{ "data": { "a": { "slug": "axolotl", "name": "Axolotl", "vendor": "Axolotl AI", "vendorUrl": "https://axolotl.ai", "kind": "framework", "category": "fine-tuning", "summary": "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.", "url": "https://www.anchorterminal.com/tools/axolotl", "markdownUrl": "https://www.anchorterminal.com/tools/axolotl.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/axolotl.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/axolotl.json", "repo": "https://github.com/axolotl-ai-cloud/axolotl", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "pypi", "name": "axolotl" }, { "registry": "oci", "name": "axolotlai/axolotl" } ], "auth": "none", "authNotes": "No account or key of its own. Gated models and Hub uploads use the owner's Hugging Face token, and Weights \u0026 Biases, MLflow or Trackio logging uses those services' own credentials from the environment.", "pricing": "free", "pricingNotes": "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.", "priceSummary": "Free · OSS", "where": "library", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs or the source (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 12541, "npmWeekly": null, "pypiWeekly": 2124, "asOf": "2026-10-08" }, "docsUrl": "https://docs.axolotl.ai/", "capabilities": [ "finetune.sft", "finetune.lora", "finetune.preference", "finetune.rl", "finetune.export" ], "tags": [ "open-source", "framework", "self-hosted", "local", "free", "python", "docker", "open-weights" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 64.8, "grade": "B", "agentReady": false, "rank": 307, "ranked": true, "rankOf": 842, "categoryRank": 2, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 60, "maintenance": 88, "payments": 60, "reliability": 64, "schema": 80, "security": 52, "transparency": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": 0, "verdict": "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.", "bestFor": "A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.", "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" ], "agentNotes": [ "Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog", "Run `axolotl agent-docs` and `axolotl config-schema --field \u003cname\u003e` before writing a config; both work offline from the installed package", "Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later", "Take example configs from the same release tag as the installed version; minor releases remove and rename config keys", "Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint \u003cpath\u003e`, then `axolotl merge-lora` and `axolotl export` only when shipping" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 64.8 } ], "editorialScores": { "ergonomics": 60, "maintenance": 88, "payments": 60, "reliability": 64, "schema": 80, "security": 52, "transparency": 75 }, "provenanceScore": 36 }, "connect": { "install": "uv pip install torch==2.14.0 torchvision \u0026\u0026 uv pip install --no-build-isolation axolotl[deepspeed] # or: docker run --gpus '\"all\"' --ipc=host --rm -it axolotlai/axolotl:main-latest", "headless": { "command": "axolotl train config.yml", "env": { "AXOLOTL_DO_NOT_TRACK": "1" } } }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/axolotl" }, "area": "models", "provenance": { "legalEntity": "", "domain": "axolotl.ai", "domainRegistered": "2022-08-02", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://github.com/axolotl-ai-cloud/axolotl/releases", "securityTxt": "none", "checked": "2026-10-08", "notes": [ "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." ], "score": 36 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/axolotl.json" }, "answer": "Axolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust.", "b": { "slug": "nebius-token-factory-fine-tuning", "name": "Nebius Token Factory fine-tuning", "vendor": "Nebius", "vendorUrl": "https://nebius.com", "kind": "http-api", "category": "fine-tuning", "summary": "Nebius Token Factory runs supervised fine-tuning jobs on open models such as Llama, Qwen, gpt-oss, Gemma and DeepSeek through an OpenAI-compatible REST API, with LoRA or full weights and downloadable checkpoints.", "url": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning", "markdownUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json", "repo": "https://github.com/nebius/token-factory-cookbook", "license": "Proprietary service (cookbook examples MIT)", "transports": [ "http" ], "remoteUrl": "https://api.tokenfactory.nebius.com/v1", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with an API key created by a person in the console under API keys, shown once and read from `NEBIUS_API_KEY` in the docs. Keys belong to a project. Project Admins and Members both have full access to the Files and Fine-tuning APIs, and no per-key scopes or expiry were found in the reviewed documentation.", "pricing": "usage", "pricingNotes": "Pay as you go, with no monthly fee stated. No fine-tuning price was found in the docs or in the public catalogue at `/api/public/models_info`. The product page sends readers to the Token Factory console's prices page, a script-drawn console page that robots.txt disallows, so we did not read it. A bank card is mandatory at onboarding. New accounts get $1 of trial credit valid for 30 days (https://docs.tokenfactory.nebius.com/other-capabilities/billing-new.md). Dedicated endpoints bill while one or more replicas are ready.", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the OpenAPI file or the billing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://docs.tokenfactory.nebius.com/post-training/overview", "llmsTxt": "https://docs.tokenfactory.nebius.com/llms.txt", "openapi": "https://api.tokenfactory.nebius.com/openapi.json", "capabilities": [ "finetune.sft", "finetune.lora", "finetune.export" ], "tags": [ "hosted", "usage-priced", "card-required", "open-weights", "llms-txt", "openapi", "async-jobs" ], "lastRelease": "2026-09-30", "graded": true, "anchor": { "graded": true, "score": 47.7, "grade": "D", "agentReady": false, "rank": 738, "ranked": true, "rankOf": 842, "categoryRank": 9, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 51, "maintenance": 61, "payments": 0, "reliability": 45, "schema": 68, "security": 52, "transparency": 79 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": -2, "negativeNotes": [ "-2: on 2026-10-08 the product page said a fine-tuned model goes live with one click on 'serverless endpoints, on-demand GPU, or dedicated enterprise clusters', while the docs say deployment is by dedicated endpoints only and custom model weights are in beta on request, and the guide's deployment links return 404 (https://nebius.com/services/token-factory/fine-tuning, https://docs.tokenfactory.nebius.com/post-training/models.md)" ], "verdict": "Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request.", "bestFor": "Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.", "strengths": [ "49 base models listed, 42 with LoRA and full fine-tuning and 7 with full fine-tuning only, at context lengths from 8,192 to 131,072 tokens", "Checkpoint files download through `GET /v1/files/{file_id}/content`, and an `hf` integration pushes the result to a Hugging Face repository", "A public OpenAPI 3.1 file covers the fine-tuning, files, datasets and operations paths, with ranges on every hyperparameter", "The legal guide says content is not used to train models and that customers own the models they fine-tune", "A dated sub-processor list for Token Factory, with 15 days' notice of changes, and a valid security.txt on nebius.com" ], "weaknesses": [ "No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows", "The models page says deployment is by dedicated endpoints only, and custom model weights are in beta and available on request", "A bank card is mandatory at onboarding, so the $1 trial credit (30 days) is not a card-free trial", "Two major incidents tagged Token Factory in 90 days, 93 minutes on 27 July and about 21.5 hours in us-central1 from 19 August 2026", "No public changelog, no idempotency key on job creation and only a 422 response documented in the reference", "The Services Agreement (clause 4.1.10) forbids competitive analysis or benchmarking" ], "agentNotes": [ "Use the OpenAI client with `base_url` `https://api.tokenfactory.nebius.com/v1/` and `NEBIUS_API_KEY`. Upload JSONL with `purpose=fine-tune`, then create the job.", "Set `hyperparameters.lora` to true for an adapter. The default is false, which runs full fine-tuning.", "Poll `GET /v1/fine_tuning/jobs/{job_id}` no faster than every 15 seconds. There is no idempotency key, so list jobs before recreating one after a timeout.", "Download every ID in a checkpoint's `result_files` before relying on hosted copies. The terms allow deletion of tuned models at three days' notice.", "The spec requires `wandb.api_key` although the guide omits it, and it also accepts `mlflow` and `hf` integrations. Check the price in the console before starting a job." ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 47.7 } ], "editorialScores": { "ergonomics": 51, "maintenance": 61, "payments": 0, "reliability": 45, "schema": 68, "security": 52, "transparency": 70 }, "provenanceScore": 88 }, "connect": { "install": "pip3 install --upgrade openai", "http": "curl 'https://api.tokenfactory.nebius.com/v1/fine_tuning/jobs' \\\n -X POST \\\n -H 'Accept: application/json' \\\n -H 'Content-Type: application/json' \\\n -H \"Authorization: Bearer $NEBIUS_API_KEY\" \\\n -d '{\"model\":\"meta-llama/Llama-3.1-8B-Instruct\",\"suffix\":\"my-domain-adapter\",\"training_file\":\"\u003ctraining_file_ID\u003e\",\"hyperparameters\":{\"n_epochs\":3,\"lora\":true,\"lora_r\":16,\"lora_alpha\":16}}'" }, "letme": { "capability": "https://letme.dev/finetune.sft", "tool": "https://letme.dev/nebius-token-factory-fine-tuning" }, "sameCompany": [ "nebius-ai-cloud" ], "area": "models", "provenance": { "legalEntity": "Nebius B.V.", "domain": "nebius.com", "domainRegistered": "2004-06-26", "endpointOnVendorDomain": true, "terms": "https://docs.nebius.com/legal/agreement", "privacy": "https://docs.nebius.com/legal/privacy", "statusPage": "https://status.nebius.com", "changelog": "", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The Nebius Services Agreement (published 15 September 2026, effective 28 September 2026) names Nebius B.V. as the contracting entity by default, Nebius Inc. for US customers who registered from 15 September 2026 and Nebius Israel Ltd for some customers in Israel. The parent is Nebius Group N.V.", "The Token Factory Supplemental Terms at https://docs.nebius.com/legal/token-factory are incorporated into the agreement and carry the fine-tuning clauses.", "The privacy policy (effective 23 September 2026) names Nebius Token Factory in its scope and covers data Nebius holds as controller. Customer content is covered by the DPA at https://docs.nebius.com/legal/dpa.", "security.txt at nebius.com gives security@nebius.com and expires on 31 December 2027. The same path on tokenfactory.nebius.com returns the console's HTML shell.", "status.nebius.com is an Atlassian Statuspage for the whole Nebius cloud, with a Token Factory component in each of nine regions and no separate fine-tuning component.", "No public changelog for Token Factory was found in the docs index. The OpenAPI file carries the version stamp 20260930-cfb76be12.", "nebius.com was registered on 26 June 2004 per Verisign RDAP." ], "score": 88 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.json", "live": { "slug": "nebius-token-factory-fine-tuning", "probe": { "target": "https://api.tokenfactory.nebius.com/v1", "method": "get", "lastAt": "2026-10-09T11:00:30.72469802Z", "lastOk": true, "lastStatus": 404, "lastMs": 87, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 87, "p95ms24h": 172, "samples24h": 36, "samples30d": 36, "days": [ { "date": "2026-10-09", "probes": 36, "ok": 36 } ] }, "vendorStatus": { "page": "https://status.nebius.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-09T11:03:49.658305196Z" }, "updatedAt": "2026-10-09T11:03:49.658305196Z" } }, "facts": [ { "a": "Agent framework", "b": "HTTP API", "name": "Kind" }, { "a": "Axolotl AI", "b": "Nebius", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.tokenfactory.nebius.com/v1", "name": "Hosted endpoint" }, { "a": "", "b": "HTTP", "name": "Transports" }, { "a": "None", "b": "API key", "name": "Auth" }, { "a": "Free", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Proprietary service (cookbook examples MIT)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-09-30", "b": "2026-09-30", "name": "Last release" }, { "a": "no document linked", "b": "2026-09-28", "name": "Terms last updated" }, { "a": "no document linked", "b": "2026-09-23", "name": "Privacy policy last updated" }, { "a": "", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "", "b": "not found in the text", "name": "Terms restrict automated access" }, { "a": "", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "", "b": "yes", "name": "Arbitration or class-action waiver" }, { "a": "13k stars, 2.1k PyPI/wk", "b": "none", "name": "Popularity" } ], "faq": [ { "answer": "Axolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust.", "question": "Which is better for AI agents, Axolotl or Nebius Token Factory fine-tuning?" }, { "answer": "No hosted endpoint is listed for Axolotl. Nebius Token Factory fine-tuning has a hosted endpoint at https://api.tokenfactory.nebius.com/v1.", "question": "Can an agent call Axolotl and Nebius Token Factory fine-tuning without installing anything?" }, { "answer": "Axolotl is open source (Apache-2.0). No open-source release is listed for Nebius Token Factory fine-tuning.", "question": "Are Axolotl and Nebius Token Factory fine-tuning open source?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 64 against 45", "Schema \u0026 documentation, 80 against 68", "Agent ergonomics, 60 against 51", "Payments \u0026 pricing, 60 against 0", "Maintenance \u0026 community, 88 against 61" ], "also": [ "No key needed to call it", "Open source" ], "goodFor": "A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.", "slug": "axolotl", "watchFor": "Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way" }, { "aheadOn": [ "Transparency \u0026 trust, 79 against 56" ], "also": [ "A hosted endpoint, with nothing to install" ], "goodFor": "Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.", "slug": "nebius-token-factory-fine-tuning", "watchFor": "No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows" } ], "job": { "capability": "finetune.sft", "name": "Finetune sft" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.json", "title": "Amazon Bedrock model customisation vs Axolotl", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-fine-tuning.json", "title": "Amazon Bedrock model customisation vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.json", "title": "Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)", "url": "https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.json", "title": "Axolotl vs Fireworks AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-tinker.json", "title": "Axolotl vs Tinker", "url": "https://www.anchorterminal.com/compare/axolotl-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.json", "title": "Axolotl vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-unsloth.json", "title": "Axolotl vs Unsloth", "url": "https://www.anchorterminal.com/compare/axolotl-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning.json", "title": "Axolotl vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning" }, { "json": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.json", "title": "Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning", "url": "https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker.json", "title": "Nebius Token Factory fine-tuning vs Tinker", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.json", "title": "Nebius Token Factory fine-tuning vs Together AI Fine-tuning", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth.json", "title": "Nebius Token Factory fine-tuning vs Unsloth", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth" }, { "json": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.json", "title": "Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning", "url": "https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning" } ], "scores": [ { "axolotl": 64, "by": 19, "edge": "axolotl", "key": "reliability", "name": "Reliability", "nebius-token-factory-fine-tuning": 45, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "axolotl": 80, "by": 12, "edge": "axolotl", "key": "schema", "name": "Schema \u0026 documentation", "nebius-token-factory-fine-tuning": 68, "weight": 13 }, { "axolotl": 60, "by": 9, "edge": "axolotl", "key": "ergonomics", "name": "Agent ergonomics", "nebius-token-factory-fine-tuning": 51, "weight": 13 }, { "axolotl": 52, "by": 0, "edge": "", "key": "security", "name": "Security \u0026 auth", "nebius-token-factory-fine-tuning": 52, "weight": 14 }, { "axolotl": 60, "by": 60, "edge": "axolotl", "key": "payments", "name": "Payments \u0026 pricing", "nebius-token-factory-fine-tuning": 0, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "axolotl": 88, "by": 27, "edge": "axolotl", "key": "maintenance", "name": "Maintenance \u0026 community", "nebius-token-factory-fine-tuning": 61, "weight": 7 }, { "axolotl": 56, "by": 23, "edge": "nebius-token-factory-fine-tuning", "key": "transparency", "name": "Transparency \u0026 trust", "nebius-token-factory-fine-tuning": 79, "weight": 7 } ], "summary": "Axolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust. Both do finetune sft.", "verdicts": { "axolotl": "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.", "nebius-token-factory-fine-tuning": "Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning", "json": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.md", "slim": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.min.md" }, "markdown": "Axolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust. Both do finetune sft.\n\n- Axolotl: grade B, 64.8/100, rank #307 of 842. Markdown https://www.anchorterminal.com/tools/axolotl.md · JSON https://www.anchorterminal.com/api/v1/tools/axolotl.json\n- Nebius Token Factory fine-tuning: grade D, 47.7/100, rank #738 of 842. Markdown https://www.anchorterminal.com/tools/nebius-token-factory-fine-tuning.md · JSON https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json\n\n## Which one, for what\n\n### Axolotl (B)\n\nGood for: A team that wants a repeatable, config-driven fine-tune of an open model on its own or rented GPUs, including multi-GPU and multi-node runs.\n\nAhead on:\n- Reliability, 64 against 45\n- Schema \u0026 documentation, 80 against 68\n- Agent ergonomics, 60 against 51\n- Payments \u0026 pricing, 60 against 0\n- Maintenance \u0026 community, 88 against 61\n\nAlso in its favour:\n- No key needed to call it\n- Open source\n\nWatch for: Telemetry to PostHog is on by default and delays training start by 10 seconds until the variable is set either way\n\n### Nebius Token Factory fine-tuning (D)\n\nGood for: Teams that want supervised LoRA or full fine-tuning of a wide list of open models, up to Qwen3 Coder 480B and DeepSeek, through OpenAI-style calls, with EU storage and the weights to take away.\n\nAhead on:\n- Transparency \u0026 trust, 79 against 56\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: No fine-tuning price found in the docs or the public catalogue JSON. The price page is a script-drawn console page that robots.txt disallows\n\n\n## Score by category\n\n| Category | Weight | Axolotl | Nebius Token Factory fine-tuning | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 64 | 45 | Axolotl +19 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 80 | 68 | Axolotl +12 |\n| Agent ergonomics | 13% (16.2 this run) | 60 | 51 | Axolotl +9 |\n| Security \u0026 auth | 14% (17.5 this run) | 52 | 52 | even |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 0 | Axolotl +60 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 88 | 61 | Axolotl +27 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 56 | 79 | Nebius Token Factory fine-tuning +23 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **64.8 · B** | **47.7 · D** | |\n\n## Facts side by side\n\n| Fact | Axolotl | Nebius Token Factory fine-tuning |\n| --- | --- | --- |\n| Kind | Agent framework | HTTP API |\n| Vendor | Axolotl AI | Nebius |\n| Hosted endpoint | no (local only) | `https://api.tokenfactory.nebius.com/v1` |\n| Transports | | HTTP |\n| Auth | None | API key |\n| Pricing | Free | Pay per use |\n| x402 | no | no |\n| Licence | Apache-2.0 | Proprietary service (cookbook examples MIT) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-09-30 | 2026-09-30 |\n| Terms last updated | no document linked | 2026-09-28 |\n| Privacy policy last updated | no document linked | 2026-09-23 |\n| Customer content may train models | | not found in the text |\n| Terms restrict automated access | | not found in the text |\n| Terms restrict benchmarking | | yes |\n| Terms or service can change without notice | | not found in the text |\n| Arbitration or class-action waiver | | yes |\n| Popularity | 13k stars, 2.1k PyPI/wk | none |\n\n## Verdicts\n\n**Axolotl.** 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.\n\n**Nebius Token Factory fine-tuning.** Supervised fine-tuning on 49 open base models through OpenAI-style `/v1/fine_tuning/jobs` calls, with LoRA or full weights and every checkpoint file downloadable. No fine-tuning price was found outside the script-drawn console, and the docs say tuned models deploy only to dedicated endpoints, with custom weights in beta on request.\n\n## Before you call either\n\n### Axolotl\n\n1. Set `AXOLOTL_DO_NOT_TRACK=1` before any command, or training waits 10 seconds and sends usage events to PostHog\n2. Run `axolotl agent-docs` and `axolotl config-schema --field \u003cname\u003e` before writing a config; both work offline from the installed package\n3. Install torch first, then `uv pip install --no-build-isolation axolotl[deepspeed]`, on Python 3.12 or later with PyTorch 2.13 or later\n4. Take example configs from the same release tag as the installed version; minor releases remove and rename config keys\n5. Resume an interrupted run with `axolotl train config.yml --resume-from-checkpoint \u003cpath\u003e`, then `axolotl merge-lora` and `axolotl export` only when shipping\n\n### Nebius Token Factory fine-tuning\n\n1. Use the OpenAI client with `base_url` `https://api.tokenfactory.nebius.com/v1/` and `NEBIUS_API_KEY`. Upload JSONL with `purpose=fine-tune`, then create the job.\n2. Set `hyperparameters.lora` to true for an adapter. The default is false, which runs full fine-tuning.\n3. Poll `GET /v1/fine_tuning/jobs/{job_id}` no faster than every 15 seconds. There is no idempotency key, so list jobs before recreating one after a timeout.\n4. Download every ID in a checkpoint's `result_files` before relying on hosted copies. The terms allow deletion of tuned models at three days' notice.\n5. The spec requires `wandb.api_key` although the guide omits it, and it also accepts `mlflow` and `hf` integrations. Check the price in the console before starting a job.\n\n## Questions\n\n### Which is better for AI agents, Axolotl or Nebius Token Factory fine-tuning?\n\nAxolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust.\n\n### Can an agent call Axolotl and Nebius Token Factory fine-tuning without installing anything?\n\nNo hosted endpoint is listed for Axolotl. Nebius Token Factory fine-tuning has a hosted endpoint at https://api.tokenfactory.nebius.com/v1.\n\n### Are Axolotl and Nebius Token Factory fine-tuning open source?\n\nAxolotl is open source (Apache-2.0). No open-source release is listed for Nebius Token Factory fine-tuning.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.json, and with the fewest tokens: https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"axolotl\", \"b\": \"nebius-token-factory-fine-tuning\"}`. From a terminal: `anchor compare axolotl nebius-token-factory-fine-tuning`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/axolotl.json and https://www.anchorterminal.com/api/v1/tools/nebius-token-factory-fine-tuning.json\n\n## Other comparisons with Axolotl or Nebius Token Factory fine-tuning\n\n- [Amazon Bedrock model customisation vs Axolotl](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-axolotl.md)\n- [Amazon Bedrock model customisation vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/amazon-bedrock-customization-vs-nebius-token-factory-fine-tuning.md)\n- [Axolotl vs Microsoft Foundry fine-tuning (Azure OpenAI)](https://www.anchorterminal.com/compare/axolotl-vs-azure-foundry-fine-tuning.md)\n- [Axolotl vs Fireworks AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-fireworks-fine-tuning.md)\n- [Axolotl vs Tinker](https://www.anchorterminal.com/compare/axolotl-vs-tinker.md)\n- [Axolotl vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/axolotl-vs-together-fine-tuning.md)\n- [Axolotl vs Unsloth](https://www.anchorterminal.com/compare/axolotl-vs-unsloth.md)\n- [Axolotl vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/axolotl-vs-vertex-ai-tuning.md)\n- [Microsoft Foundry fine-tuning (Azure OpenAI) vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/azure-foundry-fine-tuning-vs-nebius-token-factory-fine-tuning.md)\n- [Fireworks AI Fine-tuning vs Nebius Token Factory fine-tuning](https://www.anchorterminal.com/compare/fireworks-fine-tuning-vs-nebius-token-factory-fine-tuning.md)\n- [Nebius Token Factory fine-tuning vs Tinker](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-tinker.md)\n- [Nebius Token Factory fine-tuning vs Together AI Fine-tuning](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-together-fine-tuning.md)\n- [Nebius Token Factory fine-tuning vs Unsloth](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-unsloth.md)\n- [Nebius Token Factory fine-tuning vs Vertex AI Gemini tuning](https://www.anchorterminal.com/compare/nebius-token-factory-fine-tuning-vs-vertex-ai-tuning.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-09", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Axolotl vs Nebius Token Factory fine-tuning", "url": "" } ], "description": "Axolotl scores 64.8 (B) on agent readiness against Nebius Token Factory fine-tuning's 47.7 (D), and leads in 5 of 7 scored categories. Nebius Token Factory fine-tuning leads on transparency \u0026 trust. Both do finetune sft. Category scores, facts, verdicts and agent notes side by…", "facts": [ "Axolotl B 64.8", "Nebius Token Factory fine-tuning D 47.7", "scores" ], "h1": "Axolotl vs Nebius Token Factory fine-tuning", "image": "https://www.anchorterminal.com/assets/og/compare-axolotl-vs-nebius-token-factory-fine-tuning.png", "path": "/compare/axolotl-vs-nebius-token-factory-fine-tuning", "published": "2026-10-01", "section": "tools", "title": "Axolotl vs Nebius Token Factory fine-tuning for AI agents", "toc": null, "updated": "2026-10-09", "url": "https://www.anchorterminal.com/compare/axolotl-vs-nebius-token-factory-fine-tuning" }, "tokens": { "markdown": 2450, "slim": 730 }, "version": 1 }