minicpm5-finetune-llamafactory

Fine-tune MiniCPM5-1B with LLaMA-Factory (YAML-driven SFT / DPO / WebUI). Use when the user wants to fine-tune via LLaMA-Factory, llamafactory-cli, mentions YAML configs, WebUI, or asks for the most-documented community framework.

Quality

88%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Low

Low-risk findings worth noting

Fine-tune MiniCPM5-1B with LLaMA-Factory

YAML-driven SFT / DPO with WebUI. Most-documented community framework.

Required input

Var	Example	Default
`BASE_MODEL`	`openbmb/MiniCPM5-1B`	required
`DATA_DIR`	dir containing `dataset_info.json` + jsonl	required
`DATASET_NAME`	name registered in `dataset_info.json`	required
`OUTPUT_DIR`	`./runs/minicpm5_lf`	required
`GPU_ID`	`0`	`0`

Steps

1. Install (once, in its own venv to avoid breaking vLLM)

python -m venv .venv-lf && source .venv-lf/bin/activate
pip install "llamafactory==0.9.3"

⚠️ LLaMA-Factory pins transformers==4.52. Do NOT install it into a vLLM env (vLLM 0.21 wants transformers>=5.6). Always use a separate venv.

2. Register the dataset (sharegpt / messages format)

${DATA_DIR}/dataset_info.json:

{
  "${DATASET_NAME}": {
    "file_name": "your_data.jsonl",
    "formatting": "sharegpt",
    "columns": {"messages": "messages"},
    "tags": {
      "role_tag": "role", "content_tag": "content",
      "user_tag": "user", "assistant_tag": "assistant", "system_tag": "system"
    }
  }
}

Each line of your_data.jsonl:

{"messages": [{"role":"system","content":"..."}, {"role":"user","content":"..."}, {"role":"assistant","content":"..."}]}

3. Write the training YAML

Save as ${OUTPUT_DIR}/lora_sft.yaml:

### model
model_name_or_path: ${BASE_MODEL}
trust_remote_code: false

### method
stage: sft
do_train: true
finetuning_type: lora
lora_rank: 16
lora_alpha: 32
lora_target: all                      # all linear layers

### dataset
dataset: ${DATASET_NAME}
dataset_dir: ${DATA_DIR}
template: empty                       # 🔑 MANDATORY for MiniCPM5 — delegates to model's own jinja
cutoff_len: 4096
max_samples: 100000
overwrite_cache: true
preprocessing_num_workers: 8

### output
output_dir: ${OUTPUT_DIR}
logging_steps: 10
save_steps: 200
plot_loss: true
overwrite_output_dir: true

### train
per_device_train_batch_size: 4
gradient_accumulation_steps: 4
learning_rate: 2.0e-4
num_train_epochs: 2.0
lr_scheduler_type: cosine
warmup_ratio: 0.03
bf16: true
ddp_timeout: 180000000

🔑 template: empty is MANDATORY. It delegates to the model's own chat_template.jinja (which is the MiniCPM5 ChatML template with think / nothink / tools support). Do NOT set template: llama3 / qwen / etc. — those produce a corrupted token layout.

4. Train

CUDA_VISIBLE_DEVICES=${GPU_ID} llamafactory-cli train ${OUTPUT_DIR}/lora_sft.yaml

For multi-GPU: prepend FORCE_TORCHRUN=1 and set NPROC_PER_NODE=8.

5. Validate

Loss should decrease monotonically; you should see lines like:

{'loss': 4.19, 'learning_rate': 0.000192, 'epoch': 0.2}
{'loss': 3.62, 'learning_rate': 0.000001, 'epoch': 1.0}

After training, the LoRA adapter is at ${OUTPUT_DIR}/. Sanity-check inference:

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()
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)
print(tok.decode(model.generate(inputs, max_new_tokens=32, do_sample=False)[0][inputs.shape[-1]:], skip_special_tokens=True))

Coherent answer ⇒ ✅. Gibberish ⇒ check template: empty in the YAML.

Merge LoRA for serving

cat > ${OUTPUT_DIR}/merge.yaml <<EOF
model_name_or_path: ${BASE_MODEL}
adapter_name_or_path: ${OUTPUT_DIR}
template: empty
finetuning_type: lora
export_dir: ./minicpm5-merged
export_size: 4
EOF
llamafactory-cli export ${OUTPUT_DIR}/merge.yaml

The merged model is a regular LlamaForCausalLM and serves with any minicpm5-deploy-* skill.

Full SFT (no LoRA)

Replace finetuning_type: lora and the LoRA fields with finetuning_type: full. Add deepspeed: examples/deepspeed/ds_z2_config.json for multi-GPU.

Reference

docs/finetune/llamafactory.md

Repository: OpenBMB/MiniCPM
Path: skills/minicpm5-finetune-llamafactory/SKILL.md
Commit: 719e4fc

Last updated: 17 days ago
First committed: 3 months ago

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.