Axolotl is an open-source framework for fine-tuning large language models, including models with mixture-of-experts, multimodal, reinforcement-learning, and distributed-training setups. Researchers and developers use it to adapt language models to custom training data and objectives. The catalogue agents and instructions support workflows for operating this fine-tuning framework.
Borrowing it
Nothing to install: this file belongs to axolotl-ai-cloud/axolotl. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/AGENTS.mdgit clone --depth 1 https://github.com/axolotl-ai-cloud/axolotlWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/axolotl-ai-cloud/axolotl/agents-md)<a href="https://agentmods.dev/instructions/axolotl-ai-cloud/axolotl/agents-md"><img src="https://agentmods.dev/badge/instructions/axolotl-ai-cloud/axolotl/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/axolotl-ai-cloud/axolotl/agents-md"><img src="https://agentmods.dev/badge/instructions/axolotl-ai-cloud/axolotl/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01677 | $0.01677 |
| Opus 5 | $0.00839 | $0.00839 |
| Sonnet 5 | $0.00335 | $0.00335 |
| Haiku 4.5 | $0.00168 | $0.00168 |
Grade A, and why
axolotl AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Axolotl
Fine-tuning framework for LLMs. Config-driven: every training run is defined by a single YAML file.
Tech Stack
Python, PyTorch, HuggingFace Transformers, TRL, PEFT (LoRA/QLoRA), DeepSpeed, FSDP, vLLM (for GRPO generation).
Commands
axolotl train config.yaml # Train (single or multi-GPU, auto-detected)
axolotl preprocess config.yaml # Tokenize dataset and validate config
axolotl preprocess config.yaml --debug # Inspect tokenized samples and label masking
axolotl inference config.yaml # Interactive inference
axolotl merge-lora config.yaml # Merge LoRA adapter into base model
axolotl vllm-serve config.yaml # Start vLLM server for GRPO/EBFT training
axolotl fetch examples # Download example configs
axolotl agent-docs # Show agent-optimized docs (bundled with pip package)
axolotl agent-docs grpo # Topic-specific agent reference
axolotl config-schema # Dump config JSON schema
Training Methods
| Method | Config Key | When to Use |
|---|---|---|
| SFT | (default) | Input-output pairs, instruction tuning |
| DPO/IPO | rl: dpo / rl: dpo, dpo_loss_type: ["ipo"] |
Paired preference data (chosen vs rejected) |
| KTO | rl: kto |
Unpaired binary preference labels |
| ORPO | rl: orpo |
Single-stage alignment, no ref model |
| GRPO | rl: grpo |
RL with verifiable reward functions (math, code) |
| EBFT | rl: ebft |
Feature-matching rewards from internal representations |
Agent-specific references:
- docs/agents/sft.md — supervised fine-tuning
- docs/agents/preference_tuning.md — DPO, IPO, KTO, ORPO, SimPO
- docs/agents/grpo.md — GRPO online RL with reward functions
- docs/agents/reward_modelling.md — outcome and process reward models
- docs/agents/pretraining.md — continual pretraining
- docs/agents/model_architectures.md — model-specific quirks (Gemma4, Qwen3.5 MoE, etc.)
- docs/agents/new_model_support.md — debugging and adding support for new model architectures
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago Changed · +1 lines · +51 tokens per session fd150b0df60a
- 9d ago First seen · 119 lines · 1,626 tokens per session scan A 3ab83d7fa555
axolotl AGENTS.md is an instructions file published in the GitHub repository axolotl-ai-cloud/axolotl (12,456 stars, last pushed yesterday), licensed Apache-2.0. It adds 1,677 tokens to every session, about $0.0084 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.