Fine-tune MiniCPM5-1B — framework router

You're being asked to fine-tune MiniCPM5-1B. Pick exactly one framework skill below and invoke that skill rather than improvising — every framework has at least one MiniCPM5-specific gotcha that the dedicated skill knows about.

1. Required input from the user

Default base model

If BASE_MODEL is not pinned, default to the Hugging Face fp16 release:

openbmb/MiniCPM5-1B

Any local directory containing config.json + model.safetensors + tokenizer.json also works.

2. Decision matrix — pick exactly one

Decision shortcuts

• First time fine-tuning MiniCPM5: pick minicpm5-finetune-llamafactory — most documented, fewest surprises.
• Need DPO / KTO / ORPO: pick minicpm5-finetune-ms-swift (best out-of-the-box) or minicpm5-finetune-trl (most control).
• Single 24 GB consumer GPU: pick minicpm5-finetune-unsloth with load_in_4bit=True.
• Target is llama.cpp / Ollama / LM Studio / MiniCPM Desk Pet (GGUF): train with any skill above, then convert the adapter with minicpm5-finetune-gguf-lora (PEFT adapter → GGUF LoRA).

3. Invocation contract

Each sub-skill expects BASE_MODEL, DATA, OUTPUT_DIR and outputs an LoRA adapter (or full checkpoint) at OUTPUT_DIR/. Read the picked sub-skill in full before running — every framework has at least one MiniCPM5-specific gotcha:

4. Universal sanity check after training

Regardless of framework:

# 1. Verify adapter (or full ckpt) was saved
ls "$OUTPUT_DIR"        # adapter_model.safetensors + adapter_config.json (LoRA) OR a full HF directory

# 2. Quick inference check (HF-side, works for both LoRA and full)
python -c "
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained('$BASE_MODEL', torch_dtype=torch.bfloat16, device_map='auto').eval()
model = PeftModel.from_pretrained(base, '$OUTPUT_DIR').eval()    # skip this line for full SFT
tok = AutoTokenizer.from_pretrained('$BASE_MODEL')
inputs = tok.apply_chat_template([{'role':'user','content':'1+1=?'}], add_generation_prompt=True, enable_thinking=False, return_tensors='pt').to(model.device)
out = model.generate(inputs, max_new_tokens=32, do_sample=False)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
"

Expected: a coherent answer (e.g. "2"). If it's gibberish or empty, training broke (most likely chat template misalignment — ⮕ check the framework's gotcha row above).

5. Don't reinvent: link to the cookbook

Each sub-skill is paired with a one-page cookbook in docs/finetune/. The skill is the machine-readable shortcut; the cookbook is the human-readable reference.

6. Next step — deploying the adapter

The adapter at OUTPUT_DIR/ is a PEFT adapter (adapter_model.safetensors). How you ship it depends on the runtime:

• vLLM / transformers / SGLang (fp16 HF base): load the PEFT adapter directly — no conversion.
• llama.cpp / Ollama / LM Studio / MiniCPM Desk Pet (GGUF base): convert it to a GGUF LoRA first with minicpm5-finetune-gguf-lora, then --lora adapter.gguf (or upload it in the Desk Pet app).