axteam/MiniCPM5-2B-GGUF

MiniCPM5-2B · GGUF (llama.cpp)

GGUF quantizations of openbmb/MiniCPM5-2B for llama.cpp / Ollama / LM Studio. Produced by axteam.

Files

File	Size	Notes
MiniCPM5-2B-Q4_K_M.gguf	1.6 GB	4-bit k-quant (medium) — recommended size/quality balance.
MiniCPM5-2B-Q8_0.gguf	2.7 GB	8-bit — closest to the original.

Text generation via llama.cpp. Converted with llama.cpp convert_hf_to_gguf.py; the original chat_template is embedded.

Quantization method

These GGUF files are post-training quantization (PTQ) made with llama.cpp (convert_hf_to_gguf.py → llama-quantize): the BF16 weights are quantized block-wise (k-quants: per-block scales with mixed bit allocation), with no retraining. The original chat_template is embedded in each file.

Requirements

llama.cpp	build ≥ b9354 — architecture llama + pre-tokenizer minicpm5 needs ≥ b9354, and the commands on this card use -fa on (≥ b6325). Load-checked here with b12005 (commit e0f142d) and b10724 on CPU — the loader accepts the model, tokenizer and the -fa on / q8_0 KV-cache flags; no generation was run in that check.
Memory	the chosen GGUF file (sizes in the table) plus KV cache for the context (-c); --cache-type-k/v q8_0 -fa on halves the KV cache.
Input	text.
Ollama / LM Studio	not tested here.

Run with llama.cpp

# pull one quant straight from the Hub and serve an OpenAI-compatible API on :8080
llama-server -hf axteam/MiniCPM5-2B-GGUF:Q4_K_M -c 8192 --jinja

# or download a single file and chat in the terminal
hf download axteam/MiniCPM5-2B-GGUF MiniCPM5-2B-Q4_K_M.gguf --local-dir .
llama-cli -m MiniCPM5-2B-Q4_K_M.gguf -c 8192 --jinja

Query the server:

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
  "messages": [{"role": "user", "content": "대한민국의 수도는 어디인가요?"}],
  "temperature": 1.0, "top_p": 0.95, "max_tokens": 512
}'

Reduce KV-cache memory (optional, run-time)

KV-cache type is chosen at run time in llama.cpp (it is not baked into the file):

llama-server -hf axteam/MiniCPM5-2B-GGUF:Q4_K_M -c 8192 --jinja --cache-type-k q8_0 --cache-type-v q8_0 -fa on

Ollama

ollama run hf.co/axteam/MiniCPM5-2B-GGUF:Q4_K_M

LM Studio

Search for axteam/MiniCPM5-2B-GGUF in the model browser and pick a quant (e.g. Q4_K_M).

Recommended sampling (from the original generation_config.json): temperature=1.0, top_p=0.95.

License

Quantized derivative of openbmb/MiniCPM5-2B (Apache-2.0). Original weights & tokenizer belong to OpenBMB.

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한국어 요약

openbmb/MiniCPM5-2B 를 llama.cpp 용 GGUF(Q8_0·Q4_K_M) 로 변환. Q8_0=8비트 최고품질, Q4_K_M=4비트(Q8_0 의 약 60% 크기)(소형 모델은 4비트 손실이 큼). 원본 chat_template 내장. 라이선스 Apache-2.0(원본 OpenBMB).

• 파일 — Q4_K_M(4비트·권장), Q8_0(8비트·원본에 가장 가까움).
• 요구사항 — llama.cpp b9354 이상(구조 llama + pre-tokenizer minicpm5 는 b9354+, 카드 명령의 -fa on 은 b6325+). b12005·b10724 에서 CPU 로드 점검(생성은 안 해 봄). Ollama·LM Studio 는 미확인.
• 실행 — llama-server -hf axteam/MiniCPM5-2B-GGUF:Q4_K_M -c 8192 --jinja (OpenAI 호환 서버 :8080) 또는 llama-cli -m MiniCPM5-2B-Q4_K_M.gguf --jinja.
• Ollama — ollama run hf.co/axteam/MiniCPM5-2B-GGUF:Q4_K_M. LM Studio 에서는 axteam/MiniCPM5-2B-GGUF 검색.
• KV 캐시 절감(런타임) — --cache-type-k q8_0 --cache-type-v q8_0 -fa on.
• 권장 샘플링 temperature=1.0, top_p=0.95.