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 It has been fully superseded by models like Llama 3 8B, Qwen 2.5 14B, and Mistral 7B.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 | Cost/Month |
|---|---|---|---|
| Koala 13B | ~7.3GB Q4 | 10GB | Free* |
| Vicuna 13B | ~7.3GB Q4 | 10GB | Free* |
| Llama 2 13B Chat | ~7.3GB Q4 | 10GB | Free |
| Qwen 2.5 14B | ~8.5GB Q4 | 12GB | 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.
📚 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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