Instructions to use AXERA-TECH/MiniCPM5-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXERA-TECH/MiniCPM5-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AXERA-TECH/MiniCPM5-1B")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AXERA-TECH/MiniCPM5-1B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AXERA-TECH/MiniCPM5-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AXERA-TECH/MiniCPM5-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AXERA-TECH/MiniCPM5-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AXERA-TECH/MiniCPM5-1B
- SGLang
How to use AXERA-TECH/MiniCPM5-1B 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 "AXERA-TECH/MiniCPM5-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AXERA-TECH/MiniCPM5-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "AXERA-TECH/MiniCPM5-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AXERA-TECH/MiniCPM5-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AXERA-TECH/MiniCPM5-1B with Docker Model Runner:
docker model run hf.co/AXERA-TECH/MiniCPM5-1B
yongqiang commited on
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library_name: transformers
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license: apache-2.0
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base_model:
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pipeline_tag: text-generation
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tags:
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---
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# MiniCPM5-1B on AXERA NPU
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Ready-to-run
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This package has been validated through the OpenAI-compatible `axllm serve` API.
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## Supported Platform
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- [x] AX650 /
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##
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- Multi-turn text chat: supported
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## Performance
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The validated text prompts below were kept within one `128`-token prefill chunk. To avoid one-time startup effects, each `TTFT` row excludes the first request for that prompt pattern.
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| Scenario | Input tokens | Prefill chunks | TTFT | Decode |
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| Text smoke prompt | `24` | `1 x 128` | `160.34 ms avg` (`159.40-161.28 ms`) | `
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| Short front-end prompt | `14` | `1 x 128` | `157.76 ms avg` (`157.68-157.84 ms`) | `n/a (short reply)` |
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| Multi-turn text prompt | `40` | `1 x 128` | `159.89 ms avg` (`159.19-160.59 ms`) | `n/a (short reply)` |
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| Text + post axmodels | `1.4 GiB` |
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| Full package, excluding `.git` | `1.9 GiB` |
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## Package Layout
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├── bin/
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│ ├── axllm
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│ └── axllm.version.json
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├── llama_p128_l0_together.axmodel
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├── ...
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├── llama_p128_l23_together.axmodel
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```
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```bash
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chmod +x ./bin/axllm
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./bin/axllm serve .
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```
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Expected model id:
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AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047
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```
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Health check:
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```bash
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curl http://127.0.0.1:8000/health
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```
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```bash
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curl http://127.0.0.1:8000/v1/chat/completions \
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-d '{
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"model": "AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047",
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"messages": [
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"max_tokens": 32,
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"temperature": 0
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}'
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```
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```bash
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curl http://127.0.0.1:8000/v1/chat/completions \
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"model": "AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047",
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"messages": [
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"enable_thinking": true,
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"temperature": 0
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```
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```json
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{
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}
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```
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- This release does not include the original HuggingFace `safetensors` weights.
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library_name: transformers
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license: apache-2.0
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base_model:
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- openbmb/MiniCPM5-1B
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pipeline_tag: text-generation
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tags:
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- minicpm5
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- llm
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- thinking
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- axera
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- ax650
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language:
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---
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# MiniCPM5-1B on AXERA NPU
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Ready-to-run deployment package for `openbmb/MiniCPM5-1B` on AX650 / NPU3.
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- This release packages the AX650 `axllm` runtime together with the compiled text `.axmodel` files.
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- The packaged runtime is configured for text-only inference on AX650 / NPU3.
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- The packaged context layout is `prefill_len=128`, `kv_cache_len=2047`, and `prefill_max_token_num=1280`.
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- Thinking is disabled by default and can be enabled per request through the public OpenAI-compatible API.
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- The package includes the tokenizer, runtime config files, and the validated `bin/axllm` binary for board-side deployment.
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## Supported Platform
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- [x] AX650 / NPU3
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## Validated Devices
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This package has been validated on the following AX650-based device:
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- AX650 / NPU3 development board
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## Performance
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All measurements below were taken on AX650 / NPU3 with the packaged `axllm` runtime. `TTFT` stands for time to first token. In this table, `TTFT` is measured end-to-end from request arrival at `axllm serve` to the first generated token.
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The validated text prompts below were kept within one `128`-token prefill chunk. To avoid one-time startup effects, each `TTFT` row excludes the first request for that prompt pattern.
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| Scenario | Input tokens | Prefill chunks | TTFT | Decode |
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| Text smoke prompt | `24` | `1 x 128` | `160.34 ms avg` (`159.40-161.28 ms`) | `n/a (single-token reply)` |
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| Short front-end prompt | `14` | `1 x 128` | `157.76 ms avg` (`157.68-157.84 ms`) | `n/a (short reply)` |
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| Multi-turn text prompt | `40` | `1 x 128` | `159.89 ms avg` (`159.19-160.59 ms`) | `n/a (short reply)` |
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| Long text generation reference | `30` | `1 x 128` | `159.91 ms avg` (`159.34-160.49 ms`) | `17.96 token/s avg` |
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The packaged runtime uses the following context layout:
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- `prefill_len=128`
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- `kv_cache_len=2047`
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- `prefill_max_token_num=1280`
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The `Long text generation reference` row is the recommended sustained text-only decode figure for this package. Very short replies under-report decode speed because EOS and response-tail overhead become relatively larger.
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## Startup Runtime Footprint
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| `Flash total (24 text axmodels + post axmodel + embedding bin)` | `1.42 GiB` (`1456.71 MiB`) |
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| `Package flash total (excluding .git/)` | `1.43 GiB` (`1464.24 MiB`) |
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| `Runtime CMM requirement` | `Board-dependent; validate on your target AX650 CMM pool` |
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On the validated AX650 board, the packaged startup log confirmed `max_token_len=2047`, `prefill_len=128`, and `prefill_max_token_num=1280`. This README does not present one board's `remain_cmm(...)` value as a package-wide memory requirement, because the absolute remaining CMM pool depends on the board's global memory layout.
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## Package Layout
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├── bin/
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│ ├── axllm
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│ └── axllm.version.json
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├── config.json
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├── post_config.json
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├── minicpm5_tokenizer.txt
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├── model.embed_tokens.weight.bfloat16.bin
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├── llama_p128_l0_together.axmodel
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├── ...
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├── llama_p128_l23_together.axmodel
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└── llama_post.axmodel
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```
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This package uses a flat runtime layout. The packaged `axllm` binary reads the root-level runtime files directly, so no extra path arguments are required when you serve the repository root.
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## Direct Inference with `axllm`
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### Download the Model Package
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Download the release package from Hugging Face:
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```shell
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mkdir -p AXERA-TECH/MiniCPM5-1B
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hf download AXERA-TECH/MiniCPM5-1B --local-dir .
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```
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### Install `axllm`
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Option 1: use the validated binary included in this repository:
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```bash
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chmod +x ./bin/axllm
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```
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Option 2: install `axllm` from the public repository:
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```shell
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git clone -b axllm https://github.com/AXERA-TECH/ax-llm.git
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```
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Option 3: install with a one-line command:
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```
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Option 4: download the prebuilt binary from GitHub Actions CI:
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If you do not have a local build environment, download the latest CI-generated `axllm` binary from GitHub Actions:
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`https://github.com/AXERA-TECH/ax-llm/actions?query=branch%3Aaxllm`
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Then run:
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```
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### Run on the Board
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This package already includes a validated `bin/axllm` binary for AX650.
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From the package root on the board:
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chmod +x ./bin/axllm
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```
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Expected model id:
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AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047
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```
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Health check and model listing:
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```bash
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curl http://127.0.0.1:8000/health
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```
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Example health output:
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```json
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"status": "healthy"
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}
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```
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Example model list output:
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```json
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{
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+
"data": [
|
| 173 |
+
{
|
| 174 |
+
"created": 1780908633,
|
| 175 |
+
"id": "AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047",
|
| 176 |
+
"object": "model",
|
| 177 |
+
"owned_by": "openai-api"
|
| 178 |
+
}
|
| 179 |
+
],
|
| 180 |
+
"object": "list"
|
| 181 |
+
}
|
| 182 |
+
```
|
| 183 |
+
|
| 184 |
+
### Text Request
|
| 185 |
+
|
| 186 |
+
By default, this package uses no-thinking mode because the packaged `config.json` sets `enable_thinking=false`.
|
| 187 |
|
| 188 |
```bash
|
| 189 |
curl http://127.0.0.1:8000/v1/chat/completions \
|
|
|
|
| 191 |
-d '{
|
| 192 |
"model": "AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047",
|
| 193 |
"messages": [
|
| 194 |
+
{
|
| 195 |
+
"role": "user",
|
| 196 |
+
"content": "中国的首都是哪里?请只回答城市名。"
|
| 197 |
+
}
|
| 198 |
],
|
| 199 |
"max_tokens": 32,
|
| 200 |
"temperature": 0
|
| 201 |
}'
|
| 202 |
```
|
| 203 |
|
| 204 |
+
Example output:
|
| 205 |
|
| 206 |
+
```json
|
| 207 |
+
{
|
| 208 |
+
"choices": [
|
| 209 |
+
{
|
| 210 |
+
"message": {
|
| 211 |
+
"role": "assistant",
|
| 212 |
+
"content": "北京"
|
| 213 |
+
},
|
| 214 |
+
"finish_reason": "stop"
|
| 215 |
+
}
|
| 216 |
+
],
|
| 217 |
+
"model": "AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047",
|
| 218 |
+
"object": "chat.completion"
|
| 219 |
+
}
|
| 220 |
+
```
|
| 221 |
|
| 222 |
+
### Enable Thinking Per Request
|
| 223 |
+
|
| 224 |
+
To enable explicit reasoning output for a single request, pass top-level `enable_thinking=true`:
|
| 225 |
|
| 226 |
```bash
|
| 227 |
curl http://127.0.0.1:8000/v1/chat/completions \
|
|
|
|
| 229 |
-d '{
|
| 230 |
"model": "AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047",
|
| 231 |
"messages": [
|
| 232 |
+
{
|
| 233 |
+
"role": "user",
|
| 234 |
+
"content": "中国的首都是哪里?请简短思考后给最终答案。"
|
| 235 |
+
}
|
| 236 |
],
|
| 237 |
"enable_thinking": true,
|
| 238 |
+
"max_tokens": 384,
|
| 239 |
"temperature": 0
|
| 240 |
}'
|
| 241 |
```
|
| 242 |
|
| 243 |
+
Typical output shape:
|
| 244 |
+
|
| 245 |
+
```json
|
| 246 |
+
{
|
| 247 |
+
"choices": [
|
| 248 |
+
{
|
| 249 |
+
"message": {
|
| 250 |
+
"role": "assistant",
|
| 251 |
+
"content": "<think>\n...\n</think>\n\n中国的首都是北京。"
|
| 252 |
+
},
|
| 253 |
+
"finish_reason": "stop"
|
| 254 |
+
}
|
| 255 |
+
],
|
| 256 |
+
"model": "AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047",
|
| 257 |
+
"object": "chat.completion"
|
| 258 |
+
}
|
| 259 |
+
```
|
| 260 |
|
| 261 |
+
The Hugging Face-style request form is also accepted:
|
| 262 |
|
| 263 |
```json
|
| 264 |
{
|
|
|
|
| 268 |
}
|
| 269 |
```
|
| 270 |
|
| 271 |
+
When thinking mode is enabled, the service returns client-visible `<think>...</think>` markup so front ends can render reasoning and final answer separately. Follow-up turns also keep the official MiniCPM5 template behavior: previous assistant reasoning content is not reinserted into the next user prompt.
|
| 272 |
+
|
| 273 |
+
## Browser UI with `lite_webui`
|
| 274 |
+
|
| 275 |
+
If you want a browser UI for the OpenAI-compatible service started by `axllm serve`, use [AXERA-TECH/lite_webui](https://huggingface.co/AXERA-TECH/lite_webui/tree/main).
|
| 276 |
+
|
| 277 |
+
Set the OpenAI base URL to `http://<board-ip>:8000` and the model name to `AXERA-TECH/MiniCPM5-1B-AX650-C128-P1152-CTX2047`.
|
| 278 |
+
|
| 279 |
+
## Conversion References
|
| 280 |
+
|
| 281 |
+
If you need the original model files or want to rebuild the deployment artifacts, start with:
|
| 282 |
+
|
| 283 |
+
- Original Hugging Face model: [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)
|
| 284 |
+
- AXERA conversion and deployment workflow: [AXERA-TECH/MiniCPM5-1B.axera](https://github.com/AXERA-TECH/MiniCPM5-1B.axera)
|
| 285 |
+
|
| 286 |
+
## Discussion
|
| 287 |
|
| 288 |
+
- GitHub Issues
|
| 289 |
+
- QQ group: `139953715`
|
|
|