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 / README.md

Ttimms

docs: full quant comparison table + serving notes + writeup link

8ccc63f verified about 1 month ago

preview code

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	---
	license: apache-2.0
	base_model:
	- openbmb/MiniCPM5-2B
	base_model_relation: quantized
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- minicpm
	- minicpm5
	- llama
	- fp8
	- compressed-tensors
	- quantization
	- vllm
	- blackwell
	- code
	- on-device
	language:
	- en
	- zh
	quantized_by: Ttimms
	---

	# MiniCPM5-2B — FP8 (compressed-tensors)

	FP8-dynamic quantization of [`openbmb/MiniCPM5-2B`](https://huggingface.co/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](https://github.com/vllm-project/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](https://huggingface.co/Ttimms/MiniCPM5-2B-NVFP4)
	(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

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

	```python
	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`.