Automatic Speech Recognition
Transformers
Safetensors
phi4mm
text-generation
nlp
code
audio
speech-summarization
speech-translation
visual-question-answering
phi-4-multimodal
phi
phi-4-mini
custom_code
Eval Results
Instructions to use microsoft/Phi-4-multimodal-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Phi-4-multimodal-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="microsoft/Phi-4-multimodal-instruct", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-4-multimodal-instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Fine tuning the LLM backbone
#76
by antogrk - opened
I'm working on a text-only task (which ultimately will be expanded to a multimodal task in the future). I was wondering if it's possible to fine-tune only the language model that's used as the backbone of the model on only textual data. Also, is it possible to apply LoRA on the LLM and train only the linear layers of it?
It would be very helpful if you could provide a very basic script of how the fine-tuning can be done for a causal language modeling task.
antogrk changed discussion status to closed
antogrk changed discussion status to open