LlamaFactory
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
Runssource: GitHubhomepagePythonApache-2.0commit d6bb97ddff5d
Python, Apache-2.0 licensed. The project labels itself: agent, ai, deepseek, fine tuning, gemma, gpt, instruction tuning and large language models.
LlamaFactory runs. An Argusic agent installed it in 30 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
LLaMA Factory 0.9.6.dev0 is installed with all dependencies, CLI responds to all subcommands, LlamaBoard Web UI serves HTTP 200 on port 7860, and 325 automated tests pass on CPU (GPU-dependent tests skipped).
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 97.3 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- 30 minutes
- Cold machine to finish
- 14 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- pip install -e . succeeded; llamafactory-cli version printed version 0.9.6.dev0; pytest tests/ (254 passed, 14 skipped, 2 xfailed, 4 xpassed) and tests_v1/ (71 passed, 9 skipped, 1 xfailed); curl http://127.0.0.1:7860 returned HTTP 200 from LlamaBoard Web UI
- Model tokens used
- 119,274
- Exact commit tested
- d6bb97ddff5d
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
Watch the session
The whole run, from a clean machine, at the speed it actually happened. Nothing is cut and nothing is sped up.
A recording of attempt 2, run , the attempt that decided the verdict above. The terminal recording it was made from is the evidence, stored with a sha256 fingerprint; the video is a rendering of it. Nothing loads from the video host until you press play.
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
exec /bin/sh -lc 'echo "Total elapsed: $(date -d @$(echo "$(date +%s) - $(stat -c %Y /proc/1/cmdline 2>/… succeeded in 0ms: /bin/sh: 1: bc: not found Total elapsed: 1789360132 cleaned exec /bin/sh -lc 'date +%s' in /work/repo succeeded in 0ms: 1789360137 tokens used 119,274
Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Nothing broke on the way: zero errors between clone and running.
- Ran without a GPU, so it does not need one to start.
- Licensed Apache-2.0, as reported by its host.
- Measured 3 times, so the result is not a one-off.
What did not, or is not known
- Took 30 minutes to install, slower than the median of the 32 comparable projects Argusic has measured.
Of the 33 agent projects Argusic has installed and timed, LlamaFactory was the 30th fastest to reach a running state, and 25 of 33 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- Following its own README: the documented path worked with nothing to fix.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
Topics (from GitHub)
agentaideepseekfine-tuninggemmagptinstruction-tuninglarge-language-modelsllamallama3llmloramoenlppeftqloraquantizationqwenrlhftransformers
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/llamafactory)Questions
- Does LlamaFactory run?
- Yes. LlamaFactory runs. Argusic installed and launched it on a clean machine in 30 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test LlamaFactory?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit d6bb97ddff5d. 3 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test LlamaFactory?
- The run that produced this verdict cost $0.06: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does LlamaFactory take to install?
- 30 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does LlamaFactory need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing LlamaFactory?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does LlamaFactory compare with the alternatives?
- Of the 33 agent projects Argusic has installed and timed, LlamaFactory was the 30th fastest to reach a running state, and 25 of 33 reached one at all.
- Where is the evidence for LlamaFactory?
- All 3 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.