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# /// script
# requires-python = ">=3.12"
# dependencies = [
# "numpy",
# "einops",
# "torch",
# "transformers",
# "diffusers",
# "datasets",
# "accelerate",
# "timm",
# ]
# ///
try:
# Use a pipeline as a high-level helper
from transformers import pipeline
from transformers import AutoTokenizer
model_id = "HuggingFaceTB/SmolLM3-3B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline("text-generation", model=model_id, tokenizer=tokenizer)
messages = [
{"role": "user", "content": "Give me a brief explanation of gravity in simple terms."},
]
pipe(messages)
messages = [
{"role": "system", "content": "/no_think"},
{"role": "user", "content": "Give me a brief explanation of gravity in simple terms."},
]
pipe(messages)
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "HuggingFaceTB/SmolLM3-3B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
).to(device)
# prepare the model input
prompt = "Give me a brief explanation of gravity in simple terms."
messages_think = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages_think,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate the output
generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
# Get and decode the output
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
{"role": "system", "content": "/no_think"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate the output
generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
# Get and decode the output
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
tools = [
{
"name": "get_weather",
"description": "Get the weather in a city",
"parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "The city to get the weather for"}}}}
]
messages = [
{
"role": "user",
"content": "Hello! How is the weather today in Copenhagen?"
}
]
inputs = tokenizer.apply_chat_template(
messages,
enable_thinking=False, # True works as well, your choice!
xml_tools=tools,
add_generation_prompt=True,
tokenize=True,
return_tensors="pt"
).to(model.device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
with open('HuggingFaceTB_SmolLM3-3B_0.txt', 'w') as f:
f.write('Everything was good in HuggingFaceTB_SmolLM3-3B_0.txt')
except Exception as e:
with open('HuggingFaceTB_SmolLM3-3B_0.txt', 'w') as f:
import traceback
traceback.print_exc(file=f)
finally:
from huggingface_hub import upload_file
upload_file(
path_or_fileobj='HuggingFaceTB_SmolLM3-3B_0.txt',
repo_id='model-metadata/custom_code_execution_files',
path_in_repo='HuggingFaceTB_SmolLM3-3B_0.txt',
repo_type='dataset',
) |