ardeshir · August 11, 2026 18:08
diff --git a/gistfile1.txt b/gistfile1.txt
 1. Start now with QLoRA SFT on an 8B instruct modelin LLaMA-Factory or Axolotl: r=16–32, α=32, all-linear, LR 2e-4, 3 epochs,train_on_inputs:false. Dataset = (request → tool call → grounded answer). Target metric: tool-call schema-validity and solver-acceptance rate, not loss.Threshold to advance:if validity ≥ your bar, ship; if not, continue.
 2. If behavior issues remain, add a LoRA DPO pass(β=0.1, LR 5e-6) on (faithful-tool-call vs fabricated) pairs. Cheap, low-risk.
 3. Only if validity/acceptance still misses the bar, run a GRPO stagewith the solver as a composed verifiable reward (format + canonical-ID + solver-acceptance), in TRL (or Unsloth if single-GPU), with vLLM andloss_type="dr_grpo". Budget ≥2 GPUs and real monitoring.Change trigger:adopt GRPO when the gap between “imitatable” and “verifiable-correct” behavior is what’s blocking you.
 4. Serving on Azure/AKS:use vLLM with multi-LoRA to host the merged/adapter model; the same vLLM stack doubles as GRPO’s generation backend, so standardizing on it early pays off twice.
 5. Pin your stack.bitsandbytes/CUDA and TRL/Unsloth versions are the top upgrade-breakage sources; containerize (or use NeMo’s pinned images) and freeze versions per training run.
 5. Avoid torchtune/Forge and LLM Foundry for this project— torchtune is unmaintained, Forge is paused/experimental, and LLM Foundry lacks DPO/GRPO and only partially supports LoRA.
	1. Start now with QLoRA SFT on an 8B instruct modelin LLaMA-Factory or Axolotl: r=16–32, α=32, all-linear, LR 2e-4, 3 epochs,train_on_inputs:false. Dataset = (request → tool call → grounded answer). Target metric: tool-call schema-validity and solver-acceptance rate, not loss.Threshold to advance:if validity ≥ your bar, ship; if not, continue.
	2. If behavior issues remain, add a LoRA DPO pass(β=0.1, LR 5e-6) on (faithful-tool-call vs fabricated) pairs. Cheap, low-risk.
	3. Only if validity/acceptance still misses the bar, run a GRPO stagewith the solver as a composed verifiable reward (format + canonical-ID + solver-acceptance), in TRL (or Unsloth if single-GPU), with vLLM andloss_type="dr_grpo". Budget ≥2 GPUs and real monitoring.Change trigger:adopt GRPO when the gap between “imitatable” and “verifiable-correct” behavior is what’s blocking you.
	4. Serving on Azure/AKS:use vLLM with multi-LoRA to host the merged/adapter model; the same vLLM stack doubles as GRPO’s generation backend, so standardizing on it early pays off twice.
	5. Pin your stack.bitsandbytes/CUDA and TRL/Unsloth versions are the top upgrade-breakage sources; containerize (or use NeMo’s pinned images) and freeze versions per training run.
	5. Avoid torchtune/Forge and LLM Foundry for this project— torchtune is unmaintained, Forge is paused/experimental, and LLM Foundry lacks DPO/GRPO and only partially supports LoRA.
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