Instructions to use FredyRivera-dev/MiniCPM5-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FredyRivera-dev/MiniCPM5-1B with Transformers:

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

pipe = pipeline("text-generation", model="FredyRivera-dev/MiniCPM5-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FredyRivera-dev/MiniCPM5-1B")
model = AutoModelForCausalLM.from_pretrained("FredyRivera-dev/MiniCPM5-1B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FredyRivera-dev/MiniCPM5-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FredyRivera-dev/MiniCPM5-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FredyRivera-dev/MiniCPM5-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FredyRivera-dev/MiniCPM5-1B

SGLang

How to use FredyRivera-dev/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 "FredyRivera-dev/MiniCPM5-1B" \
    --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": "FredyRivera-dev/MiniCPM5-1B",
		"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 "FredyRivera-dev/MiniCPM5-1B" \
        --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": "FredyRivera-dev/MiniCPM5-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FredyRivera-dev/MiniCPM5-1B with Docker Model Runner:
```
docker model run hf.co/FredyRivera-dev/MiniCPM5-1B
```

FredyRivera-dev commited on about 21 hours ago

Commit

bf7afe5

verified ·

1 Parent(s): 29ca31f

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -124,7 +124,7 @@ During **post-training**, we proceed in three steps: **SFT**, **RL**, and **OPD*
 ```bash
 pip install "vllm>=0.21"
-vllm serve openbmb/MiniCPM5-1B --port 8000
 ```
 ```bash
@@ -142,7 +142,7 @@ curl http://localhost:8000/v1/chat/completions \
 ```bash
 pip install "sglang[srt]>=0.5.12"
-python -m sglang.launch_server --model-path openbmb/MiniCPM5-1B --port 30000
 ```
 ```bash
@@ -165,7 +165,7 @@ pip install -U "transformers>=5.6" accelerate torch
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_id = "openbmb/MiniCPM5-1B"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,

 ```bash
 pip install "vllm>=0.21"
+vllm serve FredyRivera-dev/MiniCPM5-1B --port 8000
 ```
 ```bash
 ```bash
 pip install "sglang[srt]>=0.5.12"
+python -m sglang.launch_server --model-path FredyRivera-dev/MiniCPM5-1B --port 30000
 ```
 ```bash
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "FredyRivera-dev/MiniCPM5-1B"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,