Text Generation
Transformers
Safetensors
OpenVINO
English
qwen2
openvino-export
conversational
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("themightywolfie/DeepCoder-1.5B-Preview-openvino")
model = AutoModelForCausalLM.from_pretrained("themightywolfie/DeepCoder-1.5B-Preview-openvino")
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]:]))Quick Links
This model was converted to OpenVINO from agentica-org/DeepCoder-1.5B-Preview using optimum-intel
via the export space.
First make sure you have optimum-intel installed:
pip install optimum[openvino]
To load your model you can do as follows:
from optimum.intel import OVModelForCausalLM
model_id = "themightywolfie/DeepCoder-1.5B-Preview-openvino"
model = OVModelForCausalLM.from_pretrained(model_id)
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agentica-org/DeepCoder-1.5B-Preview
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="themightywolfie/DeepCoder-1.5B-Preview-openvino") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)