whatmodel.sh / Python-Code-13B
Python-Code-13B
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Python-Code-13B is an open-weights language model from ajibawa-2023, released November 2023. Its published size is 13.02B parameters. Licence: cc-by-nc-nd-4.0.
Catalogue identity notes
Maker repo ajibawa-2023/Python-Code-13B @f8a8fc8: licence tag cc-by-nc-nd-4.0. 13,015,866,880 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2023-11: month the maker's Hugging Face repo was created (createdAt 2023-11-11).
$ how it compares
The LLMs ranked either side of Python-Code-13B, out of 1132 with a score, then more from ajibawa-2023.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 981 |  Llama 2 13B (base) | 34±1 | local | ||
| 982 |  GPT-3.5 text-davinci-003 | 34±3 | comparison only | ||
| 983 | Python-Code-13B | 34±1 | local | ||
| 984 | Code Llama 7B (base) | 34±1 | local | ||
| 985 |  Nous-Hermes-13b | 34±2 | local | ||
| more from ajibawa-2023 | |||||
| 753 | Code-290k-6.7B-Instruct | 44±1 | local | ||
| 836 | Code-13B (ajibawa-2023) | 42±1 | local | ||
| 852 | Code-33B (ajibawa-2023) | 41±1 | local | ||
$ what it's good at
coding▁▁▁▃▆██▇▆▄▃▁ #427 of 469 * · 100% of its score · 2 tests
Beats DeepSeek R1 Distill Qwen 7B, Qwen3.5 2B, DeepSeek R1 Distill Llama 8B here, though they rank higher overall. Trails Llama 3.2 1B Instruct, SmolLM2 1.7B Instruct, Granite 3B Code Base (2K) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| HumanEval | 32.9% ✎ | ▁·▁▁▃▄▆▅█▃▁▁ beats 17/99 | 60% |
| HumanEval+ | 30.5% ✎ | ▁·▁▁▁▃▄▄▄█▃▁ beats 21/99 | 40% |
$ where to run it
llama.cpp · recommended
version
- Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.macOS / Linux
curl -LsSf https://llama.app/install.sh | shWindowsirm https://llama.app/install.ps1 | iex - Run Python-Code-13B · needs ~7.9 GB · from huggingface.co/TheBlokechat in the terminal
llama cli -hf TheBloke/Python-Code-13B-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf TheBloke/Python-Code-13B-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run Python-Code-13B · needs ~7.9 GB · from huggingface.co/TheBloke
- Or download it in the terminal
lms get https://huggingface.co/TheBloke/Python-Code-13B-GGUF@Q4_K_M -y - Then run lms ls to see its name, and lms chat followed by that name
Ollama
version
- Install Ollama
download it (macOS / Windows) ↗
Linuxcurl -fsSL https://ollama.com/install.sh | sh - Run Python-Code-13B · needs ~7.9 GB · from huggingface.co/TheBlokechat in the terminal
ollama run hf.co/TheBloke/Python-Code-13B-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~26.03 GB · from huggingface.co/ajibawa-2023 (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='ajibawa-2023/Python-Code-13B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/ajibawa-2023 ↗
$ how the score was worked out · EvalPlus / EvalPerf published… 100%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 EvalPlus / EvalPerf published evaluation runs | 2 | 100% |
| prior · pull toward the middle | 0% | |
| weighted average | = 34.0 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json