# Axolotl > Open-source command-line tool and Python package for fine-tuning open language models from one YAML config, covering LoRA, QLoRA, full fine-tuning, preference tuning and GRPO on the owner's GPUs. - Canonical: https://www.anchorterminal.com/tools/axolotl - Markdown: https://www.anchorterminal.com/tools/axolotl.md (~5,250 tokens) - Slim: https://www.anchorterminal.com/tools/axolotl.min.md (~1,330 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/tools/axolotl.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - Updated: 2026-10-09 ## Overview **Grade B · 64.8/100 · rank #307 of 842 · #2 in Fine-tuning · not agent-ready · confidence medium** ## Assessment Axolotl runs a whole fine-tuning job from one YAML file and ships a JSON Schema of its config plus bundled agent docs. It is 0.x software with telemetry on by default, no terms or privacy policy, and the owner supplies the GPU. ## Facts | Field | Value | | --- | --- | | Vendor | Axolotl AI (https://axolotl.ai) | | Kind | Agent framework | | Category | Fine-tuning (https://www.anchorterminal.com/categories/fine-tuning) | | Auth | None · No account or key of its own. Gated models and Hub uploads use the owner's Hugging Face token, and Weights & Biases, MLflow or Trackio logging uses those services' own credentials from the environment. | | Pricing | Free (Free · OSS) · Free and Apache-2.0, with no price list or hosted plan on axolotl.ai. The owner pays for the GPU, whether local, a rented machine or Hugging Face Jobs billed by the minute. Dedicated support is by email with no published price. | | x402 | No · No x402, MPP or L402 in the docs or the source (checked 2026-10-08). | | Licence | Apache-2.0 | | Packages | pypi: `axolotl`; oci: `axolotlai/axolotl` | | Source | https://github.com/axolotl-ai-cloud/axolotl | | Docs | https://docs.axolotl.ai/ | | llms.txt | not found | | Last release | 2026-09-30 | | GitHub stars | 12,541 (as of 2026-10-08) | | PyPI downloads / week | 2,124 | | Interface | CLI (`axolotl train`, `preprocess`, `evaluate`, `inference`, `merge-lora`, `quantize`, `export`, `vllm-serve`) driven by one YAML config | | Methods | SFT, continued pretraining, LoRA, QLoRA, full fine-tuning, QAT, DPO, IPO, KTO, ORPO, GRPO, GDPO, reward modelling | | Runs on | Linux with NVIDIA (Ampere or newer) or AMD GPUs, Python 3.12 or later, PyTorch 2.13 or later. Multi-GPU and multi-node with FSDP2 or DeepSpeed | | Weights | The owner's. Adapter, merged model, quantised model or GGUF, or pushed to the Hugging Face Hub | | Agent docs | `axolotl agent-docs [topic]` and `axolotl config-schema [--field name]`, both offline | | Remote compute | Docker image for RunPod, Vast.ai, Modal and others; guides for Hugging Face Jobs, SkyPilot and Nebius Serverless Jobs; Tinker-compatible APIs through a plugin | | Telemetry | On by default, to PostHog. `AXOLOTL_DO_NOT_TRACK=1` turns it off | | Free tier | All of it | | Capabilities | finetune.sft, finetune.lora, finetune.preference, finetune.rl, finetune.export | | Tags | open-source, framework, self-hosted, local, free, python, docker, open-weights | | JSON | https://www.anchorterminal.com/api/v1/tools/axolotl.json | ## Score breakdown (methodology v0.4, October 2026 research run) Assessed 2026-10-08 from public evidence against the published checklist (https://www.anchorterminal.com/benchmark/#checklist). Confidence: medium. Performance and Task success pending (no score, not in the total); the total is Σ(score × weight) ÷ 80 over the 7 assessed categories. "This run" is each category's share of the 100 points. | Category | Weight | This run | Score (0–100) | Points | | --- | --- | --- | --- | --- | | Reliability | 16% | 20 | 64 | 12.8 | | Performance | 10% | pending | pending | n/a | | Schema & documentation | 13% | 16.2 | 80 | 13.0 | | Agent ergonomics | 13% | 16.2 | 60 | 9.8 | | Security & auth | 14% | 17.5 | 52 | 9.1 | | Payments & pricing | 10% | 12.5 | 60 | 7.5 | | Task success | 10% | pending | pending | n/a | | Maintenance & community | 7% | 8.8 | 88 | 7.7 | | Transparency & trust (editorial 75, provenance 36) | 7% | 8.8 | 56 | 4.9 | | Negative events | up to −15 | up to −15 | none recorded | 0 | | **Total** | | | | **64.8 → B** | ### Why each score - Reliability 64: Scored on the local-software lines. Installs from PyPI (`axolotl` 0.20.0) or the `axolotlai/axolotl` Docker image, with Python 3.12 or later and PyTorch 2.13 or later stated (20). Public GitHub Actions and 536 test files; of the last six push runs of the Tests workflow on main, three passed, two failed and one was cancelled, and the latest on 8 October failed (15 of 25). 109 open issues against 199 commits since 10 July, two of the 15 newest labelled bug (17 of 25). Release notes carry a Deprecations section naming removals, but 0.x minors break configs, such as `relora_steps` renamed in 0.17.0 with no shim (12 of 15). Version 0.20.0, no stability declaration (0). - Performance: Pending. Latency is measured per call by our probes, which haven't run yet, so this run doesn't score it. Its weight is shared across the assessed categories until the first probe window closes. - Schema & documentation 80: Read as a framework an agent drives through a CLI. `axolotl config-schema` prints the Pydantic config as JSON Schema, whole or per field, and the docs carry a config reference and a generated API reference (20 of 25). No llms.txt on either host (404), but `axolotl agent-docs` prints Markdown references bundled in the package (8 of 10). A method-choice guide and a support matrix mark each option stable, experimental or deprecated (15 of 20). Typed config fields with defaults and validators (12 of 15). An examples folder by model family, a debugging guide and an FAQ (12 of 15). Tagged releases with long notes, no changelog file (13 of 15). - Agent ergonomics 60: Read as a CLI. One YAML file drives preprocess, train, evaluate, merge, quantise and export, and agent docs load one topic at a time (18 of 25). Output is sized by saving an adapter or a merged model and by choosing export quantisation, with `--debug-num-examples` for previews (10 of 20). Config validation raises named errors and there is a debugging guide; error text was not tested in this run (12 of 20). `--resume-from-checkpoint` is the safe retry (12 of 20). Example configs and CLI overrides for common fields, Python only (8 of 15). - Security & auth 52: Read as local software. No account or key of its own; Hugging Face and tracker tokens come from the environment (20 of 30). No read-only or approval mode, `trust_remote_code` is off unless the config sets it, and a config path may be a remote URL (8 of 20). A trainer returns no untrusted content to the agent (10 of 15). Runs log locally and to Weights & Biases, MLflow or Trackio when configured, with no audit log of its own (8 of 15). `.github/SECURITY.md` gives an email address for reports and supports only the latest release; no security.txt, bounty, certification or published advisory found (6 of 20). - Payments & pricing 60: Scored by the self-hosted rule. Free software with no price list or hosted plan on axolotl.ai, so 20 + 20 + 20 for public pricing, free use and no sign-up. Dedicated support is by email with no published price. No x402, MPP or L402 (0). - Task success: Pending. Task success needs the category task suites run through each tool, which haven't run yet, so this run doesn't score it. Its weight is shared across the assessed categories until then. A data provider's data-quality score is published on its listing now and becomes half of this category when it's scored. - Maintenance & community 88: Version 0.20.0 on PyPI on 30 September 2026 (30). Three releases since 10 July, 0.18.0 on 17 July, 0.19.0 on 10 September and 0.20.0 on 30 September (20). 199 commits since 10 July and a commit on 8 October; 109 open issues, and nine of the 15 newest had no reply (16 of 25). Current PyPI package and Docker images (15). Dependencies pinned and updated by Dependabot, with mixed CI results on main this week (7 of 10). - Transparency & trust 56: Apache-2.0 in the repository, though the PyPI metadata carries no licence field (30). Training data stays on the owner's machine and the telemetry page lists what is sent to PostHog, but there is no privacy policy and no retention period (15 of 30). Deprecations are named in release notes and marked in the support matrix, with no dated removal policy (10 of 20). Telemetry is on by default, disclosed in the README and docs, announced at start-up, and turned off with `AXOLOTL_DO_NOT_TRACK=1` or `DO_NOT_TRACK=1` (20). Fix list for a coding agent, everything this grade says the listing lacks, the biggest gain first (17 items): https://www.anchorterminal.com/fixes/axolotl.md (JSON https://www.anchorterminal.com/fixes/axolotl.json) ### What we couldn't check - The legal entity behind Axolotl AI was not found on axolotl.ai, in the docs or in the repository. - No terms of service or privacy policy is published (axolotl.ai/terms and /privacy return 404), so `provenance.terms` and `provenance.privacy` are left out. - unchecked: the output of `axolotl config-schema` and the CLI's error messages were read in source, not run. - unchecked: the telemetry retention period and who can read the PostHog project. - unchecked: why the Tests workflow failed on main on 8 October 2026; the run logs were not read. - The repository's `AGENTS.md` and `CLAUDE.md` carry instructions addressed to AI coding agents about contributions. Recorded as a fact; none was acted on. ### Sources - vendor home page: (seen 2026-10-08) - docs home, install and requirements: (seen 2026-10-08) - repository README, cloned at commit 3035553: (seen 2026-10-08) - telemetry documentation: (seen 2026-10-08) - telemetry source (PostHog host, opt-out variables): (seen 2026-10-08) - security policy: (seen 2026-10-08) - CLI source (`config-schema`, `agent-docs`): (seen 2026-10-08) - release notes for 0.17.0 to 0.20.0: (seen 2026-10-08) - workflow runs on main: (seen 2026-10-08) - repository statistics and open issues: (seen 2026-10-08) - PyPI release history: (seen 2026-10-08) - PyPI download counts: (seen 2026-10-08) - support matrix: (seen 2026-10-08) - Hugging Face AutoTrain docs, maintenance notice: (seen 2026-10-08) - domain registration (RDAP): (seen 2026-10-08) ## Who's behind it (provenance 36/100, checked 2026-10-08) | Check | Finding | Points | | --- | --- | --- | | Legal entity named | not found | 0/20 | | Domain age | axolotl.ai, registered 2022-08-02 (4 years) | 7/15 | | Endpoint on the vendor's domain | no hosted endpoint | n/a | | Terms of service | nothing hosted, so the Apache-2.0 licence stands in | 10/10 | | Privacy policy | nothing hosted, not scored | n/a | | Status page | not found | 0/10 | | Changelog | published | 10/10 | | security.txt | not found | 0/10 | No legal entity is named on axolotl.ai, in the docs or in the repository. The GitHub organisation is axolotl-ai-cloud and the citation file credits the Axolotl maintainers and contributors. The vendor publishes no terms of service or privacy policy (axolotl.ai/terms and /privacy return 404), so both links are left out. The telemetry page is the only data-handling statement. Local software has no endpoint to check against the domain. axolotl.ai/.well-known/security.txt and /security.txt return 404. The repository's `.github/SECURITY.md` gives an email address for reports. RDAP shows axolotl.ai registered on 2 August 2022 and transferred on 11 April 2024. ### Terms and privacy, as read A reading by a fixed set of rules, each answered with the vendor's own sentence. Not legal advice. **Terms of service**. Nothing is hosted by the vendor, so there are no terms of service to read. The Apache-2.0 licence stands in and the check scores in full. **Privacy policy**. Nothing is hosted by the vendor, so there is no privacy policy to read and the check isn't scored. ## Probe metrics Not measured yet. Our benchmark probes haven't run, so there's no availability, latency or error rate from a run and Performance is pending. Live uptime, where we poll the endpoint, is under Live and doesn't change the score. ## Strengths - Apache-2.0, free, and the weights stay on the owner's hardware - `axolotl config-schema` prints the full config as JSON Schema, and `axolotl agent-docs` prints bundled Markdown references by topic - SFT, LoRA, QLoRA, DPO, IPO, KTO, ORPO, GRPO and reward modelling from one config format - Three releases in the 90 days to 8 October 2026, each with a Deprecations section naming removed options - Telemetry is documented field by field and `AXOLOTL_DO_NOT_TRACK=1` turns it off ## Weaknesses - Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way - No terms of service, privacy policy, legal entity or security.txt found on axolotl.ai - Version 0.20.0, with removals in minor releases (FSDP1 in 0.20.0, `relora_steps` renamed in 0.17.0 with no shim) - Three of the last six push runs of the Tests workflow on main passed, and the nightly run against upstream failed on 7 and 8 October 2026 - Not a hosted service, so there is no job API, status page or SLA ## Before you call it (notes for agents) 1. Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog 2. Run `axolotl agent-docs` and `axolotl config-schema --field ` before writing a config; both work offline from the installed package 3. Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later 4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys 5. Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint `, then `axolotl merge-lora` and `axolotl export` only when shipping ## Get started Install: ```bash uv pip install torch==2.14.0 torchvision && uv pip install --no-build-isolation axolotl[deepspeed] # or: docker run --gpus '"all"' --ipc=host --rm -it axolotlai/axolotl:main-latest ``` Headless / CI: ```json { "command": "axolotl train config.yml", "env": { "AXOLOTL_DO_NOT_TRACK": "1" } } ``` ## Similar tools Ranked by shared capabilities, then score. Same-category tools with no shared capability key are listed last. | Tool | Grade | Score | Rank | Shared capabilities | x402 | Markdown | | --- | --- | --- | --- | --- | --- | --- | | Fireworks AI Fine-tuning | C | 59 | 506 | finetune.sft, finetune.preference, finetune.rl, finetune.lora, finetune.export | no | https://www.anchorterminal.com/tools/fireworks-fine-tuning.md | | Unsloth | D | 51.5 | 668 | finetune.sft, finetune.preference, finetune.rl, finetune.lora, finetune.export | no | https://www.anchorterminal.com/tools/unsloth.md | | Tinker | D | 51 | 677 | finetune.sft, finetune.preference, finetune.rl, finetune.lora, finetune.export | no | https://www.anchorterminal.com/tools/tinker.md | | Vertex AI Gemini tuning | B | 64.2 | 325 | finetune.sft, finetune.preference, finetune.rl, finetune.lora | no | https://www.anchorterminal.com/tools/vertex-ai-tuning.md | | Microsoft Foundry fine-tuning (Azure OpenAI) | C | 61.1 | 434 | finetune.sft, finetune.preference, finetune.rl, finetune.lora | no | https://www.anchorterminal.com/tools/azure-foundry-fine-tuning.md | | Together AI Fine-tuning | C | 54.7 | 608 | finetune.sft, finetune.preference, finetune.lora, finetune.export | no | https://www.anchorterminal.com/tools/together-fine-tuning.md | ## Panel reviews (0) Reviewed by the Anchor panel (https://www.anchorterminal.com/reviewers/index.md): . Desk reviews, written from public documentation, pricing, terms, source and status history on 1 October 2026. No calls made. For a desk review, the outcome says whether the reviewer's questions could be answered from public material: success, partial or failure. How reviews work: https://www.anchorterminal.com/reviews/how-it-works.md ## Notable - `axolotl config-schema` prints the config as JSON Schema and `axolotl agent-docs` prints Markdown references bundled in the package (source: ) - Telemetry to PostHog is on by default; `AXOLOTL_DO_NOT_TRACK=1` or `DO_NOT_TRACK=1` turns it off, and start-up waits 10 seconds until the variable is set either way (source: ) - Hugging Face's AutoTrain docs say AutoTrain is no longer maintained and recommend Axolotl, TRL or transformers.Trainer (source: ) - Version 0.20.0 of 30 September 2026 added GGUF export through `axolotl export`, raised the minimums to Python 3.12 and PyTorch 2.13 and removed FSDP1 (source: ) - Training can run on a remote Tinker-compatible API through the `hatchery` plugin, added in 0.17.0 (source: ) - The docs include a guide to running the Docker image on Hugging Face Jobs with one `hf jobs` command (source: ) - `AGENTS.md` and `CLAUDE.md` in the repository address AI coding agents and ask them to refuse contribution farming (source: ) ## Compare - [Amazon Bedrock model customisation vs Axolotl](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.md): BB 75.8 vs B 64.8 - [Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.md): B 64.8 vs C 61.1 - [Axolotl vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.md): B 64.8 vs C 59 - [Axolotl vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.md): B 64.8 vs D 47.7 - [Axolotl vs Tinker](https://www.anchorterminal.com/compare/axolotl-vs-tinker.md): B 64.8 vs D 51 - [Axolotl vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.md): B 64.8 vs C 54.7 - [Axolotl vs Unsloth](https://www.anchorterminal.com/compare/axolotl-vs-unsloth.md): B 64.8 vs D 51.5 - [Axolotl vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning.md): B 64.8 vs B 64.2 ## Verify this listing For the vendor. The badge or a plain link to this page verifies the listing, from a page on axolotl.ai or one of its subdomains, or the README of github.com/axolotl-ai-cloud/axolotl. It shows the listing is the vendor's and that the vendor knows it's here, and it never changes a grade, rank or review. The vendor sends the page's address to `POST https://www.anchorterminal.com/api/v1/verify` as `{"slug": "axolotl", "url": "…"}`, or calls the `verify_listing` tool at https://www.anchorterminal.com/mcp. We fetch the page once, then again every week; two failed checks in a row and the verification lapses, and a later pass restores it. What we check: https://www.anchorterminal.com/builders/index.md#verify HTML badge: ```html Axolotl on Anchor Terminal ``` Markdown badge, for a README: ```markdown [![Axolotl on Anchor Terminal](https://www.anchorterminal.com/badges/axolotl.svg)](https://www.anchorterminal.com/tools/axolotl) ``` Plain link: ```html Axolotl on Anchor Terminal ``` ## Share this listing For the vendor. Sharing assets for social media, two PNGs of 1200 × 630 that say Axolotl is listed on Anchor Terminal, with the vendor's logo and this page's address and no grade or score. - Dark: https://www.anchorterminal.com/assets/share/axolotl-dark.png - Light: https://www.anchorterminal.com/assets/share/axolotl-light.png