MiniCPM5 2B
MiniCPM5 2B from openbmb. Source-based hardware guidance from its published configuration.
At a glance
- Parameters
- 2.52B
- Architecture
- llama
- Context length
- 131,072
- License
- apache-2.0
- Software
- llama.cpp, SGLang, vLLM, Transformers
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Add my hardwareWays to run it
llama.cpp · Windows, macOS, Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
llama-server -m MiniCPM5-2B-F16.gguf -a MiniCPM5-2B --port 8080 -ngl 99 -c 8192 --jinjaSGLang · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
python -m sglang.launch_server --model-path openbmb/MiniCPM5-2B --port 30000vLLM · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
vllm serve openbmb/MiniCPM5-2B --port 8000Transformers · See official guide
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "openbmb/MiniCPM5-2B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto",
)
messages = [{"role": "user", "content": "Who are you? Please briefly introduce yourself."}]
inputs = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
enable_thinking=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))Explore its uses
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