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Merge pull request #49 from ScientistIzaak/add-device-parameter 

Add device parameter for TransformerModel in models.py
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Aymeric Roucher 2025-01-06 13:57:33 +01:00 committed by GitHub
parent 4ac9050368 7dbddb9d7e
commit 19143af576
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1 changed files with 16 additions and 4 deletions
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20
src/smolagents/models.py
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@ -29,6 +29,7 @@ import litellm
import logging
import os
import random
import torch
from huggingface_hub import InferenceClient
@ -279,9 +280,16 @@ class HfApiModel(Model):
class TransformersModel(Model):
"""This engine initializes a model and tokenizer from the given `model_id`."""
"""This engine initializes a model and tokenizer from the given `model_id`.
Parameters:
model_id (`str`, *optional*, defaults to `"HuggingFaceTB/SmolLM2-1.7B-Instruct"`):
The Hugging Face model ID to be used for inference. This can be a path or model identifier from the Hugging Face model hub.
device (`str`, optional, defaults to `"cuda"` if available, else `"cpu"`.):
The device to load the model on (`"cpu"` or `"cuda"`).
"""
def __init__(self, model_id: Optional[str] = None):
def __init__(self, model_id: Optional[str] = None, device: Optional[str] = None):
super().__init__()
default_model_id = "HuggingFaceTB/SmolLM2-1.7B-Instruct"
if model_id is None:
@ -290,15 +298,19 @@ class TransformersModel(Model):
f"`model_id`not provided, using this default tokenizer for token counts: '{model_id}'"
)
self.model_id = model_id
if device is None:
device = "cuda" if torch.cuda.is_available() else "cpu"
self.device = device
logger.info(f"Using device: {self.device}")
try:
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
self.model = AutoModelForCausalLM.from_pretrained(model_id)
self.model = AutoModelForCausalLM.from_pretrained(model_id).to(self.device)
except Exception as e:
logger.warning(
f"Failed to load tokenizer and model for {model_id=}: {e}. Loading default tokenizer and model instead from {model_id=}."
)
self.tokenizer = AutoTokenizer.from_pretrained(default_model_id)
self.model = AutoModelForCausalLM.from_pretrained(default_model_id)
self.model = AutoModelForCausalLM.from_pretrained(default_model_id).to(self.device)
def make_stopping_criteria(self, stop_sequences: List[str]) -> StoppingCriteriaList:
class StopOnStrings(StoppingCriteria):