Instructions to use Ttimms/MiniCPM5-2B-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ttimms/MiniCPM5-2B-FP8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Ttimms/MiniCPM5-2B-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Ttimms/MiniCPM5-2B-FP8")
model = AutoModelForCausalLM.from_pretrained("Ttimms/MiniCPM5-2B-FP8", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Ttimms/MiniCPM5-2B-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ttimms/MiniCPM5-2B-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ttimms/MiniCPM5-2B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ttimms/MiniCPM5-2B-FP8

SGLang

How to use Ttimms/MiniCPM5-2B-FP8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Ttimms/MiniCPM5-2B-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ttimms/MiniCPM5-2B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Ttimms/MiniCPM5-2B-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ttimms/MiniCPM5-2B-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ttimms/MiniCPM5-2B-FP8 with Docker Model Runner:
```
docker model run hf.co/Ttimms/MiniCPM5-2B-FP8
```

MiniCPM5-2B — FP8 (compressed-tensors)

FP8-dynamic quantization of openbmb/MiniCPM5-2B, weights and activations in float8_e4m3 (per-channel weights, per-token dynamic activations), lm_head and embeddings kept in bf16. Produced with llm-compressor 0.13.

2.84 GiB on disk (bf16 base is 4.68 GiB — 39 % smaller)
Serves on vLLM (compressed-tensors), native FP8 tensor-core path on Ada / Blackwell
Near-lossless on coding benchmarks — see the eval below

This is the recommended quant for MiniCPM5-2B when you want maximum quality retention. If you need to fit closer to 2 GB, see the NVFP4 W4A16 build (smaller, ~2.4 pp HumanEval / ~4.8 pp MBPP cost).

Evaluation

lm-evaluation-harness, vLLM 0.26 backend, greedy decoding, median of 3 draws with the range (the harness is non-deterministic run-to-run even at greedy — a single draw is not a reproducible score). All arms measured in one session against the released checkpoint. HumanEval-instruct pass@1/create_test (n = 164); MBPP base 3-shot (n = 500).

build	HumanEval-inst	MBPP (3-shot)	size	Δ HE / MBPP
bf16 base	86.59 % (85.98–86.59)	50.60 % (50.40–51.00)	4.68 GiB	—
FP8-dynamic (this)	84.76 % (84.15–85.37)	48.80 % (48.80–49.00)	2.84 GiB	−1.8 / −1.8 pp
NVFP4-W4A16 GPTQ	84.15 % (81.71–84.15)	45.80 % (45.60–46.40)	2.03 GiB	−2.4 / −4.8 pp
NVFP4-W4A16 RTN	79.88 % (77.44–79.88)	41.20 % (41.20–41.80)	2.03 GiB	−6.7 / −9.4 pp
mixed (MLP-NVFP4 + attn-FP8)	84.15 % (82.93–85.98)	46.60 % (46.00–46.80)	2.20 GiB	−2.4 / −4.0 pp

Both FP8 deltas sit inside the eval's own run-to-run spread — FP8 is effectively lossless here. NVFP4 costs real accuracy on a 2.5 B dense model (little redundancy to absorb 4-bit weights); pick it only if the extra ~0.8 GiB matters for your KV budget.

Full comparison, serving notes, and a fine-tune experiment that regressed the base's coding (so these quants target the released checkpoint): https://github.com/t-timms/minicpm5-2b-quants

Usage

vllm serve Ttimms/MiniCPM5-2B-FP8 --max-model-len 32768 --kv-cache-dtype fp8

from vllm import LLM, SamplingParams
llm = LLM("Ttimms/MiniCPM5-2B-FP8", trust_remote_code=True)
print(llm.chat([{"role": "user", "content": "Write a Python LRU cache."}],
               SamplingParams(temperature=0.6, max_tokens=512))[0].outputs[0].text)

On WSL, set VLLM_USE_V2_MODEL_RUNNER=0 (the V2 runner needs UVA, which WSL disables).

Method & provenance

Quantizer: llm-compressor 0.13, QuantizationModifier(scheme="FP8_DYNAMIC"), ignore lm_head + embed_tokens. No calibration data required (dynamic activations).
Base: openbmb/MiniCPM5-2B (LlamaForCausalLM, 2.5 B, Apache-2.0).
Built and evaluated on an RTX 5070 Ti (Blackwell, SM120), vLLM 0.26.

License

Apache-2.0, inherited from openbmb/MiniCPM5-2B.

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Safetensors

Model size

3B params

Tensor type

BF16

F8_E4M3

Model tree for Ttimms/MiniCPM5-2B-FP8

Base model

openbmb/MiniCPM5-2B

Quantized

(128)

this model