Hanzo Gym

LoRA fine-tuning and GRPO for language models, in Rust on hanzo-ml. One binary for CUDA, Metal and CPU. Configs use Axolotl's keys, so a config from zooai/gym runs here unchanged where the feature exists and fails at load where it does not.

cargo install hanzo-gym --features metal   # or --features cuda
gym train examples/qwen3-lora.yml   # Qwen3-0.6B, LoRA on q/v, 20 steps
gym merge examples/qwen3-lora.yml

base_model: Qwen/Qwen3-0.6B
chat_template: qwen3
datasets:
  - path: mlabonne/FineTome-100k
    type: chat_template
    field_messages: conversations
    message_property_mappings: { role: from, content: value }
adapter: lora
lora_r: 16
lora_alpha: 32
lora_target_linear: true
sequence_len: 2048
micro_batch_size: 1
gradient_accumulation_steps: 8
num_epochs: 1
learning_rate: 2e-4
lr_scheduler: cosine
warmup_ratio: 0.03
output_dir: ./outputs/qwen3-lora

The adapter is written in PEFT layout, so it loads in Hanzo Engine, vLLM and peft without conversion. rl: grpo switches the run to GRPO with the reward functions named under trl:.

Scope

Implemented: LoRA on Qwen3 and Llama, SFT on chat_template, alpaca and completion datasets, cosine/linear/constant schedules with warmup, gradient accumulation and clipping, gradient checkpointing, GRPO with built-in and HTTP verifiers, PEFT adapter save and merge.

Not implemented: QLoRA, full fine-tuning, multi-GPU, DPO/KTO/ORPO, sample packing. A config that asks for one of these is rejected at load.

Apache-2.0.