I'm having the same issue, i've fine tuned a Llama 7b model using peft, and got satisfying results in inference, but when i try to use SFTTrainer.save_model, and load the model from the saved files using LlamaForCausalLM.from_pretrained, the inference result seem to just be of the ...
🤗 Datasets originated from a fork of the awesome TensorFlow Datasets and the HuggingFace team want to deeply thank the TensorFlow Datasets team for building this amazing library. Well, let’s write some code In this example, we will start with a pre-trained BERT (uncased) model and fine-...
when I useAccelerator.save(unwrapped_model.state_dict(), path), the model will be saved twice (because I used two gpus) In the PyTorch DDP example, they save the model only when the rank is 0, which avoid saving the model multiple times. How can I do that with accelerate?
SelectSaveat the top of the page to save the changes to the managed virtual network. Configure a managed virtual network to allow only approved outbound Tip The managed VNet is automatically provisioned when you create a compute resource. When you allow automatic creation, it can take around30...
LangChain’sLLMwrappers make it easy to interface with local models. Use theHuggingFacePipelineintegration: fromlangchain.llmsimportHuggingFacePipelinefromtransformersimportpipeline# Create a text generation pipelinetext_gen_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)# Wrap the ...
1. Choose the right AI model The first step is to choose the right AI for your use case. There are many providers to choose from, e.g. OpenAI, Anthropic, Perplexity, Llama, Gemini or HuggingFace. Each has their own collection of AI models, and each of those has their own strengths,...
Evaluation is how you pick the right model for your use case, ensure that your model’s performance translates from prototype to production, and catch performance regressions. While evaluating Generative AI applications (also referred to as LLM applications) might look a little different, the same ...
save_strategy="epoch", learning_rate=2e-5, num_train_epochs=3, weight_decay=0.01, fp16=is_gpu_available, push_to_hub=True, per_device_train_batch_size=4, per_device_eval_batch_size=4, hub_model_id=huggingface_reponame ) trainer = Trainer( model=model, tokenizer=tokenizer, args=args...
The first thing you’ll need is, of course, the model itself. There are different ways to save a trained model, depending on its nature, as well as the machine learning framework that you use. This part of the job is relatively straightforward to figure out (here are the instructions in...
The plugin works with major AI platforms, including OpenAI, Anthropic, HuggingFace, Google, and Perplexity, among others. You’ll need to connect your own API key from whichever platform you want to use, and usage will depend on your plan or available credits with that provider. ...