ERNIE-4.5-0.3B-Base-Paddle
116
11
300M
2 languages
license:apache-2.0
by
baidu
Language Model
OTHER
0.3B params
New
116 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
1GB+ RAM
Mobile
Laptop
Server
Quick Summary
AI model with specialized capabilities.
Device Compatibility
Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
1GB+ RAM
Code Examples
Using `transformers` librarypythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "baidu/ERNIE-4.5-0.3B-Base-PT"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
prompt = "Large language model is"
model_inputs = tokenizer([prompt], add_special_tokens=False, return_tensors="pt").to(model.device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1024
)
result = tokenizer.decode(generated_ids[0].tolist(), skip_special_tokens=True)
print("result:", result)Using `transformers` librarypythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "baidu/ERNIE-4.5-0.3B-Base-PT"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
prompt = "Large language model is"
model_inputs = tokenizer([prompt], add_special_tokens=False, return_tensors="pt").to(model.device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1024
)
result = tokenizer.decode(generated_ids[0].tolist(), skip_special_tokens=True)
print("result:", result)Deploy This Model
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