RZN-T

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RZN-T is an open-weights language model from Phronetic AI, released June 2025. Its published size is 0.6B parameters. Licence: apache-2.0.

Catalogue identity notes

Maker repo phronetic-ai/RZN-T @a7e084b: licence tag apache-2.0. 596,049,920 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-06: month the maker's Hugging Face repo was created (createdAt 2025-06-03).

43.0±5.2#642 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of RZN-T, out of 1132 with a score.

#	model	score	where	RAM	released
797	Code Llama 70B (base) Meta	43±1	local	~28GB	2024-01
798	Infinity-Instruct-3M-0625-Qwen2-7B BAAI	43±2	local	~6.4GB	2024-07
799	RZN-T Phronetic AI	43±5	local	~1.9GB	2025-06
800	PLLuM-12B-chat (2412) PLLuM consortium (CYFRAGOVPL)	43±3	local	~9.4GB	2025-02
801	Gemma 3n E2B IT Google	43±2	local	~4.7GB	2025-06

see all LLMs · find the ones that fit your machine

$ what it's good at

agent tasks▁▁▃▆█▆▅▄▄▃▂▁ #306 of 334 * · 88% of its score · 1 test

Beats Magistral Small 1.0 (2506), Bielik 11B v2.3 Instruct, Mixtral 8x22B Instruct v0.1 here, though they rank higher overall. Trails Granite 4.0 H 350M, Qwen3 0.6B, Qwen2.5 1.5B Instruct here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
BFCL V4 (December 2025 archive)	22.3% ✎	▁▁▁▂██▅▅▆▂▂▂ beats 9/77	hard · avg 32%	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 88%	typical

$ 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 | sh
Windows
irm https://llama.app/install.ps1 | iex
other ways to install ↗
Run RZN-T · needs ~0.4 GB · from huggingface.co/mradermacher
chat in the terminal
llama cli -hf mradermacher/RZN-T-GGUF:Q4_K_M
Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/RZN-T-GGUF:Q4_K_M

LM Studio

version

Install LM Studio · Open the app once before using the lms command.
download it (macOS / Windows / Linux) ↗
Run RZN-T · needs ~0.4 GB · from huggingface.co/mradermacher
open in LM Studio ↗
Or download it in the terminal
lms get https://huggingface.co/mradermacher/RZN-T-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) ↗
Linux
curl -fsSL https://ollama.com/install.sh | sh
Run RZN-T · needs ~0.4 GB · from huggingface.co/mradermacher
chat in the terminal
ollama run hf.co/mradermacher/RZN-T-GGUF:Q4_K_M

Other ways to run it

Run it as published · needs ~2.38 GB · from huggingface.co/phronetic-ai (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='phronetic-ai/RZN-T', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/phronetic-ai ↗

$ how the score was worked out · Berkeley Function Calling Lea… 88%

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	licence
Berkeley Function Calling Leaderboard result archive	1	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 88%	Apache-2.0
prior · pull toward the middle		▇▇▇▇ 12%
weighted average		= 43.0

$ improve this page

Update model information Add a place to run it Add a test result Propose a benchmark Correct a benchmark score Edit data on GitHub

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 2026-10-11 02:20 UTC · sources and licences · json