Tadpole is an agent I built from scratch (plain Python, ~3,500 lines, no LangChain or other framework), then post-trained one measured stage at a time. I use it every day as a Telegram bot. Repo: https://github.com/shakewingo/Tadpole · Adapters and data in HF Run the trained model locally. Each adapter is on Hugging Face as a llama.cpp LoRA (66 MB) that loads on top of the Q8_0 GGUF: hf download unsloth/Qwen3-4B-Instruct-2507-GGUF Qwen3-4B-Instruct-2507-Q8_0.gguf --local-dir models hf download Shakewin/tadpole-qwen3-4b-ei-3 ei-3-lora-f16.gguf --local-dir models AGENT_MODEL_PATH=models/Qwen3-4B-Instruct-2507-Q8_0.gguf AGENT_LORA_PATH=models/ei-3-lora-f16.gguf \ python -m agent_from_scratch.agent I checked that the local route keeps the gains: on the 144-task validation set through llama.cpp (Q8_0 + LoRA, one seed), base 0.507, SFT-1 0.701, SFT-2 0.750, EI-3 0.826. The same adapters on vLLM in bf16 scored 0.752 and 0.852 on val, so quantisation and the LoRA conversion cost little. On a 16 GB M5 it holds about 8.5 GB and takes ~2.4 s per model call. Held-out results (task types no training set contains, 3 samples per task, vLLM bf16): Candidate Test Hard test BFCL multi-turn Base 0.419 0.275 0.360 SFT-1: verified teacher trajectories + When2Call + Toucan 0.774 0.429 0.285 SFT-2: plus multi-turn demonstrations 0.738 0.492 0.325 EI-3: SFT-2 + 3 rounds of expert iteration 0.745 0.471 0.395 * a local adaptation of BFCL's multi-turn base category, not a leaderboard score. What I learned: - SFT on *verified* trajectories is the big win. False "I'm done" claims fell from 31 to 1 in 420 runs. - Expert iteration looked great on val (0.752 → 0.852) and was level with its starting point on the held-out tests. The best of several noisy val scores is biased upwards. - GRPO lost to expert iteration at every equal rollout budget: on a 4B, too few tasks per step had both passing and failing samples. - A plan tool (`update_plan` plus a reminder) lifted the *base* model from 0.507 to 0.646 on val. - llama.cpp happily accepts a LoRA path that doesn't exist and segfaults on the first decode. My bot looped on it under launchd until I made it refuse a missing adapter at startup. The honest part: the benchmark is generated tasks that resemble the training data. In everyday use the tuned 4B is still weak on open-ended requests, so my bot runs on DeepSeek day to day, and the 4B is what I study and train. The README says so up front. All post-training cost about $140 of rented GPUs. The walkthrough notebook opens in Colab and shows SFT, expert iteration and GRPO on recorded samples without a GPU. Check it out and any feedback is welcome!!   submitted by   /u/DesignerIsland5524 [link]   [comments]