I'm developing LLM agents using llama.cpp as inference engine. Sometimes I want to use models in safetensors format and there is a python script (https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py) to convert. Script is awesome, but minimum ...
I've been experimenting with candle and re-implementing ESRGAN in it. I ended up needing to convert a couple .pth files I have into .safetensors format in python in order to load them into the VarBuilder. I saw on the docs you say this s...
After training the Lora I got .bin files how to convert them to safetensors. Also I found that hugging face repo https://huggingface.co/comfyanonymous/flux_RealismLora_converted_comfyui with lora converted to comfyui, is that mean not co...
If this is not what you see, clickLoad Defaulton the right panel to return this default text-to-image workflow. 1. Selecting a model First, select a Stable DiffusionCheckpoint modelin theLoad Checkpointnode. Click on the model name to show a list of available models. If the node is too...
Download theInstantID controlnet model. Rename it to control_instant_id_sdxl.safetensors Put it in the folderstable-diffusion-webui > models > ControlNet. Google Colab If you use ourAUTOMATIC1111 Colab notebook, download and rename the two models above and put them in your Google Drive und...
I'm trying to confirm that my GPT-2 model is being trained from scratch, rather than using any pre-existing pre-trained weights. Here's my approach: Load the pre-trained GPT-2 XL model: I load a pre-trained GPT-2 XL model usingAutoModelForCausalLM.from_pretrained("gpt2-xl")and cal...
Hello.I tried to launch resnet pose_estimation programm in the docker container.I got error: [12/13/2023-11:01:18] [TRT] [E] 1: [stdArchiveReader.cpp::StdArchiveReader::35] Error Code 1: Serialization (Serialization assertion safeVersionRe...
(encode, batched=True) # Format the dataset to PyTorch tensors imdb_data.set_format(type='torch', columns=['input_ids', 'attention_ mask', 'label'])With our dataset loaded up, we can run some training code to update our BERT model on our labeled data:# Define the model model = ...
(encode, batched=True) # Format the dataset to PyTorch tensors imdb_data.set_format(type='torch', columns=['input_ids', 'attention_ mask', 'label'])With our dataset loaded up, we can run some training code to update our BERT model on our labeled data:# Define the model model = ...
transformers.DistilBertTokenizer.from_pretrained(model_name) model = transformers.DistilBertModel.from_pretrained(model_path) #Define a function to query the model def query_model(text): inputs = tokenizer(text, return_tensors="pt") outputs = model(**inputs) return outputs.last_hidden_state[:...