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

Libraries

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

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

pipe = pipeline("text-generation", model="axteam/MiniCPM5-2B-FP8-dynamic")
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("axteam/MiniCPM5-2B-FP8-dynamic")
model = AutoModelForCausalLM.from_pretrained("axteam/MiniCPM5-2B-FP8-dynamic", 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 axteam/MiniCPM5-2B-FP8-dynamic with vLLM:

Install from pip and serve model

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

Use Docker

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

SGLang

How to use axteam/MiniCPM5-2B-FP8-dynamic 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 "axteam/MiniCPM5-2B-FP8-dynamic" \
    --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": "axteam/MiniCPM5-2B-FP8-dynamic",
		"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 "axteam/MiniCPM5-2B-FP8-dynamic" \
        --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": "axteam/MiniCPM5-2B-FP8-dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

MiniCPM5-2B-FP8-dynamic / README.md

uzins

Add files using upload-large-folder tool

765d4d3 verified 3 days ago

preview code

Raw History Blame Contribute Delete

2.47 kB

---
base_model: openbmb/MiniCPM5-2B
base_model_relation: quantized
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - minicpm
  - compressed-tensors
  - vllm
  - quantized
  - fp8
---
# MiniCPM5-2B · FP8 (dynamic)

8-bit quantization of [`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) for **vLLM serving**. Quantized by **axteam**.

| | |
|---|---|
| **Base model** | [`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) |
| **Method** | FP8 (dynamic) |
| **Format** | compressed-tensors `FP8_DYNAMIC` |
| **Weights / activations** | W8A8 — FP8 E4M3 weights (per-channel), dynamic per-token FP8 activations |
| **Architecture** | Dense (Llama-architecture) · text-only |
| **Size on disk** | ~2.8 GB |
| **Calibration** | None (data-free) |
| **License** | Apache-2.0 |

## Quantization details
- **Scheme** — W8A8 — FP8 E4M3 weights (per-channel), dynamic per-token FP8 activations, stored as compressed-tensors `FP8_DYNAMIC`.
- **Kept in BF16** — token embeddings, the LM head, and all RMSNorms (a small fraction of parameters).
- Post-training quantization (no retraining). Tokenizer / `chat_template` identical to the original.

## Evaluation (measured)

Fidelity to the BF16 original on an internal mixed EN/KO set (100 prompts, BF16 greedy answers scored under each build).

| Metric | This model | BF16 original |
|---|---|---|
| Perplexity (lower = closer) | 1.6494 | 1.5988 (**+3.2%**) |
| Top-1 next-token agreement | 92.9% | 98.9% |


## Usage (vLLM)
```bash
vllm serve axteam/MiniCPM5-2B-FP8-dynamic
```

## License
Quantized derivative of [`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) (Apache-2.0). Original weights & tokenizer belong to OpenBMB.

---
## 한국어 요약
[`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) 를 **FP8 (dynamic)** 로 양자화한 서빙용(8-bit) 모델입니다. 작성: **axteam**.
- **방식** — FP8 E4M3, 가중치 채널별 스케일(정적·보정불필요), 활성화 동적 FP8. 형식 compressed-tensors `FP8_DYNAMIC`.
- **BF16 유지** — 토큰 임베딩·LM 헤드·모든 norm.
- PTQ(재학습 없음), 토크나이저·chat_template 원본 동일.
- **품질(실측)** — BF16 대비 perplexity 1.6494(원본 1.5988, **+3.2%**), top-1 92.9%(원본 98.9%). 내부 영/한 100문항.
- **사용** — `vllm serve axteam/MiniCPM5-2B-FP8-dynamic`. 라이선스 Apache-2.0(원본 OpenBMB).

	---
	base_model: openbmb/MiniCPM5-2B
	base_model_relation: quantized
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	tags:
	- minicpm
	- compressed-tensors
	- vllm
	- quantized
	- fp8
	---
	# MiniCPM5-2B · FP8 (dynamic)

	8-bit quantization of [`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) for vLLM serving. Quantized by axteam.

	\| \| \|
	\|---\|---\|
	\| Base model \| [`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) \|
	\| Method \| FP8 (dynamic) \|
	\| Format \| compressed-tensors `FP8_DYNAMIC` \|
	\| Weights / activations \| W8A8 — FP8 E4M3 weights (per-channel), dynamic per-token FP8 activations \|
	\| Architecture \| Dense (Llama-architecture) · text-only \|
	\| Size on disk \| ~2.8 GB \|
	\| Calibration \| None (data-free) \|
	\| License \| Apache-2.0 \|

	## Quantization details
	- Scheme — W8A8 — FP8 E4M3 weights (per-channel), dynamic per-token FP8 activations, stored as compressed-tensors `FP8_DYNAMIC`.
	- Kept in BF16 — token embeddings, the LM head, and all RMSNorms (a small fraction of parameters).
	- Post-training quantization (no retraining). Tokenizer / `chat_template` identical to the original.

	## Evaluation (measured)

	Fidelity to the BF16 original on an internal mixed EN/KO set (100 prompts, BF16 greedy answers scored under each build).

	\| Metric \| This model \| BF16 original \|
	\|---\|---\|---\|
	\| Perplexity (lower = closer) \| 1.6494 \| 1.5988 (+3.2%) \|
	\| Top-1 next-token agreement \| 92.9% \| 98.9% \|


	## Usage (vLLM)
	```bash
	vllm serve axteam/MiniCPM5-2B-FP8-dynamic
	```

	## License
	Quantized derivative of [`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) (Apache-2.0). Original weights & tokenizer belong to OpenBMB.

	---
	## 한국어 요약
	[`openbmb/MiniCPM5-2B`](https://huggingface.co/openbmb/MiniCPM5-2B) 를 FP8 (dynamic) 로 양자화한 서빙용(8-bit) 모델입니다. 작성: axteam.
	- 방식 — FP8 E4M3, 가중치 채널별 스케일(정적·보정불필요), 활성화 동적 FP8. 형식 compressed-tensors `FP8_DYNAMIC`.
	- BF16 유지 — 토큰 임베딩·LM 헤드·모든 norm.
	- PTQ(재학습 없음), 토크나이저·chat_template 원본 동일.
	- 품질(실측) — BF16 대비 perplexity 1.6494(원본 1.5988, +3.2%), top-1 92.9%(원본 98.9%). 내부 영/한 100문항.
	- 사용 — `vllm serve axteam/MiniCPM5-2B-FP8-dynamic`. 라이선스 Apache-2.0(원본 OpenBMB).