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G33-k
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Model Details
Model Provider
G33-k
Model Tree
openbmb/MiniCPM5-2B
Input Modalities
Output Modalities
Supported Functionality
A narrow tool-use controller. It learns input-form mapping, tool order, argument discipline, clarification, honest status reporting, and expiry/foreign-ID recovery. Page selection, ownership facts and brand-voice admission live in the tools, not the model. Writing, profiling and copywriting are not training targets.
Synthetic only. The adapter was trained on sft/train.jsonl of corpus revision
2026-09-21 (1,032 conversations, 2,856 assistant decisions, 43 base families),
generated by executing the real tools against deterministic fixture worlds. There are
zero live-company or human-reviewed training examples. Fixture success is not
real-world accuracy.
| Trainer | train_unsloth.py (unsloth 2026.9.7 used only for the model; repo's own batching, masking, resume) |
| LoRA | r=16, alpha=32, dropout 0, target modules q/k/v/o/gate/up/down |
| Learning rate | 1e-4, cosine, warmup 3% |
| Epochs | 2 (4,168 optimizer steps) |
| Quantization | NF4 (4-bit) base, bf16 compute |
| Tokenization | per-decision, assistant-only masking, native chat template (enable_thinking=False), template hash cc945752... |
| Max sequence | 16,384 (longest decision 9,809 tokens) |
| Host | NVIDIA RTX PRO 6000 Blackwell (sm_120), MIG 4g.96gb slice |
| Wall clock | 3h 14m, 1,264 tok/s overall, 26.1 GiB peak |
| Validation loss | 0.0 over 714 decisions (saturation signal; harness pass rate is the selection criterion) |
An epoch-1 adapter was also saved; scores reported per checkpoint when evaluation completes.
Full 258-episode validation on corpus revision 2026-09-21
(training-runs/reports/nim-validation-2026-09-21.md): 258/258 episodes (scenario macro
1.000, every one of the 43 families 6/6) against the untouched base at 0/258. Serving was the
unmerged adapter over the same NF4 base used for training, greedy (temperature 0), thinking off,
on the nim host (RTX PRO 6000 Blackwell MIG 4g.96gb). The base model failed every episode, missing
the final-answer schema in 252 of 258 and looping past the 10-step cap in 114. Paired difference
+1.000, standard error clustered by family. Synthetic fixture worlds only: fixture success is not
real-world accuracy. An earlier adapter trained on the 2026-09-18 revision scored 252/258 on that
revision; that score is retired and not comparable.
The adapter is a LoRA delta, meant to be attached to the 4-bit base, not merged:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel base = "openbmb/MiniCPM5-2B"quant = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16)tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, quantization_config=quant, device_map={"": 0})model = PeftModel.from_pretrained(model, "G33-k/minicpm5-2b-brand-tools-controller-lora")Call the two tools with the public schemas, one tool call per assistant turn, greedy and with
thinking off. The source repository wraps this into an OpenAI-compatible endpoint
(training-runs/serve_openai.py) and scores it with the fixture harness:
sh
(cd minicpm5-eval-kit && node --experimental-strip-types scripts/evaluate.ts \ --mode model --split validation --base-url http://127.0.0.1:30000/v1 \ --model minicpm5-adapter --out ../training-runs/reports/minicpm5-adapter-validation.json)Full instructions, including the decode and argument-coercion rules for this model family, are in
minicpm5-eval-kit/docs/EVALUATION_GUIDE.md section 7 of
github.com/geekhoo/minicpm5-2b-voice.
MiniCPM5 registers <function, <param, </function>, <tool_call> as special
tokens: decode with skip_special_tokens=False and trim only the chat controls, or
every tool call silently disappears. Tool-call parameters must be coerced by declared
schema type, per OpenBMB's minicpm5xml_tool_parser.py. Serve greedy, thinking off.
Evaluate the unmerged adapter on the NF4 base it trained against.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModel base = "openbmb/MiniCPM5-2B"quant = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16)tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, quantization_config=quant, device_map={"": 0})model = PeftModel.from_pretrained(model, "G33-k/minicpm5-2b-brand-tools-controller-lora")(cd minicpm5-eval-kit && node --experimental-strip-types scripts/evaluate.ts \ --mode model --split validation --base-url http://127.0.0.1:30000/v1 \ --model minicpm5-adapter --out ../training-runs/reports/minicpm5-adapter-validation.json)