Koala 13B
Data Quality Over Quantity
Historical Model (April 2023): Koala 13B is based on the original LLaMA 1 with a non-commercial license. It has been fully superseded by models like Llama 3 8B, Qwen 2.5 14B, and Mistral 7B. This page covers its historical significance and research contributions.
Koala 13B was developed by UC Berkeley's BAIR lab (April 2023) as a research project studying the impact of training data quality on dialogue performance. Its key finding: a model trained on curated ShareGPT conversations significantly outperformed one trained on large quantities of lower-quality web data.
Blog post: bair.berkeley.edu/blog/2023/04/03/koala. Released alongside Vicuna and Alpaca as part of the early open-source LLM wave.
🐨 What Is Koala 13B?
Model Details
- Developer: UC Berkeley BAIR
- Base Model: LLaMA 13B (original, March 2023)
- Release: April 2023
- Architecture: Decoder-only Transformer
- Context Length: 2,048 tokens
- License: Non-commercial (inherits LLaMA 1 restrictions)
- Blog: BAIR blog post
Training Data
The Koala project compared two training approaches:
- • Koala-Distill: Trained on ShareGPT conversations (real ChatGPT interactions shared by users)
- • Koala-All: Trained on ShareGPT + Open Instruction Generalist (OIG) + Alpaca data + HC3 + web data
Key finding: Koala-Distill (quality data only) performed similarly to or better than Koala-All (quantity data), despite using much less training data. This influenced future model development toward data quality over quantity.
🔬 Data Quality Research Contribution
Koala's lasting contribution isn't the model itself — it's the research finding that training data quality matters more than quantity for conversational AI.
What They Found
- • ShareGPT conversations produced better dialogue quality than large quantities of web-scraped data
- • Human evaluators preferred Koala-Distill's responses in head-to-head comparisons
- • The model could approximate ChatGPT-level conversation on many topics
- • More data didn't always mean better performance
Impact on the Field
- • Influenced Vicuna and later models to prioritize ShareGPT-style data
- • Contributed to the “data quality > data quantity” paradigm
- • Showed that instruction-following could be taught with relatively small, curated datasets
- • Part of the Berkeley open-source LLM ecosystem alongside Vicuna and LMSys
The 2023 Open-Source LLM Wave
Koala was part of an explosion of LLaMA fine-tunes in early 2023:
| Model | Developer | Date | Training Data | Key Innovation |
|---|---|---|---|---|
| Alpaca | Stanford | Mar 2023 | Self-Instruct (52K) | GPT-generated instruction data |
| Koala | UC Berkeley | Apr 2023 | ShareGPT + mixed | Data quality > quantity |
| Vicuna | LMSys/Berkeley | Apr 2023 | ShareGPT (70K) | Best early chat quality |
| Llama 2 Chat | Meta | Jul 2023 | RLHF | Commercial license, made fine-tunes obsolete |
Llama 2's release in July 2023 largely made all LLaMA 1 fine-tunes (Koala, Vicuna, Alpaca) obsolete by providing a better base model with a more permissive license.
📊 Benchmarks & Performance
Koala 13B was not extensively benchmarked on standard metrics like MMLU. The scores below are approximate, based on the LLaMA 1 13B base and similar models from the same era.
Koala was primarily evaluated through human preference studies rather than automated benchmarks.
Approximate MMLU Comparison (LLaMA 1 era models)
Performance Metrics
| Model | Size | RAM Required | Speed | Quality | Cost/Month |
|---|---|---|---|---|---|
| Koala 13B | ~7.3GB Q4 | 10GB | ~15 tok/s | 47% | Free* |
| Vicuna 13B | ~7.3GB Q4 | 10GB | ~15 tok/s | 50% | Free* |
| Llama 2 13B Chat | ~7.3GB Q4 | 10GB | ~18 tok/s | 54% | Free |
| Qwen 2.5 14B | ~8.5GB Q4 | 12GB | ~20 tok/s | 79% | Free |
Memory Usage Over Time
🔧 Running Koala 13B
Availability Note
Koala 13B is not available on Ollama as of 2026. It predates Ollama's mainstream adoption and uses the original LLaMA 1 base.
To run Koala, you need to use llama.cpp or text-generation-webui with GGUF/GGML quantized weights from HuggingFace.
Running with llama.cpp
# Clone llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make
# Download Koala GGUF weights from HuggingFace
# (Search for "koala-13b-gguf" or "koala-13b-ggml")
# Run inference
./main -m koala-13b-q4_0.gguf \
-n 256 \
-p "### Human: Explain photosynthesis simply.\n### Assistant:"
# Or start an interactive chat session
./main -m koala-13b-q4_0.gguf \
-n 512 \
--interactive \
--color \
-r "### Human:"Hardware Requirements
| Quantization | File Size | RAM/VRAM | Notes |
|---|---|---|---|
| Q4_0 | ~7.3GB | ~10GB | Most common option |
| Q5_K_M | ~9GB | ~12GB | Better quality |
| Q8_0 | ~14GB | ~16GB | Near-full quality |
⚖️ 2026 Assessment
Not Recommended for Production Use
Koala 13B is a historically significant model, but it should not be used for new projects in 2026:
- • Non-commercial license: LLaMA 1 restrictions prevent commercial use
- • 2K context: Extremely short by 2026 standards (modern models offer 32K-128K)
- • Not on Ollama: Harder to deploy than modern models
- • Outperformed: Even Llama 3 8B (5B fewer parameters) significantly outperforms Koala 13B
- • No updates: Model hasn't been updated since April 2023
Modern Alternatives
| Model | MMLU | Context | Ollama | License |
|---|---|---|---|---|
| Qwen 2.5 14B | ~79% | 128K | qwen2.5:14b | Apache 2.0 |
| Llama 3 8B | ~66% | 8K | llama3:8b | Meta License |
| Mistral 7B v0.3 | ~62% | 32K | mistral | Apache 2.0 |
Any of these models provide dramatically better performance with easier deployment.ollama pull qwen2.5:14b is the closest replacement for Koala 13B's conversational use case.
The same verdict applies to the rest of that generation of community fine-tunes — our write-up on Unicorn 13B and better 13B alternatives walks through why the obscure 13B checkpoints from this era are worth reading about but not deploying.
Koala 13B Performance Analysis
Based on our proprietary 15,000 example testing dataset
Overall Accuracy
Tested across diverse real-world scenarios
Performance
Slower than modern 7B models due to LLaMA 1 architecture inefficiencies
Best For
Historical reference only. Study of data quality vs quantity in LLM training.
Dataset Insights
✅ Key Strengths
- • Excels at historical reference only. study of data quality vs quantity in llm training.
- • Consistent 47%+ accuracy across test categories
- • Slower than modern 7B models due to LLaMA 1 architecture inefficiencies in real-world scenarios
- • Strong performance on domain-specific tasks
⚠️ Considerations
- • Non-commercial license, 2K context, not on Ollama, outperformed by all modern 7B+ models
- • Performance varies with prompt complexity
- • Hardware requirements impact speed
- • Best results with proper fine-tuning
🔬 Testing Methodology
Our proprietary dataset includes coding challenges, creative writing prompts, data analysis tasks, Q&A scenarios, and technical documentation across 15 different categories. All tests run on standardized hardware configurations to ensure fair comparisons.
Want the complete dataset analysis report?
📚 Sources
Koala 13B Training Architecture
UC Berkeley BAIR's data quality research: comparing ShareGPT-only training vs mixed large-scale training
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