CausalLM 14B

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CausalLM 14B is an open-weights language model from CausalLM, released October 2023. Licence: wtfpl.

Catalogue identity notes

Maker repo CausalLM/14B @cc054cf: licence tag wtfpl. Released 2023-10: month the maker's Hugging Face repo was created (createdAt 2023-10-22).

39.4±2.0#722 of 949 LLMsfrom 2 benchmark results across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of CausalLM 14B, out of 1132 with a score.

#	model	score	where	RAM	released
883	OpenHermes 2.5 Mistral 7B Nous Research	39±1	local	~6.1GB	2023-10
884	Granite 3.3 2B Instruct IBM	39±1	local	~3GB	2025-04
885	CausalLM 14B CausalLM	39±2	local	~10GB	2023-10
886	Phi-3-mini-128k-instruct (Apr 2024) Microsoft	39±2	local	~9.5GB	2024-04
887	Mistral 7B Instruct v0.3 Mistral	39±1	local	~5.8GB	2024-05

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▄▆▅▆█▆▆▆▂ #392 of 466 * · 98% of its score · 2 tests

Beats OpenThinker3 7B, Gemma 4 E2B, Code Llama 70B Instruct here, though they rank higher overall. Trails Granite 4.0 H 350M, LFM2 350M, Gemma 3 1B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
AlpacaEval 2 length-controlled	15.7% ✎	▁▂▂▃▆▆█▆▄▄▁▁ beats 40/122	medium · avg 39%	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 87%	typical
AlpacaEval 1.0 win rate (vs text-davinci-003, GPT-4 judge)	88.3% ✎	▂▂·▆·▇▃▆█▆▄▂ beats 27/45	very easy · avg 96%	▇▇▇▇▇ 11%	sharp · noisy

$ 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 CausalLM 14B · needs ~8.2 GB · from huggingface.co/TheBloke
chat in the terminal
llama cli -hf TheBloke/CausalLM-14B-GGUF:Q4_0
Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf TheBloke/CausalLM-14B-GGUF:Q4_0

LM Studio

version

Install LM Studio · Open the app once before using the lms command.
download it (macOS / Windows / Linux) ↗
Run CausalLM 14B · needs ~8.2 GB · from huggingface.co/TheBloke
open in LM Studio ↗
Or download it in the terminal
lms get https://huggingface.co/TheBloke/CausalLM-14B-GGUF@Q4_0 -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 CausalLM 14B · needs ~8.2 GB · from huggingface.co/TheBloke
chat in the terminal
ollama run hf.co/TheBloke/CausalLM-14B-GGUF:Q4_0

Other ways to run it

Run it as published · needs ~28.33 GB · from huggingface.co/CausalLM (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='CausalLM/14B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/CausalLM ↗

$ how the score was worked out · AlpacaEval 1.0 and 2.0 leader… 98%

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
AlpacaEval 1.0 and 2.0 leaderboards (tatsu-lab/alpaca_eval repository)	2	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%	Apache-2.0
prior · pull toward the middle		▇ 2%
weighted average		= 39.4

$ 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