rullama-finetune 0.12.0

Cloud fine-tune APIs (OpenAI, Anthropic, Together, Fireworks, Anyscale, Bedrock, Vertex AI) for the rullama agent framework. Dataset pipelines live in `rullama-datasets`; local LoRA/QLoRA/DoRA fine-tuning lives in `rullama-lora`.
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rullama-finetune

Crates.io Documentation License

Cloud fine-tune APIs for rullama agents. Dataset pipelines live in the sibling rullama-datasets crate.

Fine-tuning surface:

  • rullama-finetune (this crate) — cloud fine-tune APIs (OpenAI / Anthropic / Together / Fireworks / Anyscale / Bedrock / Vertex AI).
  • rullama-datasets — dataset pipelines (JSONL I/O, tokenization, dedup, format conversion); extracted from this crate in v0.11 and re-exposed here via the datasets-* features.
  • rullama-lora (sibling rullama workspace) — local PEFT (LoRA / QLoRA / DoRA), Burn-backed. (Lived in this workspace as a separate local-PEFT crate prior to v0.11; moved out alongside the rest of the wgpu inference engine.)
  • rullama-training (sibling rullama workspace) — placeholder for actual training-from-scratch.

What lives here

  • manager::TrainingManager — dispatches fine-tune jobs to whichever provider implements FineTuneProvider.
  • cloud::FineTuneProvider + FineTuneProviderFactory — provider-agnostic trait + factory.
  • cloud::providers (one module per cloud API) — concrete impls.
  • config — hyperparameter / adapter / alignment-method types shared with rullama-lora.
  • error::TrainingError, types::{TrainingJobId, TrainingJobStatus, ...} — shared infrastructure.

Features

Feature Default Notes
cloud yes reqwest-based cloud provider clients
bedrock no AWS Bedrock fine-tune (sigv4)
vertex no Google Vertex AI (gcp_auth)
datasets-hf-tokenizer no HuggingFace tokenizers
datasets-tiktoken no OpenAI tiktoken
datasets-dedup no sha2 + rand for content dedup
datasets-full no All three datasets sub-features
full no cloud + bedrock + vertex + datasets-full

Usage

[dependencies]
rullama-finetune = "0.12"

use rullama_finetune::{TrainingManager, CloudFineTuneConfig};

let manager = TrainingManager::new(/* ... */);
let job = manager.submit(CloudFineTuneConfig { /* ... */ }).await?;

See also

  • rullama-datasets — dataset pipelines extracted from this crate (re-exposed via the datasets-* features).
  • rullama-lora (sibling rullama workspace) — local PEFT (LoRA / QLoRA / DoRA), Burn-backed. Reuses this crate's shared config / error / types modules.
  • rullama-provider — LLM chat clients (separate crate).
  • rullama — umbrella facade with training / training-cloud features (cloud only since v0.11).

License

MIT OR Apache-2.0