Purpose |
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|--------|---------|-------|---------|
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| Education | FineWeb-Edu (score ≥ 3.0) | 35% | General knowledge & reasoning |
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| Mathematics | OpenWebMath | 20% | Mathematical reasoning |
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| Code | StarCoderData (Python) | 15% | Code generation |
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| Textbook | Cosmopedia web_samples_v2 | 15% | Structured knowledge |
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| Science | Cosmopedia stanford | 10% | Scientific reasoning |
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| Story | Cosmopedia stories | 5% | Language fluency |
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**Training config:**
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- Hardware: Google Colab A100 80GB
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- Optimizer: AdamW, LR = 2e-5, Cosine decay, Warmup = 1000 steps
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- Precision: BF16
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- Effective batch size: 32 (4 × 8 grad accum)
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- Sequence length: 2048 (packed)
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- Framework: HuggingFace `transformers.Trainer` (no Unsloth)
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### Phase 2: Supervised Fine-Tuning (SFT)
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Applied LoRA adapters to teach instruction-following, then merged into base weights.
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| Dataset | Size | Purpose |
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|---------|------|---------|
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| OpenHermes 2.5 | 100K | General instruction following |
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| MetaMathQA | 50K | Mathematical reasoning (GSM8K boost) |
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| CodeAlpaca | 20K | Code generation |
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**SFT config:**
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- Method: LoRA (r=64, α=128, dropout=0.05)
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- Target modules: q/k/v/o/gate/up/down proj (all linear layers)
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- LR = 1e-4, Epochs = 3, Cosine decay
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- Merged with `PeftModel.merge_and_unload()` for standalone deployment
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---
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## 💻 Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("devwoo/Kybalion-1B")
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model = AutoModelForCausalLM.from_pretrained(
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"devwoo/Kybalion-1B",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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def chat(user_message, system="You are a helpful and knowledgeable AI assistant."):
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prompt = (
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f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n"
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f"{system}<|eot_id|>"
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f"<|start_header_id|>user<|end_header_id|>\n\n"
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f"{user_message}<|eot_id|>"
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f"<|start_header_id|>assistant<|end_header_id|>\n\n"
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)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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eos_token_id=tokenizer.convert_tokens_to_ids("<|eot_id|>"),
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)
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return tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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print(chat("Explain the Pythagorean theorem and give an example."))
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print(chat("Write a Python function to check if a number is prime."))
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```
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---
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## 📦 GGUF Version
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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).
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```bash
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# With llama.cpp
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./llama-cli -m Kybalion-1B-q4_k_m.gguf -p "Explain quantum computing." -n 256
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```
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---
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## ⚠️ Limitations
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- 1B parameters — smaller than most production models; may struggle with complex multi-step reasoning
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- Not RLHF-aligned; may occasionally produce unhelpful or inconsistent responses
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- English-only training data
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- GSM8K score (10.8%) reflects room for improvement in math reasoning compared to larger models
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---
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## 📄 License
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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/).
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Training datasets are used under their respective open licenses.
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