MiniCPM5-2B-OptiQ-4bit

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Mixed-precision MLX quantization of openbmb/MiniCPM5-2B, OpenBMB's 2B-class Llama-architecture model with hybrid reasoning, tool calling and a 128k context. Apache-2.0 from the base model.

• Base: openbmb/MiniCPM5-2B (42 layers, Llama architecture, hybrid reasoning, 128k context)
• Quantization: OptiQ sensitivity-aware mixed precision (4-bit + 8-bit)
• Disk size: 1,837 MB
• Calibration: the bundled optiq.jsonl mix (40 samples across prose, reasoning, code, agent loops, tool-calling, instructions)

The OptiQ optimizer measured every linear layer against the bf16 model and kept 112 of 295 at 8-bit (the ones whose output moved most under 4-bit: output projections, the last blocks, lm_head) and 183 at 4-bit. The full per-layer assignment is in optiq/metadata.json next to the safetensors.

Capability Score

Six benchmarks, one number, the same recipe as every mlx-optiq quant (non-thinking mode, for cross-family comparability).

Benchmark	Score
MMLU (5-shot, 1000)	59.8%
GSM8K (1000, 3-shot CoT)	82.1%
IFEval (full set, strict)	86.7%
BFCL V3 (simple AST, 200 calls)	82.5%
HumanEval (164, pass@1)	81.1%
HashHop (long-context retrieval)	24.0%
Capability Score	69.36

For scale: this 1.8 GB file scores level with the 2.8 GB Qwen3.5-4B-OptiQ-4bit (68.76), ahead on instruction following, GSM8K and HumanEval, behind on MMLU and tool-call format.

Loading

from mlx_lm import load, generate

model, tok = load("mlx-community/MiniCPM5-2B-OptiQ-4bit")

prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Summarize the plot of The Iliad in three sentences."}],
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
print(generate(model, tok, prompt=prompt, max_tokens=300))

Hybrid reasoning: think or no-think

The chat template takes an enable_thinking flag. On, the model emits a <think>...</think> block before answering, which helps on math, planning and multi-step code. The scores above are with thinking off.

Mode	temperature	top_p	Use when
No-think (default)	0.7	0.95	Fast assistant, rewriting, conversational
Think	0.9	0.95	Math, code, multi-hop reasoning

optiq serve forwards chat_template_kwargs verbatim, and also accepts the model name with a :think / :no-think suffix:

optiq serve --model mlx-community/MiniCPM5-2B-OptiQ-4bit --port 8000

curl -s http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"mlx-community/MiniCPM5-2B-OptiQ-4bit:think",
       "messages":[{"role":"user","content":"What is 17 * 23?"}]}'

Tool calling

MiniCPM5 uses its own <function name="..."><param name="..."> call format. optiq serve parses it into OpenAI tool_calls, so agents that speak the OpenAI or Anthropic protocol (OptiQ Code, Claude Code via the Anthropic endpoint, OpenCode, Codex) work unchanged.

KV cache

kv_config.json in this repo is a per-layer mixed-precision KV-cache plan from optiq kv-cache. Pass it to optiq serve --kv-config for a smaller cache at long context without the quality loss of uniform 4-bit KV.

Fine-tuning

Small enough to LoRA-train on a 24 GB Mac at max_seq_length=2048. The sensitivity-aware LoRA overlay reads optiq/metadata.json and gives 8-bit layers twice the adapter rank of 4-bit layers at the same parameter budget.

optiq lora train mlx-community/MiniCPM5-2B-OptiQ-4bit \
  --data ./my_training_data \
  --preset default \
  --max-seq-length 2048

Reproducibility

optiq convert openbmb/MiniCPM5-2B --target-bpw 5.0 --candidate-bits 4,8 --reference bf16

Deterministic given the same calibration mix.

Quantize your own

pip install mlx-optiq
optiq convert <hf-model-id> --target-bpw 5.0 --candidate-bits 4,8
optiq lab   # local workbench: chat, compare, quantize, fine-tune

License + provenance

Apache-2.0, inherited from openbmb/MiniCPM5-2B. Quantized with mlx-optiq.