repo stringclasses 147 values | number int64 1 172k | title stringlengths 2 476 | body stringlengths 0 5k | url stringlengths 39 70 | state stringclasses 2 values | labels listlengths 0 9 | created_at timestamp[ns, tz=UTC]date 2017-01-18 18:50:08 2026-01-06 07:33:18 | updated_at timestamp[ns, tz=UTC]date 2017-01-18 19:20:07 2026-01-06 08:03:39 | comments int64 0 58 ⌀ | user stringlengths 2 28 |
|---|---|---|---|---|---|---|---|---|---|---|
huggingface/transformers.js | 482 | How tot get the same output as the python library for the Resnet Model ? | ### Question
Hi,
I am trying to translate a python script to use it in my node server. Currently, I spawn a process to execute the python code, but I would like to improve response time by using the transformers.js version.
My problem is that I don't have the same output with the two codes.
The python output is a vector of dimension 2048
The js output is a vector of dimension 1000
It seems that my code has a problem as soon as the ImageProcessor step because the `inputs` are not equal
Python code :
```python
import torch
from transformers import logging
logging.set_verbosity_error()
from PIL import Image
class ImgToVec:
def __init__(self, pretrained_model="microsoft/resnet-50"):
from transformers import AutoImageProcessor, ResNetModel
self.pretrained_model = pretrained_model
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.image_processor = AutoImageProcessor.from_pretrained(pretrained_model)
self.model = ResNetModel.from_pretrained(pretrained_model).to(self.device)
def get_embedding(self, file):
im = Image.open(file)
inputs = self.image_processor(im, return_tensors="pt").to(self.device)
print(f"inputs : {inputs} dimensiosn : {inputs['pixel_values'].size()}")
with torch.no_grad():
outputs = self.model(**inputs)
return outputs.pooler_output[0, :, 0, 0].tolist()
# https://cdn-lfs.huggingface.co/repos/cf/db/cfdbeec4acf4145f96e47e07a9e161cade4dbce7cfad3ba24765bf1713d53ef3/d65b6f72943d5e2d4f7e5e4dedfb93aea0fbbda140ae7c3ee772124b579e07c4?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27football-match.jpg%3B+filename%3D%22football-match.jpg%22%3B&response-content-type=image%2Fjpeg&Expires=1704020059&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwNDAyMDA1OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9jZi9kYi9jZmRiZWVjNGFjZjQxNDVmOTZlNDdlMDdhOWUxNjFjYWRlNGRiY2U3Y2ZhZDNiYTI0NzY1YmYxNzEzZDUzZWYzL2Q2NWI2ZjcyOTQzZDVlMmQ0ZjdlNWU0ZGVkZmI5M2FlYTBmYmJkYTE0MGFlN2MzZWU3NzIxMjRiNTc5ZTA3YzQ%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=kWwcSkWcf8K62Tgr57HYD5VObZuozl3Jf%7EHV5alcyRA-gvbREfzgjMKU9rVOc84r0uwo9d3f-si-PoJ3GdyB8WObJFJWF0nE9SX5C-f3Nookj4SWevcJkLNgF27KqUPMhWWZ8B3KjEDvcxPirjHfc4fv87-uM%7EQIuazixgu0i8lXpzeSyKdZGNIc3zUG-hDzU3EKCGBWbwnGG9Yq%7Evz%7Eit-vvYc7i1AoYTAteZUP1ngDdywjwNf6VvvGqmyBdMcwVDiA0ShwAhW9Z3mqt%7EVz6HaYipWejY0mWmyVhyCWFtJOe9yrk%7ETJKr5cOV3yq6sM0jSheh3GuSd%7E2qYzjBsDVQ__&Key-Pair-Id=KVTP0A1DKRTAX
result = ImgToVec("microsoft/resnet-50").get_embedding("./football-match.jpg")
```
My JS code :
```ts
class ImgToVec {
public async getEmbedding(
file: string,
pretrainedModel = 'Xenova/resnet-50',
): Promise<number[]> {
const { ResNetForImageClassification, AutoProcessor, RawImage } =
await import('@xenova/transformers');
const model = await ResNetForImageClassification.from_pretrained(
pretrainedModel,
);
const imageProcessor = await AutoProcessor.from_pretrained(pretrainedModel);
const image = await RawImage.read(file);
const inputs = await imageProcessor(image);
const outputs = await model(inputs, { config: { embeddingSize: 2048 } });
console.log('inputs', inputs);
const embedding: number[] = outputs.data;
return embedding;
}
}
const imgToVec = new ImgToVec();
// https://cdn-lfs.huggingface.co/repos/cf/db/cfdbeec4acf4145f96e47e07a9e161cade4dbce7cfad3ba24765bf1713d53ef3/d65b6f72943d5e2d4f7e5e4dedfb93aea0fbbda140ae7c3ee772124b579e07c4?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27football-match.jpg%3B+filename%3D%22football-match.jpg%22%3B&response-content-type=image%2Fjpeg&Expires=1704020059&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcwNDAyMDA1OX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy5odWdnaW5nZmFjZS5jby9yZXBvcy9jZi9kYi9jZmRiZWVjNGFjZjQxNDVmOTZlNDdlMDdhOWUxNjFjYWRlNGRiY2U3Y2ZhZDNiYTI0NzY1YmYxNzEzZDUzZWYzL2Q2NWI2ZjcyOTQzZDVlMmQ0ZjdlNWU0ZGVkZmI5M2FlYTBmYmJkYTE0MGFlN2MzZWU3NzIxMjRiNTc5ZTA3YzQ%7EcmVzcG9uc2UtY29udGVudC1kaXNwb3NpdGlvbj0qJnJlc3BvbnNlLWNvbnRlbnQtdHlwZT0qIn1dfQ__&Signature=kWwcSkWcf8K62Tgr57HYD5VObZuozl3Jf%7EHV5alcyRA-gvbREfzgjMKU9rVOc84r0uwo9d3f-si-PoJ3GdyB8WObJFJWF0nE9SX5C-f3Nookj4SWevcJkLNgF27KqUPMhWWZ8B3KjEDvcxPirjHfc4fv87-uM%7EQIuazixgu0i8lXpzeSyKdZGNIc3zUG-hDzU3EKCGBWbwnGG9Yq%7Evz%7Eit-vvYc7i1AoYTAteZUP1ngDdywjwNf6VvvGqmyBdMcwVDiA0ShwAhW9Z3mqt%7EVz6HaYipWejY0mWmyVhyCWFtJOe9yrk%7ETJKr5cOV3yq6sM0jSheh3GuSd%7E2qYzjBsDVQ__&Key-Pair-Id=KVTP0A1DKRTAX
imgToVec.getEmbedding('./football-match.jpg').then((embedding) => {
console.log(embedding);
});
```
Any ideas how to solve my problem please ? | https://github.com/huggingface/transformers.js/issues/482 | closed | [
"question"
] | 2023-12-28T11:38:20Z | 2024-01-10T15:04:22Z | null | Spoutnik97 |
huggingface/diffusers | 6,370 | How to use diffusers lora in the AUTOMATIC1111 | Thanks for your great work, I use the train_text_to_image_lora_sdxl.py to train my custom dataset and get these output, And I get the good result. But I want to use the AUTOMATIC1111 to use the lora weight, I move the pytorch_lora_weights to the AUTOMATIC1111 lora folder But get the error report:`AssertionError: conversion failed: lora_unet_input_blocks_4_1_transformer_blocks_0_attn1_to_k_lora_A_weight. the model may not be trained by `sd-scripts``
how can I do to convert the lora model weight to which format AUTOMATIC1111 can accpet.
| https://github.com/huggingface/diffusers/issues/6370 | closed | [] | 2023-12-28T06:17:19Z | 2024-01-02T13:38:26Z | null | chongxian |
huggingface/computer-vision-course | 163 | How to include "What you'll learn" section for this course? | Hello everyone,
Our PR for Fundamentals of Computer Vision was merged a few days back. After that, one thing we still need to acknowledge based on your [feedback](https://github.com/johko/computer-vision-course/issues/38#issuecomment-1764502604) on our chapter outline is building a demo using Gradio to give learners a taste of what they'll learn. One of our teammates, @aman06012003 , created a simple [Cat vs Dog classifier deployed it on Hugging face spaces](https://ak0601-cat-dog-classifier.hf.space/), which we want you to take a look at and give feedback.
Once the demo is finalized, there are two ways to include it, referring to the [Hugging Face Audio Course](https://huggingface.co/learn/audio-course/chapter0/introduction). One is to create a new .mdx file in our fundamentals folder. The other is to create a new chapter - Welcome to the course, where we add what you'll learn, community notes, etc. We are still determining the optimal path, so please guide us.
Team members - @seshu-pavan , @bellabf , @aman06012003
bcc - @MKhalusova @johko @merveenoyan @lunarflu
Best,
Fundamentals team | https://github.com/huggingface/computer-vision-course/issues/163 | closed | [] | 2023-12-27T12:41:26Z | 2024-04-26T13:36:59Z | null | seshupavan |
huggingface/transformers | 28,260 | How to set pad_token of Llava for batched generation and training? | Hello, @younesbelkada I'm trying to use Llava for batched generation, using the default pad_token. here is the script:
```python
import json
from PIL import Image
from transformers import AutoProcessor, LlavaForConditionalGeneration,AutoTokenizer
from torch.utils.data import Dataset,DataLoader
import torch
import os
from tqdm import tqdm
DATA_ROOT = "/mnt/gozhang/code/LLaVA/playground/data/eval/mm-vet"
processor = AutoProcessor.from_pretrained("/mnt/gozhang/ckpts/llava-1.5-7b-hf")
tokenizer = AutoTokenizer.from_pretrained("/mnt/gozhang/ckpts/llava-1.5-7b-hf")
class MMVetDataset(Dataset):
def __init__(self,data_root) -> None:
super().__init__()
self.data_root = data_root
with open(os.path.join(data_root, "mm-vet.json"), "r") as f:
data = json.load(f)
self.data = [(k,v) for k,v in data.items()]
def __len__(self):
return len(self.data)
def __getitem__(self, index):
return {'id':self.data[index][0],
'image':os.path.join(self.data_root,'images',self.data[index][1]['imagename']),
'question':"USER: <image>\n"+self.data[index][1]['question']+" ASSISTANT:"}
def collator(batch):
ids = [b['id'] for b in batch]
questions = [b['question'] for b in batch]
images = [Image.open(b['image']) for b in batch]
inputs = processor(text=questions,images=images,return_tensors="pt",padding=True)
return ids,inputs
model = LlavaForConditionalGeneration.from_pretrained("/mnt/gozhang/ckpts/llava-1.5-7b-hf",torch_dtype=torch.float16)
model.to('cuda')
#model.to(torch.float16)
dataset = MMVetDataset(DATA_ROOT)
dataloader = DataLoader(dataset,batch_size=16,collate_fn=collator)
results = {}
bar = tqdm(total=len(dataset))
model.eval()
with torch.inference_mode():
for ids, inputs in dataloader:
inputs.to('cuda')
inputs['pixel_values'] = inputs['pixel_values'].half()
outputs = model.generate(**inputs,temperature=0.2,do_sample=True,max_new_tokens=1024,use_cache=True)
input_token_len = inputs['input_ids'].shape[1]
responses=tokenizer.batch_decode(outputs[:, input_token_len:], skip_special_tokens=True, clean_up_tokenization_spaces=False)
for id,res in zip(ids,responses):
results[id]=res
bar.update(len(responses))
with open('mmvet_result.json','w') as f:
json.dump(results,f,indent=4)
```
But when generating the fifth batch, it reports `RuntimeError: probability tensor contains either inf, nan or element < 0`. Then I try different pad_token, setting `processor.tokenizer.pad_token = processor.tokenizer.unk_token` (following the raw llava codebase), or `processor.tokenizer.pad_token = processor.tokenizer.eos_token`(following the common setting), or `processor.tokenizer.pad_token = processor.tokenizer.bos_token`(following this [issue](https://discuss.huggingface.co/t/llama2-pad-token-for-batched-inference/48020)). And I find that only setting pad_token to eos_token can avoid the error.
I wonder what's the effect of different pad_token during batched generation, and what's the root cause of this error, and how to set the correct pad_token for training the model? | https://github.com/huggingface/transformers/issues/28260 | closed | [] | 2023-12-27T12:17:02Z | 2024-02-05T02:43:32Z | null | TideDra |
huggingface/transformers | 28,259 | How to add new merge rules in AutoTokenizer | ### Model description
I'm training new tokenizer from llama2, however, it seems that BPE tokenizer will clear the origin "vocab" and "merge" dict, and the training result is highly bias in my own datasets (about 6M C function) with some ugly tokens.
I wonder that is it possible to train a tokenizer from llama2 with the origin "vocab" and "merge" dict unchanged, only add some new vocab and merge rules from our datasets to support my requirement?
### Open source status
- [ ] The model implementation is available
- [ ] The model weights are available
### Provide useful links for the implementation
_No response_ | https://github.com/huggingface/transformers/issues/28259 | open | [
"New model"
] | 2023-12-27T12:15:26Z | 2023-12-27T12:15:26Z | null | Sandspeare |
huggingface/accelerate | 2,289 | [QUESTION] why stage3_gather_16bit_weights_on_model_save is set to false no matter what value of it in deepspeed config | [`accelerator._prepare_deepspeed()`](https://github.com/huggingface/accelerate/blob/d08c23c20975f39393b431143237c193733e7bb8/src/accelerate/accelerator.py#L1464C13-L1464C82) looks to force the `stage3_gather_16bit_weights_on_model_save` to `false`, which should raise an exception in [`accelerator.get_state_dict()`](https://github.com/huggingface/accelerate/blob/d08c23c20975f39393b431143237c193733e7bb8/src/accelerate/accelerator.py#L2985C17-L2985C68). Additionally, [`trainer.save_model()`](https://github.com/huggingface/transformers/blob/c48787f347bd604f656c2cfff730e029c8f8c1fe/src/transformers/trainer.py#L2827C17-L2827C77) invoke above function, then catch this exception and raise another exception. Yet, the log seems totally fine. I'm confused... Why this happened? | https://github.com/huggingface/accelerate/issues/2289 | closed | [] | 2023-12-27T10:04:28Z | 2024-01-05T06:59:16Z | null | LaniakeaS |
huggingface/diffusers | 6,352 | how to choose save precision for lora file in training | I'm confused about my lora precision(fp16,bf16,float) and whether i can choose precision about my lora weights. I searched for the params about the **StableDiffusionXLPipeline.save_lora_weights** function used to save lora in sdxl text2img training script and didnt find params like 'save_precision' or sth.
anyone can help? thanks!
| https://github.com/huggingface/diffusers/issues/6352 | closed | [] | 2023-12-27T09:02:47Z | 2023-12-28T08:21:29Z | null | DoctorTar |
huggingface/transformers.js | 481 | Why do certain models not load? | ### Question
I was keen to try:
https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0
I tried:
```ts
import {
AutoModelForCausalLM,
AutoTokenizer,
} from '@xenova/transformers';
const autoTokenizer = await AutoTokenizer.from_pretrained(
'Upstage/SOLAR-10.7B-Instruct-v1.0',
);
const model = await AutoModelForCausalLM.from_pretrained(
'Upstage/SOLAR-10.7B-Instruct-v1.0',
);
```
But it fails with an error:
```ts
Error: Could not locate file: "https://huggingface.co/Upstage/SOLAR-10.7B-Instruct-v1.0/resolve/main/onnx/decoder_model_merged_quantized.onnx".
```
Is this an error on my side, is the model incompatible, ... ? | https://github.com/huggingface/transformers.js/issues/481 | open | [
"question"
] | 2023-12-27T01:44:52Z | 2024-05-10T18:21:57Z | null | adaboese |
huggingface/peft | 1,298 | [Question] What is the main difference between "modules_to_save" and "target_modules"? | Hi, in my work I need to add some special token to LLAMA, so I need to train the parameter of ["embed_tokens", "lm_head"] for both layers, what confuses me is that should I add this parameter to LoraConfig's "modules_to_save " or "target_modules"? Looking forward to your reply! | https://github.com/huggingface/peft/issues/1298 | closed | [] | 2023-12-26T07:37:05Z | 2024-02-03T15:03:27Z | null | SatireY |
huggingface/datasets | 6,534 | How to configure multiple folders in the same zip package | How should I write "config" in readme when all the data, such as train test, is in a zip file
train floder and test floder in data.zip | https://github.com/huggingface/datasets/issues/6534 | open | [] | 2023-12-26T03:56:20Z | 2023-12-26T06:31:16Z | null | d710055071 |
huggingface/trl | 1,140 | How to additional finetune with new data from previous adapter ? | Hi All, I have question about finetune. Currently I use SFTtrainer for finetuning Llama2-7b-chat model and save it in adapter format. The question is, In case of I want to additional finetune with new data from previous adapter, How I could to do. Normally I additional finetune by merge adapter with base model before finetune it. I'm not sure my method that i do is correct or not. Or have any other method that easily more than this.
Thank | https://github.com/huggingface/trl/issues/1140 | closed | [] | 2023-12-25T04:19:34Z | 2024-02-01T15:05:24Z | null | SiraHaruethaipree |
huggingface/optimum | 1,613 | Convert opus translation to onnx and run inference from it | To convert I use this snippet
```
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers.models.marian import MarianOnnxConfig
import onnxruntime as ort
model_ckpt = "Helsinki-NLP/opus-mt-en-zh"
tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
ref_model = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt)
feature = "seq2seq-lm"
onnx_path = f"onnx/{model_ckpt}-{feature}/"
!python -m transformers.onnx --model={model_ckpt} --atol=1e-4 --feature={feature} {onnx_path}
```
To inference (which is not running) I use this snippet
```
import torch
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForSeq2SeqLM
model = ORTModelForSeq2SeqLM.from_pretrained("./onnx/Helsinki-NLP/opus-mt-en-zh-seq2seq-lm")
```
The error is
```
FileNotFoundError: Could not find any ONNX model file for the regex ['(.*)?decoder(.*)?with_past(.*)?\\.onnx']
```
Maybe it tries to find model.onnx but in the folder there are 2 onnx : decoder_model.onnx and encoder_model.onnx
I think the snippet is from 2022, Is there any changes ?
Thanks | https://github.com/huggingface/optimum/issues/1613 | closed | [] | 2023-12-25T04:04:47Z | 2025-04-29T01:45:20Z | 5 | x4080 |
huggingface/chat-ui | 658 | chat-ui do not support TGI http url when deploy publicly | hi @nsarrazin, the chat-ui works well locally
~~~
# .env.local
endpoints: [{"type":"tgi","url":"http://127.0.0.1:8080/generate_stream"}]
~~~
but if deploy it in public, when chat from the external brower, get the 403 error:
~~~
403
You don't have access to this conversation. If someone gave you this link, ask them to use the 'share' feature instead.
~~~
this issue may be related this issue https://github.com/huggingface/chat-ui/issues/364
it seems that: chat-ui only support the https url, but the TGI only support the http url. it has conflicts. how to fix this?
| https://github.com/huggingface/chat-ui/issues/658 | closed | [] | 2023-12-25T03:08:10Z | 2024-04-25T16:27:52Z | 1 | walkacross |
huggingface/transformers.js | 475 | How to use your own models | ### Question
Hey I really appreciate your work here!
I'm very interested in setting up a perfect RAG pipeline / flow and therefore I need a good document extraction with table-transformers and layout detection.
Example :
https://github.com/deepdoctection/deepdoctection
Where I'd use
https://huggingface.co/microsoft/layoutlmv3-base
https://huggingface.co/microsoft/table-transformer-detection
I could ask you if would add one of these but I want to try it myself.
As I understood I can use your script and deploy it on my huggingface.co so I could consume it, is this right?
| https://github.com/huggingface/transformers.js/issues/475 | closed | [
"question"
] | 2023-12-24T21:38:02Z | 2024-05-15T09:32:26Z | null | DomEscobar |
huggingface/datasets | 6,530 | Impossible to save a mapped dataset to disk | ### Describe the bug
I want to play around with different hyperparameters when training but don't want to re-map my dataset with 3 million samples each time for tens of hours when I [fully fine-tune SDXL](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py).
After I do the mapping like this:
```
train_dataset = train_dataset.map(compute_embeddings_fn, batched=True)
train_dataset = train_dataset.map(
compute_vae_encodings_fn,
batched=True,
batch_size=16,
)
```
and try to save it like this:
`train_dataset.save_to_disk("test")`
i get this error ([full traceback](https://pastebin.com/kq3vt739)):
```
TypeError: Object of type function is not JSON serializable
The format kwargs must be JSON serializable, but key 'transform' isn't.
```
But what is interesting is that pushing to hub works like that:
`train_dataset.push_to_hub("kopyl/mapped-833-icons-sdxl-1024-dataset", token=True)`
Here is the link of the pushed dataset: https://huggingface.co/datasets/kopyl/mapped-833-icons-sdxl-1024-dataset
### Steps to reproduce the bug
Here is the self-contained notebook:
https://colab.research.google.com/drive/1RtCsEMVcwWcMwlWURk_cj_9xUBHz065M?usp=sharing
### Expected behavior
It should be easily saved to disk
### Environment info
NVIDIA A100, Linux (NC24ads A100 v4 from Azure), CUDA 12.2.
[pip freeze](https://pastebin.com/QTNb6iru) | https://github.com/huggingface/datasets/issues/6530 | open | [] | 2023-12-23T15:18:27Z | 2023-12-24T09:40:30Z | 1 | kopyl |
huggingface/sentence-transformers | 2,392 | util.paraphrase_mining returning scores only above 0.98 | Hey,
I'm using util.paraphrase_mining (sentence-transformers v2.2.2) to get similarity scores (cosine) in a corpus of ~20k texts with the encoder model being all-MiniLM-L6-v2 and with the parameters query_chunk_size=500, corpus_chunk_size=1000, top_k=500000, max_pairs=5000000.
The returned list of triplets contain scores only above 0.98. I was wondering why the lower scores don't appear.
Thanks in advance for your answer! | https://github.com/huggingface/sentence-transformers/issues/2392 | closed | [
"question"
] | 2023-12-23T13:00:27Z | 2024-01-29T14:20:33Z | null | sinangokce |
huggingface/chat-ui | 656 | Web Search failed with "Invalid URL" | 
Why is this happening? It seems to happen regardless of whether I have USE_LOCAL_WEBSEARCH set to true or false.
```
SERPAPI_KEY=<my key>
USE_LOCAL_WEBSEARCH=true
MODELS=`[
{
"name": "mistralai/Mixtral-8x7b-Instruct-v0.1",
"displayName": "mistralai/Mixtral-8x7b-Instruct-v0.1",
"description": "Mixtral-8x7b-Instruct-v0.1 is a state of the art language model, based on a mixture of experts, that outperforms ChatGPT.",
"websiteUrl": "https://www.aaprintsupplyco.com",
"preprompt": "",
"chatPromptTemplate" : "<s>{{#each messages}}{{#ifUser}}[INST] {{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}}{{content}} [/INST]{{/ifUser}}{{#ifAssistant}}{{content}}</s>{{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.4,
"top_p": 0.95,
"top_k": 50,
"truncate": 31768,
"max_new_tokens": 2048,
"stop": ["[INST]","</s>"]
},
"endpoints" : [{
"type": "openai",
"baseURL": "https://api.together.xyz/v1"
}],
"promptExamples": [
{
"title": "Write a blog post",
"prompt": "Your goal is to help me create a compelling blog post about a topic.\nYou will follow the following process:\n\n1. Ask me for the topic of the blog post.\n2. After I provide my answer you will need to collect some additional information by going through the next steps:\na) Questions (ask any relevant questions pertaining to what additional information is needed from me to write a good blog post).\n\nOnce you have enough information, or once I say I am done, you will write the blog post."
}, {
"title": "Improve my English",
"prompt": "I want you to act as an English grammar and spelling corrector and improver. I will speak to you and you will answer in the corrected and improved version of my text, in English. I want you to replace my simplified A0-level words and sentences with improved, higher level English words and sentences. Keep the meaning same, but make them sound better. I want you to only reply the correction, the improvements and nothing else, do not write explanations. If there is nothing to improve, just reply with the original text."
}, {
"title": "Assist in a task",
"prompt": "I want you to be my Prompt engineer. Your goal is to help me craft the best possible instruction prompt for my needs. The prompt will be used by you, an AI model. You will follow the following process:\n\n1. Your first response will be to simply ask me what the task I want to accomplish. \n2. After I provide my answer and you will generate a first iteration of the prompt, but we will need to improve it through continual iterations by going through the next steps. You will generate two sections:\na) Revised prompt (provide your rewritten prompt, it should be clear, concise, and easily understood by you),\nb) Questions (ask any relevant questions pertaining to what additional information is needed from me to improve the prompt).\n3. We will continue this iterative process with me providing additional information to you and you updating the prompt in the Revised prompt section until I say we are done.\n\nOnly after I say I am done, will you provide a response to the revised prompt."
}
]
},
{
"name": "openchat/openchat-3.5-1210",
"displayName": "openchat/openchat-3.5-1210",
"description": "OpenChat 3.5 is the #1 model on MT-Bench, with only 7B parameters. Small and fast.",
"websiteUrl": "https://www.aaprintsupplyco.com",
"preprompt": "",
"chatPromptTemplate" : "<s>{{#each messages}}{{#ifUser}}GPT4 Correct User: {{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}}{{content}}<|end_of_turn|>GPT4 Correct Assistant:{{/ifUser}}{{#ifAssistant}}{{content}}<|end_of_turn|>{{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.4,
"top_p": 0.95,
"top_k": 50,
"truncate": 8192,
"max_new_tokens": 1024,
"stop": ["<|end_of_turn|>","</s>"]
},
"endpoints" : [{
"type": "openai",
"baseURL": "https://api.together.xyz/v1"
}],
"promptExamples": [
{
"title": "Write a blog post",
"prompt": "Your goal is to help me create a compelling blog post about a topic.\nYou will follow the following process:\n\n1. Ask me for the topic of the blog post.\n2. After I provide my answer you will need to collect some additional information by going through the next steps:\na) Questions (ask any relevant questions pertaining to what additional information is needed from me to write a good blog post).\n\nOnce you have enough information, or once I say I am done, you will write the blog post."
}, {
"titl | https://github.com/huggingface/chat-ui/issues/656 | closed | [] | 2023-12-22T19:19:34Z | 2024-01-09T05:45:13Z | 5 | gururise |
huggingface/chat-ui | 655 | Generation failed (Module.summarize) when using TogetherAI openai compatible endpoint | TogetherAI offers an [OpenAI compatible endpoint](https://docs.together.ai/docs/openai-api-compatibility). When using this endpoint with the model setup as follows:
```
MODELS=`[
{
"name": "mistralai/Mixtral-8x7b-Instruct-v0.1",
"displayName": "Mixtral-8x7b",
"endpoints" : [{
"type": "openai",
"baseURL": "https://api.together.xyz/v1"
}],
"promptExamples": [
{
"title": "Write an email from bullet list",
"prompt": "As a restaurant owner, write a professional email to the supplier to get these products every week: \n\n- Wine (x10)\n- Eggs (x24)\n- Bread (x12)"
}, {
"title": "Code a snake game",
"prompt": "Code a basic snake game in python, give explanations for each step."
}, {
"title": "Assist in a task",
"prompt": "How do I make a delicious lemon cheesecake?"
}
]
}
]`
TASK_MODEL=`{
"name": "openchat/openchat-3.5-1210",
"chatPromptTemplate" : "<s>{{#each messages}}{{#ifUser}}GPT4 Correct User: {{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}}{{content}}<|end_of_turn|>GPT4 Correct Assistant:{{/ifUser}}{{#ifAssistant}}{{content}}<|end_of_turn|>{{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.1,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 3072,
"max_new_tokens": 1024,
"stop": ["<|end_of_turn|>","</s>"]
},
"endpoints" : [{
"type": "openai",
"baseURL": "https://api.together.xyz/v1"
}]
}`
```
Inference and streaming work just fine with the output displayed in the chat window; however, in the console, the **following error always appears** after every interaction, and the conversation titles are never summarized.
```
Error: Generation failed
at Module.generateFromDefaultEndpoint (/home/gene/Downloads/chat-ui/src/lib/server/generateFromDefaultEndpoint.ts:22:9)
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async Module.summarize (/home/gene/Downloads/chat-ui/src/lib/server/summarize.ts:28:10)
at async eval (/home/gene/Downloads/chat-ui/src/routes/conversation/[id]/+server.ts:167:26)
```
Even if I try setting TASK_MODEL='mistralai/Mixtral-8x7b-Instruct-v0.1', I still get this error. | https://github.com/huggingface/chat-ui/issues/655 | open | [] | 2023-12-22T17:34:59Z | 2024-01-23T05:14:26Z | 1 | gururise |
huggingface/datasets | 6,529 | Impossible to only download a test split | I've spent a significant amount of time trying to locate the split object inside my _split_generators() custom function.
Then after diving [in the code](https://github.com/huggingface/datasets/blob/5ff3670c18ed34fa8ddfa70a9aa403ae6cc9ad54/src/datasets/load.py#L2558) I realized that `download_and_prepare` is executed before! split is passed to the dataset builder in `as_dataset`.
If I'm not missing something, this seems like bad design, for the following use case:
> Imagine there is a huge dataset that has an evaluation test set and you want to just download and run just to compare your method.
Is there a current workaround that can help me achieve the same result?
Thank you, | https://github.com/huggingface/datasets/issues/6529 | open | [] | 2023-12-22T16:56:32Z | 2024-02-02T00:05:04Z | 2 | ysig |
huggingface/transformers.js | 470 | How to convert a model with .pt tail | ### Question
I'm new to this area,I'm woundering how to convert a model with .pt tail?thanks a lot | https://github.com/huggingface/transformers.js/issues/470 | open | [
"question"
] | 2023-12-22T10:20:16Z | 2023-12-23T20:46:37Z | null | Bzayyz |
huggingface/transformers.js | 469 | How to convert a model with .pt tail | ### Question
I'm new to this area,I'm woundering how to convert a model with .p2 tail?thanks a lot | https://github.com/huggingface/transformers.js/issues/469 | closed | [
"question"
] | 2023-12-22T10:20:05Z | 2023-12-22T10:20:54Z | null | Bzayyz |
huggingface/chat-ui | 650 | chat-ui docker image failed to connect the mongo docker contrainer | step 1: build the chat-ui image
~~~
docker build -t chat-ui -f ./Dockerfile.local .
~~~
step 2:
~~~
# bind the 27016
docker run -d -p 27016:27017 --name mongo-chatui mongo:latest
~~~
step 3: run a contrainer
~~~
# add a .env.local config
MONGODB_URL=mongodb://localhost:27016
HF_TOKEN=<your access token>
~~~
~~~
docker run --rm --mount type=bind,source="$(pwd)/.env.local",target=/app/.env.local -p 3000:3000 chat-ui
~~~
## results: when load localhost:3000
~~~
MongoServerSelectionError: connect ECONNREFUSED 127.0.0.1:27016
at Timeout._onTimeout (/app/node_modules/mongodb/lib/sdam/topology.js:278:38)
at listOnTimeout (node:internal/timers:573:17)
at process.processTimers (node:internal/timers:514:7) {
reason: TopologyDescription {
type: 'Unknown',
servers: Map(1) { 'localhost:27016' => [ServerDescription] },
stale: false,
compatible: true,
heartbeatFrequencyMS: 10000,
localThresholdMS: 15,
setName: null,
maxElectionId: null,
maxSetVersion: null,
commonWireVersion: 0,
logicalSessionTimeoutMinutes: null
},
code: undefined,
[Symbol(errorLabels)]: Set(0) {}
}
MongoTopologyClosedError: Topology is closed
at /app/node_modules/mongodb/lib/sdam/topology.js:218:46 {
[Symbol(errorLabels)]: Set(0) {}
}
MongoTopologyClosedError: Topology is closed
at processWaitQueue (/app/node_modules/mongodb/lib/sdam/topology.js:514:46)
at Topology.selectServer (/app/node_modules/mongodb/lib/sdam/topology.js:283:9)
at Topology.<anonymous> (/app/node_modules/mongodb/lib/sdam/topology.js:42:94)
at node:internal/util:442:7
at new Promise (<anonymous>)
at Topology.selectServerAsync (node:internal/util:428:12)
at executeOperationAsync (/app/node_modules/mongodb/lib/operations/execute_operation.js:74:35)
at /app/node_modules/mongodb/lib/operations/execute_operation.js:12:45
at maybeCallback (/app/node_modules/mongodb/lib/utils.js:293:21)
at executeOperation (/app/node_modules/mongodb/lib/operations/execute_operation.js:12:38) {
[Symbol(errorLabels)]: Set(0) {}
}
~~~
@nsarrazin | https://github.com/huggingface/chat-ui/issues/650 | open | [
"support",
"docker"
] | 2023-12-22T08:34:52Z | 2025-05-25T20:37:17Z | 6 | walkacross |
huggingface/chat-ui | 649 | Formatting is incorrect when using LiteLLM (Together.ai) | I'm using Mixtral-7b-Instruct-v0.1 via [LiteLLM](https://github.com/BerriAI/litellm) to provide a OpenAI compatible API to together.ai where the model is hosted.
Everything works fine, including streaming; however, the formatting is messed up as shown. Any ideas why?
| https://github.com/huggingface/chat-ui/issues/649 | closed | [
"bug",
"question",
"front",
"models"
] | 2023-12-22T05:46:37Z | 2023-12-22T17:11:09Z | null | gururise |
huggingface/distil-whisper | 67 | I can only use its encoder to extract audio features, right? How should I use it? Could you provide an example | I can only use its encoder to extract audio features, right? How should I use it? Could you provide an example | https://github.com/huggingface/distil-whisper/issues/67 | open | [] | 2023-12-22T03:50:32Z | 2024-01-15T18:07:34Z | null | wvinzh |
huggingface/transformers.js | 468 | Node.js | ### Question
Will this library work with Node.js? | https://github.com/huggingface/transformers.js/issues/468 | closed | [
"question"
] | 2023-12-21T23:03:36Z | 2023-12-21T23:06:53Z | null | Julianbullmagic |
huggingface/gsplat.js | 47 | I don't need to load loading and onProgress，When data is loaded, how can I render it on the interface immediately? | I don't need to load loading，When data is loaded, how can I render it on the interface immediately? I see Class Loader
Nothing's been done there | https://github.com/huggingface/gsplat.js/issues/47 | closed | [] | 2023-12-21T20:13:52Z | 2024-01-29T20:15:01Z | null | did66 |
huggingface/candle | 1,463 | How to introduce openai triton in candle? | The handwritten CUDA operator is very complicated. How can we use openai triton in candle to simplify this process. ：） | https://github.com/huggingface/candle/issues/1463 | open | [] | 2023-12-21T18:42:38Z | 2024-01-01T11:56:29Z | null | tyfeng1997 |
huggingface/transformers | 28,179 | How to fine tune facebook/esm2_t33_650M_UR50D | ### System Info
How to fine tune facebook/esm2_t33_650M_UR50D？It's too big and the model.half() couldn't work. Besids, i always met the error : CUDA error: CUBLAS_STATUS_INTERNAL_ERROR when calling `cublasSgemm( handle, opa, opb, m, n, k, &alpha, a, lda, b, ldb, &beta, c, ldc). Is it possible that the model in the huggingface is wrong?
The following is the script:
from os.path import join
import os
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.utils.data as data
import transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer
from datasets import Dataset,load_metric
from sklearn.model_selection import train_test_split
#os.environ['CUDA_VISIBLE_DEVICES'] = '1'
CURRENT_DIR = os.getcwd()
check_point = join(CURRENT_DIR,"esm1b_t33_650M_UR50S")
#Data processing
def process_tsv(file):
sequences = list()
labels = list()
df = pd.read_csv(file,sep="\t")
for ind in df.index:
sequences.append(df["sequence"][ind])
labels.append(df["label"][ind])
return sequences,labels
def tokenize_add_label(sequences, labels, tokenizer):
"""This function takes sequences and labels creates a Dataset containing tokenized sequences and add labels to it
args:
sequences (str): a list of sequences
labels (int): a list of labels
tokenizer : a pre-trained tokenizer
return:
Dataset: tokenized sequences and associated labels)"""
sequences_tokenized = tokenizer(sequences, padding=True, truncation=True)
sequences_tokenized = torch.float16(sequences_tokenized)
labels = torch.tensor(labels)
labels = labels.long()
sequences_dataset = Dataset.from_dict(sequences_tokenized)
sequences_dataset = sequences_dataset.add_column("labels", labels)
return sequences_dataset
sequences,labels = process_tsv(join(CURRENT_DIR,"example.tsv"))
tokenizer = AutoTokenizer.from_pretrained(check_point)
sequences_dataset = tokenize_add_label(sequences,labels,tokenizer)
num_labels = max(labels)+1
model = AutoModelForSequenceClassification.from_pretrained(check_point,num_labels=num_labels)
#device = "cuda" if torch.cuda.is_available() else "cpu"
#model.to(device)
model.cuda()
#model = model.half()
#model.enable_input_require_grads()
model_name = check_point.split("/")[-1]
trainer_dir = f"{model_name}-finetuned-model_esm-1b_on_7beta"
if not os.path.exists(trainer_dir):
os.mkdir(trainer_dir)
batch_size = 1
training_args = transformers.TrainingArguments(
output_dir=trainer_dir, # output directory
overwrite_output_dir=True,
num_train_epochs=3, # total number of training epochs
per_device_train_batch_size=batch_size, # batch size per device during training
per_device_eval_batch_size=batch_size, # batch size for evaluation
learning_rate=2e-5,
warmup_steps=500, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir=trainer_dir, # directory for storing logs
logging_steps=10,
load_best_model_at_end=True,
evaluation_strategy="epoch",
save_strategy="epoch",
save_total_limit=1,
metric_for_best_model="accuracy",
greater_is_better=True,
disable_tqdm=True,
gradient_accumulation_steps = 2,
gradient_checkpointing=True
)
metric = load_metric(join(CURRENT_DIR,"metrics","accuracy/accuracy.py"))
def compute_metrics(eval_pred):
logits, labels = eval_pred
print("logits",logits)
print("labels",labels)
predictions = np.argmax(logits, axis=-1)
print("predictions",predictions)
return metric.compute(predictions=predictions, references=labels)
trainer = Trainer(
model = model,
args = training_args,
train_dataset=sequences_dataset,
eval_dataset=sequences_dataset,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
model.config.problem_type
trainer.train()
trainer.state.log_history
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [X] My own task or dataset (give details below)
### Reproduction
Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.
Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at /home/wangmuqiang/fine_tune_esm2/esm1b_t33_650M_UR50S and are newly initialized: ['classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.out_proj.weight', 'classifier.dense.weight']
You should probably TRAIN this model on a down-stream task to be able to use it fo | https://github.com/huggingface/transformers/issues/28179 | closed | [] | 2023-12-21T09:50:27Z | 2024-01-30T08:03:39Z | null | Admire7494 |
huggingface/alignment-handbook | 81 | Why we use a lower batch size when comparing SFT lora with SFT full fine-tuning ? | https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_lora.yaml
| https://github.com/huggingface/alignment-handbook/issues/81 | closed | [] | 2023-12-20T21:09:33Z | 2024-01-07T21:03:14Z | 2 | shamanez |
huggingface/trl | 1,115 | How to prepare multi-turn dialogue dataset for dpo? | the single-turn dialogue dataset is like:
dpo_dataset_dict = {
"prompt": [
"hello",
"how are you",
"What is your name?",
"What is your name?",
"Which is the best programming language?",
"Which is the best programming language?",
"Which is the best programming language?",
],
"chosen": [
"hi nice to meet you",
"I am fine",
"My name is Mary",
"My name is Mary",
"Python",
"Python",
"Java",
],
"rejected": [
"leave me alone",
"I am not fine",
"Whats it to you?",
"I dont have a name",
"Javascript",
"C++",
"C++",
],
}
So, how to prepare a multi-turn dialogue dataset? Can you provide an example? Thank you! | https://github.com/huggingface/trl/issues/1115 | closed | [
"🏋 DPO"
] | 2023-12-20T09:14:45Z | 2024-10-03T14:12:48Z | null | chloefresh |
huggingface/transformers | 28,155 | What is the minimum video card with large memory required to run the mixtral-8x7b model | I mean the model that just came out：mistralai/Mixtral-8x7B-Instruct-v0.1，looks like a lot of parameter files，what is the minimum nvidia graphics card video memory required? | https://github.com/huggingface/transformers/issues/28155 | closed | [] | 2023-12-20T01:54:45Z | 2024-01-28T08:04:44Z | null | zysNLP |
huggingface/dataset-viewer | 2,218 | JobManagerCrashedError jobs are never retried | Currently, we have 7768 jobs with error_code `JobManagerCrashedError`. Some of them are caused by zombie killer set crashes.
```
Atlas atlas-x5jgb3-shard-0 [primary] datasets_server_cache> db.cachedResponsesBlue.aggregate([{$match:{error_code:"JobManagerCrashedError","details.copied_from_artifact":{$exists:false}}},{$group:{_id:{kind:"$kind"},count:{$sum:1}}},{$sort:{count:-1}}])
[
{ _id: { kind: 'split-duckdb-index' }, count: 3658 },
{ _id: { kind: 'split-descriptive-statistics' }, count: 1872 },
{ _id: { kind: 'config-parquet-and-info' }, count: 1765 },
{ _id: { kind: 'split-first-rows-from-streaming' }, count: 322 },
{ _id: { kind: 'split-first-rows-from-parquet' }, count: 72 },
{ _id: { kind: 'split-opt-in-out-urls-scan' }, count: 60 },
{ _id: { kind: 'dataset-config-names' }, count: 21 }
]
```
But most of them are set as crashed when deploying and are never retried, even if they are fast and straightforward to process.
Should we retry those jobs in backfill? I think we should differentiate the ones that are easy to process against those that are difficult (primarily because of OOMs), maybe retry once or twice, and set a different error so that we can identify which of them are caused by limited resources.
| https://github.com/huggingface/dataset-viewer/issues/2218 | closed | [
"question"
] | 2023-12-19T15:22:30Z | 2024-01-09T20:32:58Z | null | AndreaFrancis |
huggingface/optimum | 1,608 | XENOVA conversion issues | ### System Info
```shell
using the requirements.txt in Xenova for environment.
https://github.com/xenova/transformers.js/blob/main/scripts/requirements.txt
```
### Who can help?
@xenova
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction (minimal, reproducible, runnable)
"Error while initializing BPE: Token `_</w>` out of vocabulary"
### Expected behavior
Been trying to run blenderbot90, 400, 1b distilled.
Have had lots of issues, but I'll start with this one.
version 1 attempt, and loading from local after git-large file from HF repo.
tokenizer = AutoTokenizer.from_pretrained(model)
model = ORTModelForSeq2SeqLM.from_pretrained(model)
inputs = tokenizer("what is a black hole", return_tensors="pt")
gen_tokens = model.generate(**inputs)
response = tokenizer.batch_decode(gen_tokens)
version 2 attempt, directly repo using pipeline
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Xenova/blenderbot_small-90M")
model = ORTModelForSeq2SeqLM.from_pretrained("Xenova/blenderbot_small-90M")
onnx_pipe = pipeline("conversational", model=model, tokenizer=tokenizer)
text = "what is a black hole"
response = onnx_pipe (text)
Both cases getting this error: "Error while initializing BPE: Token `_</w>` out of vocabulary" | https://github.com/huggingface/optimum/issues/1608 | closed | [
"bug"
] | 2023-12-19T02:11:58Z | 2023-12-19T04:54:00Z | 3 | gidzr |
huggingface/safetensors | 409 | Doesn't work with versions of torch where "meta" dtype is not supported. | ### System Info
This is on my mac where I was just testing the interface. It seems like this could easily be fixed.
```
...
>>> from safetensors.torch import save_file
>>> x
{'a': tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])}
>>> x['a'].device
device(type='cpu')
>>> save_file(x, filename='foo')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.9/site-packages/safetensors/torch.py", line 281, in save_file
serialize_file(_flatten(tensors), filename, metadata=metadata)
File "/usr/local/lib/python3.9/site-packages/safetensors/torch.py", line 460, in _flatten
shared_pointers = _find_shared_tensors(tensors)
File "/usr/local/lib/python3.9/site-packages/safetensors/torch.py", line 72, in _find_shared_tensors
if v.device != torch.device("meta") and storage_ptr(v) != 0 and storage_size(v) != 0:
RuntimeError: Expected one of cpu, cuda, xpu, mkldnn, opengl, opencl, ideep, hip, msnpu, xla, vulkan device type at start of device string: meta
>>> safetensors.__version__
'0.4.1'
>>> torch.__version__
'1.8.1'
```
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Reproduction
Install torch 1.8.1 and safetensors 0.4.1 (this is current safetensor version in pip default channel)
run the code above (sorry I have not reduced this to a script but it's the most minimal example of using safetensors)
### Expected behavior
save_file should work with older versions of torch, like 1.8.1 | https://github.com/huggingface/safetensors/issues/409 | closed | [
"Stale"
] | 2023-12-18T15:51:28Z | 2024-01-23T01:49:25Z | null | danpovey |
huggingface/candle | 1,457 | How to do to quantize manually a phi-2 version, starting from safetensors file | Hi
I have fine tuned a phi-2 model using lora
I merged adapter with base model to get a trained one
I now have a bunch of safetensors file
How is it possible to convert these files into a gguf file ( llama.cpp concerter does not support phi)
In other words, how is it possible to achieve the same as : model-v2-q4k.gguf in lmz/candle-quantized-phi
| https://github.com/huggingface/candle/issues/1457 | closed | [] | 2023-12-18T15:14:37Z | 2023-12-18T15:58:12Z | null | ghost |
huggingface/optimum | 1,605 | Static Quantization - Token classification | Hi,
I am following the code [here](https://github.com/huggingface/optimum/tree/main/examples/onnxruntime/quantization/token-classification) for doing static quantization on my token classification model.
The inference time for quantized model(static) is almost the same as non quantized one. I have tried dynamic quantization too and it is showing some improvement in terms of latency but i need more latency improvements.
Do i have to do anything additional to lower/improve the inference time than what is mentioned [here](https://github.com/huggingface/optimum/tree/main/examples/onnxruntime/quantization/token-classification) for static quantization. Can anyone please help me? | https://github.com/huggingface/optimum/issues/1605 | open | [
"quantization"
] | 2023-12-18T13:31:33Z | 2024-10-09T09:21:22Z | 0 | akshay-babbar |
huggingface/diffusers | 6,211 | [Examples] How much time you support training scripts of text to video in diffusers? | I want to train svd in diffusers, can you support this feature in examples.
Thanks for your contributions. | https://github.com/huggingface/diffusers/issues/6211 | closed | [
"stale"
] | 2023-12-18T08:26:57Z | 2024-01-26T15:05:32Z | null | jiaxiangc |
huggingface/optimum | 1,604 | Table Transformer to ONNX | ### Feature request
Hi all,
I am trying to convert Table-transformer model from transformers(pretrained) to ONNX. Error reads something like " 'table-transformer' is not a supported format.
Is there any way to convert table-transformer (TATR) to ONNX model. Any help would be cherished.
Thanks.
### Motivation
Motivation for this is, I am working on developing a light weight table structure recognition model, ONNX model would help me in that regard.
### Your contribution
None | https://github.com/huggingface/optimum/issues/1604 | closed | [
"feature-request",
"onnx"
] | 2023-12-18T07:18:21Z | 2024-02-28T08:52:49Z | 3 | balajiChundi |
huggingface/safetensors | 407 | Does safetensors save the model's hierarchical structure? Is it similar to ONNX? | If safetensors saves the model's hierarchical structure, how can one access this structure? Is it possible to read it directly like with ONNX?Can I directly load a model from safetensors?
If the hierarchical structure of the model is not preserved, does it mean that the original model must be read from config.json? | https://github.com/huggingface/safetensors/issues/407 | closed | [
"Stale"
] | 2023-12-17T15:04:55Z | 2024-02-24T01:45:09Z | 3 | ZDragonX |
huggingface/datasets | 6,507 | where is glue_metric.py> @Frankie123421 what was the resolution to this? | > @Frankie123421 what was the resolution to this?
use glue_metric.py instead of glue.py in load_metric
_Originally posted by @Frankie123421 in https://github.com/huggingface/datasets/issues/2117#issuecomment-905093763_
| https://github.com/huggingface/datasets/issues/6507 | closed | [] | 2023-12-17T09:58:25Z | 2023-12-18T11:42:49Z | null | Mcccccc1024 |
huggingface/peft | 1,278 | How to add trainable parameters? (bugs in 'modules_to_save') | ### System Info
Hi,
How can I train other weights in the model rather than fix them during lora training?
### Who can help?
@BenjaminBossan Hi, I find you are active recently so I @ you here..
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder
- [ ] My own task or dataset (give details below)
### Reproduction
```
self.model, self.peft_optimizer, _, self.peft_lr_scheduler = deepspeed.initialize(
config=training_args.deepspeed,
model=model,
model_parameters=optimizers['model_parameters'] if self.training_args.do_train else None,
optimizer=hf_optimizer,
lr_scheduler=hf_lr_scheduler
)
```
I add the parameters I want to train in `hf_optimizer`, but those parameters still do not change
### Expected behavior
the gradient of those parameters added to `hf_optimizer` should not be None | https://github.com/huggingface/peft/issues/1278 | closed | [] | 2023-12-17T05:34:09Z | 2024-01-29T15:03:39Z | null | shawnricecake |
huggingface/accelerate | 2,262 | When I trained with two processes the gradient of the parameters could not be shared and I ended up with two different models. How to solve this problem? | When I trained with two processes the gradient of the parameters could not be shared and I ended up with two different models. Did anyone meet this problem before? How to solve it? | https://github.com/huggingface/accelerate/issues/2262 | closed | [] | 2023-12-15T13:48:34Z | 2024-06-11T12:26:07Z | null | zypsjtu |
huggingface/datasets | 6,501 | OverflowError: value too large to convert to int32_t | ### Describe the bug
### Steps to reproduce the bug
just loading datasets
### Expected behavior
how can I fix it
### Environment info
pip install /mnt/cluster/zhangfan/study_info/LLaMA-Factory/peft-0.6.0-py3-none-any.whl
pip install huggingface_hub-0.19.4-py3-none-any.whl tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl transformers-4.36.1-py3-none-any.whl pyarrow_hotfix-0.6-py3-none-any.whl datasets-2.15.0-py3-none-any.whl tyro-0.5.18-py3-none-any.whl trl-0.7.4-py3-none-any.whl
done | https://github.com/huggingface/datasets/issues/6501 | open | [] | 2023-12-15T10:10:21Z | 2025-06-27T04:27:14Z | 1 | zhangfan-algo |
huggingface/diffusers | 6,178 | How to train Stable Diffusion with DDPM? | I want to train Stable Diffusion with DDPM, but I can't find the code in this project. I found a lot of training code elsewhere on the internet, but most of it is distillation code on pre-trained models, not the original DDPM training code. I also tried to implement the original training code myself, but I couldn't get good results. Could you provide me with the code for this part if it's convenient for you?
| https://github.com/huggingface/diffusers/issues/6178 | closed | [] | 2023-12-15T02:43:07Z | 2023-12-15T02:54:06Z | null | MenSanYan |
huggingface/dataset-viewer | 2,208 | Add a collection with datasets infos | While working on enabling private datasets (#39) under conditions (isPro, isEnterprise), I thought we missed a place where we control the access to the dataset.
I think the first step in the DAG, instead of dataset-config-names, should be more about the dataset characteristics: if it's private or public, maybe if it's gated (not sure if it's useful info), if the user is pro or if the org is enterprise, if the viewer is disabled through the README (see https://github.com/huggingface/datasets-server/issues/2207), if the dataset is in the block list.
All that information could go to a new step called `dataset-status` or something similar.
The content could be:
```json
{
"dataset": "namespace/dataset",
"private": true,
"proUser": false,
"enterpriseOrg": true,
"disabledFromReadme": false,
"gated": false,
"blocked": false,
}
```
And a second step, called `dataset-enabled`, that would depend on `dataset-status`, and would return:
- 200 `{enabled: true}` if all the conditions are met
- 404 if we don't want to disclose the existence of the dataset, or if it does not exist
- 501 if it's not implemented
- 403? 404? if the dataset viewer is not enabled (private dataset, no pro user/enterprise org)
Then, the following steps would propagate the error if so, or if 200, will act as currently.
I think it's clearer to have two different steps: one to collect the data, another one to take a decision on this basis. We could also have everything in one cache entry, but I think the logic for maintenance would be harder (we would have to add info like: is that dataset private, is the user pro, etc. in the error details, or in the content, etc. to be able to check them regularly) | https://github.com/huggingface/dataset-viewer/issues/2208 | closed | [
"question",
"refactoring / architecture",
"P2"
] | 2023-12-14T13:59:42Z | 2024-01-11T14:30:03Z | null | severo |
huggingface/dataset-viewer | 2,207 | Backfill job processes datasets with disabled viewer? | If I read the code correctly, the backfill cronjob does not check if the dataset viewer is disabled (`viewer: false` in the README).
If we want to implement the dataset viewer for private datasets, under conditions (isPro, isEnterprise), we will have to check these conditions before adding jobs. | https://github.com/huggingface/dataset-viewer/issues/2207 | closed | [
"bug",
"question",
"P2"
] | 2023-12-14T13:01:53Z | 2024-02-06T16:03:10Z | null | severo |
huggingface/huggingface_hub | 1,907 | How to fix "VBox(children=(HTML(value='<center> <img..." error? When trying login() | ### Describe the bug
Hello. I am doing like below but it doesn't show enter token panel as supposed to be
What could be the reason?
Pip freeze is as below
```
alembic @ file:///home/conda/feedstock_root/build_artifacts/alembic_1701459233889/work
anyio @ file:///home/conda/feedstock_root/build_artifacts/anyio_1700835416766/work
archspec @ file:///home/conda/feedstock_root/build_artifacts/archspec_1699370045702/work
argon2-cffi @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi_1692818318753/work
argon2-cffi-bindings @ file:///home/conda/feedstock_root/build_artifacts/argon2-cffi-bindings_1695386553988/work
arrow @ file:///home/conda/feedstock_root/build_artifacts/arrow_1696128962909/work
asttokens @ file:///home/conda/feedstock_root/build_artifacts/asttokens_1698341106958/work
async-generator==1.10
async-lru @ file:///home/conda/feedstock_root/build_artifacts/async-lru_1690563019058/work
attrs @ file:///home/conda/feedstock_root/build_artifacts/attrs_1683424013410/work
Babel @ file:///home/conda/feedstock_root/build_artifacts/babel_1698174530262/work
beautifulsoup4 @ file:///home/conda/feedstock_root/build_artifacts/beautifulsoup4_1680888073205/work
bleach @ file:///home/conda/feedstock_root/build_artifacts/bleach_1696630167146/work
blinker @ file:///home/conda/feedstock_root/build_artifacts/blinker_1698890160476/work
boltons @ file:///home/conda/feedstock_root/build_artifacts/boltons_1677499911949/work
Brotli @ file:///home/conda/feedstock_root/build_artifacts/brotli-split_1695989787169/work
cached-property @ file:///home/conda/feedstock_root/build_artifacts/cached_property_1615209429212/work
certifi @ file:///home/conda/feedstock_root/build_artifacts/certifi_1700303426725/work/certifi
certipy==0.1.3
cffi @ file:///home/conda/feedstock_root/build_artifacts/cffi_1696001724357/work
charset-normalizer @ file:///home/conda/feedstock_root/build_artifacts/charset-normalizer_1698833585322/work
colorama @ file:///home/conda/feedstock_root/build_artifacts/colorama_1666700638685/work
comm @ file:///home/conda/feedstock_root/build_artifacts/comm_1691044910542/work
conda @ file:///home/conda/feedstock_root/build_artifacts/conda_1699392346065/work
conda-libmamba-solver @ file:///home/conda/feedstock_root/build_artifacts/conda-libmamba-solver_1700148543755/work/src
conda-package-handling @ file:///home/conda/feedstock_root/build_artifacts/conda-package-handling_1691048088238/work
conda_package_streaming @ file:///home/conda/feedstock_root/build_artifacts/conda-package-streaming_1691009212940/work
cryptography @ file:///home/conda/feedstock_root/build_artifacts/cryptography-split_1701563208210/work
debugpy @ file:///home/conda/feedstock_root/build_artifacts/debugpy_1695534290440/work
decorator @ file:///home/conda/feedstock_root/build_artifacts/decorator_1641555617451/work
defusedxml @ file:///home/conda/feedstock_root/build_artifacts/defusedxml_1615232257335/work
entrypoints @ file:///home/conda/feedstock_root/build_artifacts/entrypoints_1643888246732/work
exceptiongroup @ file:///home/conda/feedstock_root/build_artifacts/exceptiongroup_1700579780973/work
executing @ file:///home/conda/feedstock_root/build_artifacts/executing_1698579936712/work
fastjsonschema @ file:///home/conda/feedstock_root/build_artifacts/python-fastjsonschema_1700055509243/work/dist
filelock==3.13.1
fqdn @ file:///home/conda/feedstock_root/build_artifacts/fqdn_1638810296540/work/dist
fsspec==2023.12.2
greenlet @ file:///home/conda/feedstock_root/build_artifacts/greenlet_1698243379066/work
huggingface-hub==0.19.4
idna @ file:///home/conda/feedstock_root/build_artifacts/idna_1701026962277/work
importlib-metadata @ file:///home/conda/feedstock_root/build_artifacts/importlib-metadata_1701632192416/work
importlib-resources @ file:///home/conda/feedstock_root/build_artifacts/importlib_resources_1699364556997/work
ipykernel @ file:///home/conda/feedstock_root/build_artifacts/ipykernel_1698244021190/work
ipython @ file:///home/conda/feedstock_root/build_artifacts/ipython_1701703101339/work
ipython-genutils==0.2.0
ipywidgets==8.1.1
isoduration @ file:///home/conda/feedstock_root/build_artifacts/isoduration_1638811571363/work/dist
jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1696326070614/work
Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1654302431367/work
json5 @ file:///home/conda/feedstock_root/build_artifacts/json5_1688248289187/work
jsonpatch @ file:///home/conda/feedstock_root/build_artifacts/jsonpatch_1695536281965/work
jsonpointer @ file:///home/conda/feedstock_root/build_artifacts/jsonpointer_1695397236330/work
jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1700159890288/work
jsonschema-specifications @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-specifications_1701365715051/w | https://github.com/huggingface/huggingface_hub/issues/1907 | closed | [
"bug"
] | 2023-12-14T11:45:44Z | 2025-03-15T08:03:44Z | null | FurkanGozukara |
huggingface/unity-api | 17 | Android support | Great repo! My question is - does it work on Android?
I did some research but couldn't find much - except for some comments on [YouTube](https://www.youtube.com/watch?v=Ngmb7l7tO0I) that speech recognition doesn't really work on Android ("_when i export to an a Android Device the text always is "you", no matter what did i say. I don't know if needs another configuration because in the unity editor works fine_").
Could you please clarify?
Thank you! | https://github.com/huggingface/unity-api/issues/17 | open | [
"question"
] | 2023-12-14T11:15:56Z | 2024-01-18T10:56:45Z | null | dogadogan |
huggingface/alignment-handbook | 76 | can we inference with lora adapter after running the SFT ? | I trained the model using SFT on a custom dataset using lora config, which produced a Lora adapter, can we infer with it like having a base model and this adapter on top of it, or merge it ? | https://github.com/huggingface/alignment-handbook/issues/76 | closed | [] | 2023-12-14T10:55:20Z | 2023-12-28T07:14:29Z | 2 | Tejaswi-kashyap-006 |
huggingface/accelerate | 2,251 | when a tensor is generated from some_func(A.shape) (where A is a tensor), the generated tensor locates in cpu, not A's device | how to solve it ? I have tried tensor.to(A.device) and tensor.to(accelerator.device), but it seems not to work. | https://github.com/huggingface/accelerate/issues/2251 | closed | [] | 2023-12-14T09:18:15Z | 2023-12-14T14:38:17Z | null | weizhenhuan |
huggingface/peft | 1,265 | When generate outputs, how to get the probility of the outputs? Is there any param to let the model output probility ? | ### Feature request
xx
### Motivation
xx
### Your contribution
xx | https://github.com/huggingface/peft/issues/1265 | closed | [] | 2023-12-14T08:05:34Z | 2023-12-14T10:37:19Z | null | ShawnALiu |
huggingface/transformers | 28,025 | How to combine two pretrained model in huggingface transformers？ | ### Feature request
I want to combine two pretrained model(LLAMA and BERT) in a new python class. More specific，The way I've tried is to define a new class c that inherits llama and load bert in c's \_\_init\_\_ function.
So that I can use c.from_pretrained('llama_ckpt_dir') to load two model together.
`model=C.from_pretrained('llama_ckpt_dir',low_cpu_mem_usage=True)`
After I use c.save_pretrained(), even the checkpoint keeps total structure of llama and bert ,bert's params are all random initialize(weights Gaussian initialization bias all zero). （I checked this by torch.load the saved c checkpoint and print it out）
Sincerely requesting some help, what should be done?
### Motivation
Since trainer can be passed only one model at a time, so it seems a good feature that should be concerned for who wants to do things like train two model together?
But there is another difficulty that how to deal with two total diffrent tokenizer from bert and llama(even though this is not required for trainer(since tokenizer usually only used by data preprocess), but I hope I can fix this so that I can completely transform c into a total hf model)
### Your contribution
I'm not sure what I can help, but I can fully support anything that can contribute to this issue. | https://github.com/huggingface/transformers/issues/28025 | closed | [] | 2023-12-14T04:45:51Z | 2024-01-03T10:26:31Z | null | rangehow |
huggingface/chat-ui | 631 | Can we add full version number/build number on the landingpage? | Can we add full version number/build number or whatever, on the landingpage?
To distinguish between different installations.
If you go to https://huggingface.co/chat/, it looks like this:
If you go to https://huggingfaceh4-zephyr-chat.hf.space/, it looks like this:
So the version seems to be the same, but the buttons on the right side seems to indicate that there is differences in the version, i would guess? (if not huggingchat is a custom build?) | https://github.com/huggingface/chat-ui/issues/631 | open | [
"enhancement"
] | 2023-12-13T10:50:19Z | 2023-12-14T14:26:31Z | 4 | patchie |
huggingface/optimum | 1,592 | Can optimum.bettertransformer supports LLAVA model? | ### System Info
```shell
Local NVIDIA env:
(llava) xuyang@nobisuke:~$ nvcc -V
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2023 NVIDIA Corporation
Built on Fri_Jan__6_16:45:21_PST_2023
Cuda compilation tools, release 12.0, V12.0.140
Build cuda_12.0.r12.0/compiler.32267302_0
Python=3.10.4
Torch==2.0.1+cu117
```
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction (minimal, reproducible, runnable)
```
from optimum.bettertransformer import BetterTransformer
model = BetterTransformer.transform(model)
```
### Expected behavior
Recently, we sought to apply the optimum.bettertransformer in LLAVA for fine-tuning. The code run successfully and we found that the memory has decreased significantly.
However, in https://huggingface.co/docs/optimum/v1.15.0/bettertransformer/overview, we found that LLAVA is not in the support list.
Therefore, we want to confirm that can bettertransformer employ for pre-training or fine-tuning in LLAVA now? | https://github.com/huggingface/optimum/issues/1592 | closed | [
"bug"
] | 2023-12-13T09:08:35Z | 2023-12-13T12:37:13Z | 1 | xiaovhua |
huggingface/blog | 1,702 | How to introduce new alphabets in Whisper fine-tuning | Dear @sanchit-gandhi,
I was following your tutorial, [Fine-Tune Whisper For Multilingual ASR with 🤗 Transformers](https://huggingface.co/blog/fine-tune-whisper), to fine-tune Whisper with a dataset in the Amharic language. Amharic is used in Whisper training as speech-translation only, [Amharic audio -> corresponding English translation text]. Hence the Amharic alphabets are unseen in Whisper training.
The dataset I am trying to fine-tune with is [Amharic audio -> corresponding text in Amharic characters]. It consists of 92.28 hours (32901 instances) for training and 9.12 hours (3139 instances) for the testing set.
My data sources are:
1. https://github.com/getalp/ALFFA_PUBLIC/tree/master/ASR/AMHARIC and
2. https://www.findke.ovgu.de/findke/en/Research/Data+Sets/Amharic+Speech+Corpus.html
I tried the tiny, base, and small model sizes. In my first run with whisper-small, I observed a bad performance but when tried to play around with some parameters, including the model size, I was unable to run the code even.
I am not quite sure how to introduce the Amharic language characters other than giving the corresponding text as I have seen in the Hindi example.
I would appreciate your comment regarding the language whose characters were not seen in the Whisper training because it was treated as a speech translation only.
Thank you! | https://github.com/huggingface/blog/issues/1702 | open | [] | 2023-12-13T02:47:31Z | 2024-10-02T02:16:12Z | null | mequanent |
huggingface/chat-ui | 629 | Unable to use Azure AD for OpenID signin | Azure AD does not return the `picture` claim for the `profile` scope which results in a Zod validation error and authentication failing with `HTTP 500`:
```
chat-ui-chat-ui-1 | 21:07:21 28|index | ZodError: [
chat-ui-chat-ui-1 | 21:07:21 28|index | {
chat-ui-chat-ui-1 | 21:07:21 28|index | "code": "invalid_type",
chat-ui-chat-ui-1 | 21:07:21 28|index | "expected": "string",
chat-ui-chat-ui-1 | 21:07:21 28|index | "received": "undefined",
chat-ui-chat-ui-1 | 21:07:21 28|index | "path": [
chat-ui-chat-ui-1 | 21:07:21 28|index | "picture"
chat-ui-chat-ui-1 | 21:07:21 28|index | ],
chat-ui-chat-ui-1 | 21:07:21 28|index | "message": "Required"
chat-ui-chat-ui-1 | 21:07:21 28|index | }
chat-ui-chat-ui-1 | 21:07:21 28|index | ]
chat-ui-chat-ui-1 | 21:07:21 28|index | at get error [as error] (file:///app/node_modules/zod/lib/index.mjs:538:31)
chat-ui-chat-ui-1 | 21:07:21 28|index | at ZodEffects.parse (file:///app/node_modules/zod/lib/index.mjs:638:22)
chat-ui-chat-ui-1 | 21:07:21 28|index | at updateUser (file:///app/build/server/chunks/7-74fde01e.js:34:6)
chat-ui-chat-ui-1 | 21:07:21 28|index | at load (file:///app/build/server/chunks/7-74fde01e.js:126:9)
chat-ui-chat-ui-1 | 21:07:21 28|index | at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
chat-ui-chat-ui-1 | 21:07:21 28|index | at async load_server_data (file:///app/build/server/index.js:1932:18)
chat-ui-chat-ui-1 | 21:07:21 28|index | at async file:///app/build/server/index.js:3303:18 {
chat-ui-chat-ui-1 | 21:07:21 28|index | issues: [
chat-ui-chat-ui-1 | 21:07:21 28|index | {
chat-ui-chat-ui-1 | 21:07:21 28|index | code: 'invalid_type',
chat-ui-chat-ui-1 | 21:07:21 28|index | expected: 'string',
chat-ui-chat-ui-1 | 21:07:21 28|index | received: 'undefined',
chat-ui-chat-ui-1 | 21:07:21 28|index | path: [Array],
chat-ui-chat-ui-1 | 21:07:21 28|index | message: 'Required'
chat-ui-chat-ui-1 | 21:07:21 28|index | }
chat-ui-chat-ui-1 | 21:07:21 28|index | ],
chat-ui-chat-ui-1 | 21:07:21 28|index | addIssue: [Function (anonymous)],
chat-ui-chat-ui-1 | 21:07:21 28|index | addIssues: [Function (anonymous)],
chat-ui-chat-ui-1 | 21:07:21 28|index | errors: [
chat-ui-chat-ui-1 | 21:07:21 28|index | {
chat-ui-chat-ui-1 | 21:07:21 28|index | code: 'invalid_type',
chat-ui-chat-ui-1 | 21:07:21 28|index | expected: 'string',
chat-ui-chat-ui-1 | 21:07:21 28|index | received: 'undefined',
chat-ui-chat-ui-1 | 21:07:21 28|index | path: [Array],
chat-ui-chat-ui-1 | 21:07:21 28|index | message: 'Required'
chat-ui-chat-ui-1 | 21:07:21 28|index | }
chat-ui-chat-ui-1 | 21:07:21 28|index | ]
chat-ui-chat-ui-1 | 21:07:21 28|index | }
``` | https://github.com/huggingface/chat-ui/issues/629 | closed | [
"support"
] | 2023-12-12T21:22:19Z | 2024-02-19T09:39:51Z | 8 | zacps |
huggingface/chat-ui | 628 | isModelsModalOpen is not defined in ChatIntroduction.svelte probably after recent update ? | Hi getting this error after updating to the latest version :
Am Running :
{
'chat-ui': '0.6.0',
npm: '10.2.4',
node: '21.3.0',
acorn: '8.11.2',
ada: '2.7.4',
ares: '1.20.1',
base64: '0.5.1',
brotli: '1.0.9',
cjs_module_lexer: '1.2.2',
cldr: '44.0',
icu: '74.1',
llhttp: '9.1.3',
modules: '120',
napi: '9',
nghttp2: '1.58.0',
nghttp3: '0.7.0',
ngtcp2: '0.8.1',
openssl: '3.0.12+quic',
simdutf: '4.0.4',
tz: '2023c',
undici: '5.27.2',
unicode: '15.1',
uv: '1.46.0',
uvwasi: '0.0.19',
v8: '11.8.172.17-node.17',
zlib: '1.2.13.1-motley-5daffc7'
}
```
> chat-ui@0.6.0 dev
> vite dev
VITE v4.3.9 ready in 1206 ms
➜ Local: http://localhost:5173/
➜ Network: use --host to expose
(node:1526125) [DEP0040] DeprecationWarning: The `punycode` module is deprecated. Please use a userland alternative instead.
(Use `node --trace-deprecation ...` to show where the warning was created)
12:13:23 AM [vite-plugin-svelte] /home/user/public_html/chatui3/src/lib/components/chat/ChatIntroduction.svelte:53:7 'isModelsModalOpen' is not defined
12:13:23 AM [vite-plugin-svelte] /home/user/public_html/chatui3/src/lib/components/chat/ChatIntroduction.svelte:54:53 'isModelsModalOpen' is not defined
12:13:23 AM [vite-plugin-svelte] /home/user/public_html/chatui3/src/lib/components/chat/ChatIntroduction.svelte:64:22 'isModelsModalOpen' is not defined
ReferenceError: isModelsModalOpen is not defined
at /home/user/public_html/chatui3/src/lib/components/chat/ChatIntroduction.svelte:61:8
at Object.$$render (/home/user/public_html/chatui3/node_modules/svelte/src/runtime/internal/ssr.js:156:16)
at eval (/home/user/public_html/chatui3/src/lib/components/chat/ChatMessages.svelte:75:99)
at Object.$$render (/home/user/public_html/chatui3/node_modules/svelte/src/runtime/internal/ssr.js:156:16)
at eval (/home/user/public_html/chatui3/src/lib/components/chat/ChatWindow.svelte:116:102)
at Object.$$render (/home/user/public_html/chatui3/node_modules/svelte/src/runtime/internal/ssr.js:156:16)
at /home/user/public_html/chatui3/src/routes/+page.svelte:57:25
at Object.$$render (/home/user/public_html/chatui3/node_modules/svelte/src/runtime/internal/ssr.js:156:16)
at Object.default (/home/user/public_html/chatui3/.svelte-kit/generated/root.svelte:50:42)
at eval (/home/user/public_html/chatui3/src/routes/+layout.svelte:203:39)
``` | https://github.com/huggingface/chat-ui/issues/628 | closed | [
"support"
] | 2023-12-12T18:49:31Z | 2023-12-24T07:40:42Z | 7 | DrShivang |
huggingface/autotrain-advanced | 389 | How to disable default used --multi_gpu ? | File "/app/env/lib/python3.10/site-packages/accelerate/commands/launch.py", line 822, in _validate_launch_command
raise ValueError("You need to use at least 2 processes to use `--multi_gpu`.")
ValueError: You need to use at least 2 processes to use `--multi_gpu`.
How to disable this from the default provided params ?
Can autotrain be used with the free CPU version ?
thank you | https://github.com/huggingface/autotrain-advanced/issues/389 | closed | [] | 2023-12-12T13:32:03Z | 2023-12-15T09:21:52Z | null | FiveTechSoft |
huggingface/chat-ui | 627 | Rlhf data collection feature | Is it possible to add a way to generate multiple drafts for a given input. And then based on what the user picks save that data so that it can be used for rlhf? | https://github.com/huggingface/chat-ui/issues/627 | open | [
"enhancement",
"front",
"back"
] | 2023-12-12T13:29:06Z | 2023-12-14T08:53:14Z | 0 | nivibilla |
huggingface/transformers | 27,974 | how to replace the existing token in a tokenizer | ### Feature request
I have a tokenizer which have lots of preserved tokens like bellow:
```
'<reserved_7>': 100,
'<reserved_8>': 101,
'<reserved_9>': 102,
'<reserved_10>': 103,
'<reserved_11>': 104,
'<reserved_12>': 105,
'<reserved_13>': 106,
'<reserved_14>': 107,
```
I want to replace the '<reserved_7>' with '<|im_start|>' and replace '<reserved_8>' with '<|im_end|>'
what I want to get is a tokenizer which can act as below:
tokenizer.encode('<|im_start|>') => 100
### Motivation
I want to replace the '<reserved_7>' with '<|im_start|>' and replace '<reserved_8>' with '<|im_end|>'
### Your contribution
no | https://github.com/huggingface/transformers/issues/27974 | closed | [] | 2023-12-12T12:59:53Z | 2025-05-05T19:18:29Z | null | muziyongshixin |
huggingface/chat-ui | 623 | ChatUI with Docker - Permissions Issue | I'm trying to use the ChatUI space with Docker. I have a private, custom model which I've trained.
I want to access it in a private space using Docker ChatUI
I seem to be running into permissions errors.
Things I've tried:
Following the instructions set out here: https://huggingface.co/blog/Llama2-for-non-engineers (I used Llama2 with a custom dataset)
Creating it with / without the MongoDB URI
Adding an existing secret as the HF_TOKEN
Creating a new "HUGGING_FACE_HUB_TOKEN" in my settings and in the new space and using that
Addint he new token as a secret in the space where the model was generated
Hardcoding the access token in .env.local.template to see if it gives a temp fix (it didn't)
Does it matter if I don't have a centralised secret that is explicitly named as "HF_TOKEN"?
Error:
huggingface_hub.utils._errors.RepositoryNotFoundError: 401 Client Error. (Request ID: Root=1-6576f9fe-00986ef531649f933739e793;0d286b3c-5e65-45c1-a1f9-7efea56654dd)
Error: DownloadError
Repository Not Found for url: https://huggingface.co/api/models/<USERNAME>/<MODELNAME>.
Please make sure you specified the correct repo_id and repo_type.
If you are trying to access a private or gated repo, make sure you are authenticated.
Invalid username or password.
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
curl: (7) Failed to connect to 127.0.0.1 port 8080: Connection refused
Warning: Transient problem: connection refused Will retry in 10 seconds. 59
Warning: retries left. | https://github.com/huggingface/chat-ui/issues/623 | open | [
"support"
] | 2023-12-12T08:10:31Z | 2023-12-28T13:58:22Z | 1 | aidansys17 |
huggingface/text-generation-inference | 1,332 | How can I set log output to local file | ### Feature request
I want to set the TGI log to file instead of stdout.
### Motivation
I want to set the TGI log to file instead of stdout.
### Your contribution
how can I use params in command of env variables to set log output to file. | https://github.com/huggingface/text-generation-inference/issues/1332 | closed | [
"Stale"
] | 2023-12-12T07:54:26Z | 2024-01-18T01:46:56Z | null | soulseen |
huggingface/alignment-handbook | 74 | A question about the SFTTrainer (also a theoretical question about SFT in general) | I have a general question about Supervised Fine Tuning (SFT) for Dialogue applications.
Should the SFT process use the same LM objective (next-token prediction) that is used in pre-training a language model?
The "Dialogue" task is predicting "assistant" tokens, right? Shouldn't the objective be predicting only those tokens? Is one way to do this is to set labels for only assistant tokens and ignore the labels on others?
The SFTTrainer [implementation](https://github.com/huggingface/trl/blob/main/trl/trainer/sft_trainer.py#L381) does not set labels - as far as I understand, this leads to "labels" being cloned to "input_ids" and shifted right (within transformers code) leading to using "next-token" prediction objective.
More on a philosophical note - if using the same objective as pre-training for SFT, why shouldn't that be called "Fine Tuning" the model (On a dialogue dataset of course) rather than "Supervised Fine Tuning". What am I missing? Is there a reference paper that explains this well? The right approach to do SFT for Dialogue applications?
It is not obvious hence the question. For example, the [InstructGPT](https://arxiv.org/abs/2203.02155) paper mentions SFT but mainly redirects to the (seemingly) first attempt at SFT in [this](https://arxiv.org/pdf/2109.10862.pdf) paper which talks about a "Summarization" task but not a "Dialogue" task.
In that paper, when human labelers are asked to summarize and then when the paper mentions "Behavioral Cloning" is used to finetune the LLM to adapt to this task, I'd imagine that only "Summary" section is considered label but not the entire prompt/document. Following that principle, for "Dialogue" tasks, intuitively, I'd imagine that only "assistant" turns should be part of labels.
(By the way I already asked [this](https://github.com/huggingface/trl/issues/1083) in trl repository as well but not sure which is the best repository to ask the question (this repository is for alignment tasks in which SFT is a step - hence posted here too). | https://github.com/huggingface/alignment-handbook/issues/74 | open | [] | 2023-12-12T06:54:02Z | 2024-01-22T14:34:15Z | 3 | PradeepKadubandi |
huggingface/transformers.js | 453 | Summarization Parameters not working | ### Question
I've tried several of the supported summarization models with the code used in the browser extension example.
The only one I get any results from in a reasonable time is t5-small.
My problem with it is that despite any parameters I try to pass in the result is always same length.
I've traced through the code and it appears that the config params get passed in.
I've tried max_new_tokens, min_new_tokens, max_length, no joy.
I initially started specifying 2.5.3 and last tried just letting cdn handle it, looks like 2.10.x, no joy, same thing.
Could someone please provide me with an example of getting, in my case, the t5-small model running a summarization task that implements parameters as to output? | https://github.com/huggingface/transformers.js/issues/453 | open | [
"question"
] | 2023-12-12T06:21:52Z | 2023-12-19T21:52:32Z | null | kwlayman |
huggingface/safetensors | 400 | torch.nn.Module named_parameters() seem to be failing for safetensors | ### System Info
safetensors==0.4.1
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Reproduction
Noticed this issue with the new Mixtral model
https://github.com/vllm-project/vllm/issues/2020
Is there any way to fix this with safetensors?
### Expected behavior
Load the mixtral model in safe tensor format | https://github.com/huggingface/safetensors/issues/400 | closed | [
"Stale"
] | 2023-12-11T18:54:06Z | 2024-01-17T01:48:50Z | 1 | 0-hero |
huggingface/optimum | 1,583 | Add support for Chatglm2 & qwen onnx models | ### Feature request
Need to export ChatGLM2 & Qwen models to onnx using hf optimum.
ChatGLM2: model-card-> [https://huggingface.co/THUDM/chatglm2-6b](https://github.com/huggingface/optimum/issues/url)
Qwen: model-card-> [https://huggingface.co/Qwen/Qwen-7B-Chat](https://github.com/huggingface/optimum/issues/url)
### Motivation
I would like to make the process of exporting llm models to onnx simpler. There should be a generic boilerplate code which can export the models to onnx by simply passing hugging_face model_id.
### Your contribution
I have this piece of code for the export: I'm using this code to export chatglm2: [https://gist.github.com/manishghop/9be5aee6ed3d7551c751cc5d9f7eb8c3](https://github.com/huggingface/optimum/issues/url)
i use it for both chatglm2 & qwen by simply updating model_id.
Is there a way to run the inference of these onnx models?
| https://github.com/huggingface/optimum/issues/1583 | closed | [] | 2023-12-11T15:22:59Z | 2024-04-24T10:21:48Z | 4 | manishghop |
huggingface/peft | 1,247 | How to save parameters in prompt_encoder layers in p-tuning? | I want to resume training from checkpoint in p-tuning, but the model only save parameters in prompt_embeddings.
<img width="370" alt="image" src="https://github.com/huggingface/peft/assets/58416622/a085224f-32f2-409c-9a51-77c7438bc6a2">
| https://github.com/huggingface/peft/issues/1247 | closed | [] | 2023-12-11T02:44:59Z | 2024-01-19T15:03:32Z | null | lyt719 |
huggingface/optimum-benchmark | 102 | How to evaluate a model that already exists locally and hasn't been uploaded yet, "model=?" | 
i really want to know how to load qwen model, thank you very much | https://github.com/huggingface/optimum-benchmark/issues/102 | closed | [] | 2023-12-10T08:35:59Z | 2024-01-11T08:18:17Z | null | WCSY-YG |
huggingface/transformers | 27,928 | [Question] What is the main difference between "AutoModelForCasualLM" and "PeftModelForCausalLM"? | I also wrote it down in peft repo. However this issue is also related to transformers. So i write my question here again.
issue is here in peft(https://github.com/huggingface/peft/issues/1245)
Hello, Sorry for naive question.
I noticed that the``model.generate()`` function performed differently when inferrence right after train with ```trainer.model``` and after merge and unload. (Every params are the same.)
So I checked two different object with simple print function.
Difference was the object that contains model.
1. ```model = trainer.model```
```
PeftModelForCausalLM(
(base_model): LoraModel(
(model): LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): ModulesToSaveWrapper(
(original_module): Embedding(32008, 5120)
(modules_to_save): ModuleDict(
(default): Embedding(32008, 5120)
)
)
(layers): ModuleList(
(0-39): 40 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=5120, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=5120, bias=False)
)
(k_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=5120, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=5120, bias=False)
)
(v_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=5120, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=5120, bias=False)
)
(o_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=5120, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=5120, bias=False)
)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=13824, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=13824, bias=False)
)
(up_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=13824, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(i | https://github.com/huggingface/transformers/issues/27928 | closed | [] | 2023-12-10T03:10:36Z | 2024-02-01T00:49:07Z | null | daehuikim |
huggingface/peft | 1,245 | [Question] What is the main difference between "AutoModelForCasualLM" and "PeftModelForCausalLM"? | Because This is is related to "transformers". Therefore I wrote this question in transformers repo either.
issue is here in transformers(https://github.com/huggingface/transformers/issues/27928)
Hello, Sorry for naive question.
I noticed that the``model.generate()`` function performed differently when inferrence right after train with ```trainer.model``` and after merge and unload. (Every params are the same.)
So I checked two different object with simple print function.
Difference was the object that contains model.
1. ```model = trainer.model```
```
PeftModelForCausalLM(
(base_model): LoraModel(
(model): LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): ModulesToSaveWrapper(
(original_module): Embedding(32008, 5120)
(modules_to_save): ModuleDict(
(default): Embedding(32008, 5120)
)
)
(layers): ModuleList(
(0-39): 40 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=5120, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=5120, bias=False)
)
(k_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=5120, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=5120, bias=False)
)
(v_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=5120, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=5120, bias=False)
)
(o_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=5120, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=5120, bias=False)
)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=13824, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit(in_features=5120, out_features=13824, bias=False)
)
(up_proj): Linear4bit(
(lora_dropout): ModuleDict(
(default): Dropout(p=0.1, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=5120, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=13824, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
(base_layer): Linear4bit | https://github.com/huggingface/peft/issues/1245 | closed | [] | 2023-12-10T03:08:54Z | 2023-12-11T11:15:25Z | null | daehuikim |
huggingface/diffusers | 6,113 | How to use the models from sd_control_collection hf repo in diffusers | How to load/convert the models at https://huggingface.co/lllyasviel/sd_control_collection/tree/main with diffusers?
```
>>> pipe = diffusers.StableDiffusionPipeline.from_single_file("diffusers_xl_canny_full.safetensors")
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/loaders/single_file.py", line 261, in from_single_file
pipe = download_from_original_stable_diffusion_ckpt(
File "/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py", line 1436, in download_from_original_stable_diffusion_ckpt
converted_unet_checkpoint = convert_ldm_unet_checkpoint(
File "/home/ubuntu/.local/lib/python3.10/site-packages/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py", line 426, in convert_ldm_unet_checkpoint
new_checkpoint["time_embedding.linear_1.weight"] = unet_state_dict["time_embed.0.weight"]
KeyError: 'time_embed.0.weight'
```
Also not able to convert it via hf script: https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_controlnet_to_diffusers.py
We are able to run it through https://github.com/AUTOMATIC1111 webui. How can it be used with diffusers? | https://github.com/huggingface/diffusers/issues/6113 | closed | [] | 2023-12-09T14:11:26Z | 2024-06-11T18:22:03Z | null | anilsathyan7 |
huggingface/tokenizers | 1,410 | How to create Tokenizer.json? | I have this tokenizer and I want to convert it to **tokenizer.json** format.
- added_tokens.json
- normalizer.json
- special_tokens_map.json
- config.json
- preprocessor_config.json
- vocab.json
- merges.txt
- pytorch_model.bin
Is it possible to replace my tokenizer data with the original **tokenizer.json**?
```
import json
j = open('hf/tokenizer.json')
data = json.load(j)
with open('medium-tokenizer/merges.txt') as f:
merges = f.readlines()
merges.pop(0)
j = open('medium-tokenizer/vocab.json')
vocab = json.load(j)
j = open('medium-tokenizer/added_tokens.json')
added_tokens = json.load(j)
j = open('medium-tokenizer/normalizer.json')
normalizer = json.load(j)
data['added_tokens'] = added_tokens
data['normalizer'] = normalizer
data['model']['vocab'] = vocab
data['model']['merges'] = merges
with open("tokenizer.json", "w") as outfile:
json.dump(data, outfile)
``` | https://github.com/huggingface/tokenizers/issues/1410 | closed | [
"Stale"
] | 2023-12-08T09:41:18Z | 2024-01-14T01:52:39Z | null | kenaii |
huggingface/optimum | 1,577 | Support the ORT of the Stable Diffusion XL inpaint model | ### Feature request
Hi all.
We would like to convert the stable-diffusion-xl-inpaint model below to ONNX and run it using ORT. The conversion to ONNX went well using Optimum's cli, but there doesn't seem to be a Python class for ORT inference.
https://huggingface.co/diffusers/stable-diffusion-xl-1.0-inpainting-0.1
Is there a way to perform inference on this model with the optimum package? If not, do you have any plans to provide support?
Thank you
### Motivation
To run sd-xl inpaint model with ORT
### Your contribution
I can submit a PR for you if I have something to help | https://github.com/huggingface/optimum/issues/1577 | closed | [
"feature-request",
"Stale"
] | 2023-12-08T09:21:06Z | 2025-02-19T02:02:54Z | 2 | 0-chan-kor |
huggingface/chat-ui | 617 | Does Chat-UI support multithreading? | Maybe it depends on node.js, but I want to know the CPU utilization. | https://github.com/huggingface/chat-ui/issues/617 | closed | [
"question"
] | 2023-12-08T05:36:18Z | 2023-12-14T07:30:01Z | null | calycekr |
huggingface/chat-ui | 615 | npm run error (latest git pull) | I created a .env.local as:
```
MONGODB_URL=mongodb://localhost:27017
MONGODB_DB_NAME=chat-ui
MONGODB_DIRECT_CONNECTION=false
COOKIE_NAME=hf-chat
HF_TOKEN=
HF_API_ROOT=https://api-inference.huggingface.co/models
OPENAI_API_KEY=
```
Then I tried:
```
npm install #everything went fine
npm run dev -- --host 0.0.0.0
```
but I got the error below:
```
(node:770942) [DEP0040] DeprecationWarning: The `punycode` module is deprecated. Please use a userland alternative instead.
(Use `node --trace-deprecation ...` to show where the warning was created)
11:47:42 AM [vite] Error when evaluating SSR module /src/lib/server/auth.ts:
|- SyntaxError: "undefined" is not valid JSON
at JSON.parse (<anonymous>)
at /home/shuther/devProjects/chat-ui/src/lib/server/auth.ts:43:14
at async instantiateModule (file:///home/shuther/devProjects/chat-ui/node_modules/vite/dist/node/chunks/dep-e8f070e8.js:54405:9)
11:47:42 AM [vite] Error when evaluating SSR module /src/hooks.server.ts: failed to import "/src/lib/server/auth.ts"
|- SyntaxError: "undefined" is not valid JSON
at JSON.parse (<anonymous>)
at /home/shuther/devProjects/chat-ui/src/lib/server/auth.ts:43:14
at async instantiateModule (file:///home/shuther/devProjects/chat-ui/node_modules/vite/dist/node/chunks/dep-e8f070e8.js:54405:9)
SyntaxError: "undefined" is not valid JSON
at JSON.parse (<anonymous>)
at /home/shuther/devProjects/chat-ui/src/lib/server/auth.ts:43:14
at async instantiateModule (file:///home/shuther/devProjects/chat-ui/node_modules/vite/dist/node/chunks/dep-e8f070e8.js:54405:9)
SyntaxError: "undefined" is not valid JSON
at JSON.parse (<anonymous>)
at /home/shuther/devProjects/chat-ui/src/lib/server/auth.ts:43:14
at async instantiateModule (file:///home/shuther/devProjects/chat-ui/node_modules/vite/dist/node/chunks/dep-e8f070e8.js:54405:9)
```
On the browser side, I have error 500 (nice picture) | https://github.com/huggingface/chat-ui/issues/615 | closed | [
"support"
] | 2023-12-07T10:59:53Z | 2024-04-24T12:29:46Z | 4 | shuther |
huggingface/chat-ui | 614 | Docker build - multiple errors - documentation | I can't find documentation to build it myself; so I tried:
`docker-compose build up`
But I got multiple errors amoung:
> chat-ui/.env: line 23: unexpected character "\"" in variable name "\"PROVIDER_URL\": \"\","
Even `source .env` returned multiple errors; I tried to change the `into a ' with no luck.
My goal was to build it and include it into a docker compose. | https://github.com/huggingface/chat-ui/issues/614 | open | [
"support"
] | 2023-12-07T10:55:04Z | 2024-06-01T12:44:18Z | 4 | shuther |
huggingface/text-generation-inference | 1,318 | how to run tgi installed locally without any UI | ### System Info
how to run tgi installed locally without any UI?
pip install text-generation , giving error: ERROR: No matching distribution found for text-generation
### Information
- [ ] Docker
- [X] The CLI directly
### Tasks
- [X] An officially supported command
- [ ] My own modifications
### Reproduction
pip install text-generation
### Expected behavior
need some help running tgi+my model on cmdline | https://github.com/huggingface/text-generation-inference/issues/1318 | closed | [
"Stale"
] | 2023-12-07T08:47:13Z | 2024-01-13T01:46:40Z | null | poojitharamachandra |
huggingface/autotrain-advanced | 376 | How to a Autotrain Seq2Seq ? | Hi everyone , I'm trying to finetune a Helsinki-NLP/opus-mt-tc-big-ar-en on local arabic of morocco which is called Daraija Arabic , the problem is that I'm unable to use Autotrain I keep getting 500 error code
[output.csv](https://github.com/huggingface/autotrain-advanced/files/13593069/output.csv)
FYI : I didnt modify Training Parameters (find params to copy-paste [here] area so I dont know if its necessary | https://github.com/huggingface/autotrain-advanced/issues/376 | closed | [] | 2023-12-07T00:22:46Z | 2023-12-08T17:27:57Z | null | Lachkar-Ahmed-Salim |
huggingface/autotrain-advanced | 375 | How to do a Seq2Seq Autotrain ? | https://github.com/huggingface/autotrain-advanced/issues/375 | closed | [] | 2023-12-07T00:10:33Z | 2023-12-11T09:41:24Z | null | Lachkar-Ahmed-Salim | |
huggingface/alignment-handbook | 68 | DPO alignment doesn't work on Lora models as suggested | You claim that "[In practice, we find comparable performance for both full and LoRA fine-tuning, with the latter having the advantage of producing small adapter weights that are fast to upload and download from the Hugging Face Hub.](https://github.com/huggingface/alignment-handbook/tree/main/scripts#:~:text=In%20practice%2C%20we%20find%20comparable%20performance%20for%20both%20full%20and%20LoRA%20fine%2Dtuning%2C%20with%20the%20latter%20having%20the%20advantage%20of%20producing%20small%20adapter%20weights%20that%20are%20fast%20to%20upload%20and%20download%20from%20the%20Hugging%20Face%20Hub.)"
However, when I try the Lora model DPO-aligned LLM that you have trained, [alignment-handbook/zephyr-7b-dpo-lora](https://huggingface.co/alignment-handbook/zephyr-7b-dpo-lora), I experience a total performance degradation.
Here is an example of model output that seems confused:
Even the training loss indicates that the model has not learned much
<img width="773" alt="image" src="https://github.com/huggingface/alignment-handbook/assets/3280518/550451f4-4afb-470c-ace7-71b332bb5087">
Here is the training loss for the full model DPO alignment.
Would you please do a clarification? Is my observation different from what you have experienced?
Thanks
| https://github.com/huggingface/alignment-handbook/issues/68 | open | [] | 2023-12-06T19:12:30Z | 2023-12-07T09:43:32Z | 1 | Abe13 |
huggingface/alignment-handbook | 66 | How to specify another GPU to run rather than cuda:0? | I tried to modify the --gpu_ids paramater in recipes/accelerate_configs/multi_gpu.yaml, however, it didn't work, the device was still 'cuda:0'. | https://github.com/huggingface/alignment-handbook/issues/66 | closed | [] | 2023-12-06T10:48:25Z | 2023-12-06T11:13:02Z | null | njupopsicle |
huggingface/datasets | 6,478 | How to load data from lakefs | My dataset is stored on the company's lakefs server. How can I write code to load the dataset? It would be great if I could provide code examples or provide some references
| https://github.com/huggingface/datasets/issues/6478 | closed | [] | 2023-12-06T09:04:11Z | 2024-07-03T19:13:57Z | null | d710055071 |
huggingface/tokenizers | 1,407 | How to add byte_fallback tokens? | # Alternative title
How to make a tokenizer behaving similarly to Llama
## Background
Llama tokenizer considers byte_fallback tokens **not special**. When it decodes, it doesn't remove these tokens other than special tokens (unk, pad, bos, eos).
## What I am trying to do
I'm trying to create a tokenizer behaving like Llama. However, I **am only able** to add byte_fallback tokens as **special tokens**.
```python
from tokenizers import Tokenizer
from tokenizers import decoders, pre_tokenizers
from tokenizers.models import BPE
from tokenizers.processors import TemplateProcessing
from tokenizers.trainers import BpeTrainer
from tokenizers import AddedToken
from datasets import load_dataset
dataset = load_dataset("tapaco")
def topaco_generator():
for i in dataset['train']:
yield i['paraphrase']
bpe_trainer = BpeTrainer(
special_tokens=["<unk>", "<s>", "</s>", "<pad>"]
+ [f"<0x{i:02X}>" for i in range(256)] # byte_fallback tokens
)
tokenizer = Tokenizer(BPE(byte_fallback=True))
tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
[pre_tokenizers.Metaspace(), pre_tokenizers.Digits(individual_digits=True)]
)
tokenizer.enable_padding(pad_id=3, pad_token="<pad>")
tokenizer.post_processor = TemplateProcessing(
single="<s> $A </s>",
pair="<s> $A </s> $B </s>",
special_tokens=[
("<s>", 1),
("</s>", 2),
],
)
tokenizer.decoder = decoders.Sequence(
[
decoders.Metaspace(),
decoders.ByteFallback(),
]
)
# my attempt to add byte_fallback as non-special tokens
# tokenizer.add_tokens([AddedToken(content=f"<0x{i:02X}>", special=True, normalized=False) for i in range(256)])
tokenizer.train_from_iterator(topaco_generator(), trainer=bpe_trainer)
tokenizer.save("topaco_tokenizer.json")
tokenizer = Tokenizer.from_file("topaco_tokenizer.json")
text = "I love you more than I can say 🤗"
encoded_text = tokenizer.encode(text)
print(encoded_text.tokens)
# My work around to preverse byte_fallback tokens
# and remove other special tokens
decoded_text = tokenizer.decode(encoded_text.ids, skip_special_tokens=False)
print(decoded_text.removeprefix('<s> ').removesuffix('</s>'))
```
## Problem
No matter how I tried this line `tokenizer.add_tokens([AddedToken(content=f"<0x{i:02X}>", special=True, normalized=False) for i in range(256)])` with different position in my code (before training, after training) and with different parameters of AddedToken, I still can not achieve Llama's behavior. | https://github.com/huggingface/tokenizers/issues/1407 | open | [
"bytefallback",
"Feature Request"
] | 2023-12-06T09:03:35Z | 2024-08-27T01:57:04Z | null | dinhanhx |
huggingface/transformers.js | 432 | Cannot download the model from huggingface | Because of the network reason, when using transfomer.js we cannot download the model successful
How to set the network proxy for the model download
| https://github.com/huggingface/transformers.js/issues/432 | open | [
"question"
] | 2023-12-06T08:18:58Z | 2023-12-10T13:42:50Z | null | wujohns |
huggingface/blog | 1,677 | how to achieve image-text matching of BLIP2 | Hi, Thanks to the authors for the works.
I am trying to achieve image-text matching of BLIP2, but I didn't find any examples of that. Can you give me some help or tips? | https://github.com/huggingface/blog/issues/1677 | open | [] | 2023-12-06T07:03:21Z | 2023-12-06T07:08:48Z | null | wkqun555 |
huggingface/diffusers | 6,070 | How to overload existing class in diffusers | That's just for personal development. I want to write a new class inherited from existing class (e.g. `ControlNetModel`) and I added some new parameters to `__init__` function, but found that the `__init__` function is still the parent's implementation, whether to add the decorator `register_to_config` or not.
Hope some advice.
| https://github.com/huggingface/diffusers/issues/6070 | closed | [] | 2023-12-06T06:41:44Z | 2024-09-25T14:44:04Z | null | OrangeSodahub |
huggingface/diffusers | 6,067 | How to run the fine_tuned model? | Hi all,
I used the instructions given [here](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) to fine_tune the model on dog pictures (as explained in the link).
The fine_tuning has finished, and a folder called path-to-save-model has been created (that has the weights of the model). Now how do I use this output? Do I run test_dreambooth.py? (I tried running it but it gives error at "from test_examples_utils import ExamplesTestsAccelerate, run_command # noqa: E402"
I appreciate it if someone can please let me know how to use the output of the trained model.
Thank you | https://github.com/huggingface/diffusers/issues/6067 | closed | [] | 2023-12-06T01:01:56Z | 2025-04-28T10:32:33Z | null | alireza18878 |
huggingface/text-generation-inference | 1,314 | What is the default tokenizer behaviour? | ### System Info
N/A
### Information
- [ ] Docker
- [X] The CLI directly
### Tasks
- [X] An officially supported command
- [ ] My own modifications
### Reproduction
I'm trying to understand whether special tokens (i.e. BOS and EOS) are added and suppressed on tokenization and decoding.
Encoding:
- I searched for add_special_tokens in the repo and I don't see anywhere this is being set to true when tokenizing. So, it seems that there are no EOS tokens automatically added.
Decoding:
- I searched for skip_special_tokens and it seems that [here](https://github.com/huggingface/text-generation-inference/blob/3238c49121b02432bf2938c6ebfd44f06c5adc2f/server/text_generation_server/models/causal_lm.py#L525) on line 541 that indeed BOS and EOS are being supressed.
Is this understanding correct?
### Expected behavior
If possible, could the default tokenization strategy be described on the ReadMe so users know what to expect? | https://github.com/huggingface/text-generation-inference/issues/1314 | closed | [] | 2023-12-05T17:35:05Z | 2024-01-19T13:14:13Z | null | RonanKMcGovern |
huggingface/chat-ui | 609 | [Feature Request] Uploading PDFS/Text Files/Images? | I love the search function and it makes the chat feel so much more accurate! I use it mainly as a direct ChatGPT replacment, using code models when needed or normal models for chat.
Can we have the option to upload images/pdfs/other files to the chat? the images could be integrated by clip/blip, and the PDF or text files could just be added to the context or summarized and then added?
It would be awesome to have! Thank you for all the work made into this project | https://github.com/huggingface/chat-ui/issues/609 | open | [] | 2023-12-05T12:20:39Z | 2024-10-04T01:13:18Z | 3 | iChristGit |
huggingface/trl | 1,059 | How can I have the evaluation pass in only the response to a prompted/instructed generation into the metric. | I have created the following metric:
```py
class MyCustomMetric(Metric):
def _info(self):
# Returns the MetricInfo that defines the name, description, etc.
return datasets.MetricInfo(
# This should be a short description of your metric.
description="_DESCRIPTION",
# You can cite papers, GitHub repositories, etc.
citation="_CITATION",
# The inputs and outputs your metric expects.
# These are used to validate the inputs and outputs of _compute
inputs_description="_KWARGS_DESCRIPTION",
features=datasets.Features({
'predictions': datasets.Value('string'),
'references': datasets.Value('string')
})
)
def _compute(self, predictions, references):
# Here is where you should put your main metric computation logic
# Adapt your existing code to fit in here
fc_results = []
for idx, example in enumerate(predictions):
print(f"Example {idx}: ", end="")
post_message = ""
# Custom Function Calling metric
prompts = None
try:
generated_arguments, expected_arguments, prompts = json_arguments_from_prompt(
references[idx],
predictions[idx],
INSTRUCTION
# {"idx": idx, "epoch": epoch}
)
fc_result = fc_metric.run(generated_arguments, expected_arguments)
fc_results.append(fc_result)
# if save_prompts_path:
# # add prompts to dpo_data.json
# dpo_data.append({
# "fc_result": fc_result,
# **prompts
# })
# with open(save_prompts_path, "w") as f:
# json.dump(dpo_data, f)
except Exception as e:
print(f"Error function calling: {e}\n")
fc_results.append(0)
return fc_results
```
This metric expects the prediction to be generated after passing the instruction. For example I have my prompts in the following format:
`<s> [INST] {message} [/INST] {response}`
I want the evaluation to receive the `predictions` for response and then compare those with my `references`. To reiterate, the predictions should be generated from the model being passed `<s> [INST] {message} [/INST]`.
Currently it seems as if the logits are just generated without any prompt resulting in responses like:
```
predicted_strings: ['Unterscheidung Unterscheidung![: What<<NOP What favorite is to help the patterns climate a following is is a to a topic you\nineited by the >>_> in returnFUNCTIONS>\n the is related, return program should be " the format formatname format format. functionFUNCTION_CALL>FORM>( <</OFIGNCIATED_WITH_USER_USERUNCTION</FUNCTION_CALL_NAME>brUNCTIONSCALL_NAMEGSUMENTS>\nGUMENTS_ASS_THE_FIED_FORM_FORMAT</FUNCTION_CALL_ARGUMENTS> If, respond " " response.\nFUNCTIONS>username": "get",meanalth",",function_ "description": "Get health "input": [root": "string", "properties": {" "}] {"name": "leaf_Results", "description": "Search search list of searchists", on a search query", "parameters": {"type": "array", "properties": {"query": {"type": {"query": {"type": "string" "required": "Search"}} "type": "array" "title": ["query"] "description": "Searchphy Search"}}}, {"name": "getUserending",", "description": "Get a list of trifs that on the tr trending", "parameters": {"type": "object", "properties": {"}}},}</FUNCTIONS>\nUSERFS>\n me the ofif from a cat cat doing</users FUNCTION_CALL_NAME>rootSearchResults</FUNCTION_CALL_NAME>FUNCTION_CALL_ARGUMENTS>{"json": {"query": "cool cat"}}</FUNCTION_CALL_ARGUMENTS></s>��']
```
after looking through the source code it seems like modifying the `prediction_step` method inside `Trainer` is the way to go. | https://github.com/huggingface/trl/issues/1059 | closed | [] | 2023-12-04T19:01:34Z | 2024-01-12T15:05:10Z | null | CakeCrusher |
huggingface/distil-whisper | 49 | How to make training data? | I have a folder like this:
audio_1
transcript_1.txt
audio_2
transcript_2.txt
how can I make this folder into huggingface dataset? | https://github.com/huggingface/distil-whisper/issues/49 | open | [] | 2023-12-04T18:44:40Z | 2023-12-12T16:51:48Z | null | satani99 |
huggingface/computer-vision-course | 77 | Issue with rendering the course | If we try to render the course to preview how our added content looks like, it throws the following error
```bash
sarthak@kde:~/Desktop/computer-vision-course$ doc-builder preview computer-vision-course chapters/ --not_python_module
Initial build docs for computer-vision-course chapters/ /tmp/tmp0uqdjoxf/computer-vision-course/main/en
Building the MDX files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 29/29 [00:00<00:00, 1288.27it/s]
Traceback (most recent call last):
File "/home/sarthak/anaconda3/bin/doc-builder", line 8, in <module>
sys.exit(main())
File "/home/sarthak/anaconda3/lib/python3.9/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main
args.func(args)
File "/home/sarthak/anaconda3/lib/python3.9/site-packages/doc_builder/commands/preview.py", line 171, in preview_command
source_files_mapping = build_doc(
File "/home/sarthak/anaconda3/lib/python3.9/site-packages/doc_builder/build_doc.py", line 405, in build_doc
sphinx_refs = check_toc_integrity(doc_folder, output_dir)
File "/home/sarthak/anaconda3/lib/python3.9/site-packages/doc_builder/build_doc.py", line 460, in check_toc_integrity
raise RuntimeError(
RuntimeError: The following files are not present in the table of contents:
- en/Unit 5 - Generative Models/variational_autoencoders
- en/Unit 5 - Generative Models/README
- en/Unit 11 - Zero Shot Computer Vision/README
- en/Unit 2 - Convolutional Neural Networks/README
- en/Unit 1 - Fundamentals/README
- en/Unit 8 - 3D Vision, Scene Rendering and Reconstruction/README
- en/Unit 4 - Mulitmodal Models/README
- en/Unit 9 - Model Optimization/README
- en/Unit 6 - Basic CV Tasks/README
- en/Unit 7 - Video and Video Processing/README
- en/Unit 13 - Outlook/README
- en/Unit 3 - Vision Transformers/README
- en/Unit 12 - Ethics and Biases/README
- en/Unit 10 - Synthetic Data Creation/README
Add them to chapters/_toctree.yml.
```
**Explanation:** This is because there have been README files added to each chapter. However, these README files are not present in the `_toctree.yml`.
**Why it's important:** Being able to render the course locally is important as it can give us a rough overview of how the content looks like.
**Possible solutions could be:**
* Remove the README files for the time being
* Add them to the toctree and also making sure that if anyone adds any chapter contents they also update the toctree making it easier for others to render the course
Open for discussion from other members :v:
| https://github.com/huggingface/computer-vision-course/issues/77 | open | [
"question"
] | 2023-12-04T01:02:22Z | 2023-12-08T18:17:19Z | null | sarthak247 |
huggingface/sentence-transformers | 2,363 | How to retrieve the epoch of the saved model from model.save ? | Hi,
Thank you for the repo.
Can anyone help me with retrieving the epoch of the saved model, in both cases where save_best_model=True and save_best_model=False?
Thank you
```
model.fit(train_objectives=[(train_dataloader, train_loss)],
evaluator=evaluator,
epochs=num_epochs,
evaluation_steps=1000,
warmup_steps=warmup_steps,
save_best_model=True,
output_path=output_path)
model.save(path)``` | https://github.com/huggingface/sentence-transformers/issues/2363 | closed | [] | 2023-12-02T15:25:52Z | 2024-01-09T22:16:20Z | null | gowrijsuria |
huggingface/transformers.js | 426 | [Question] feature-extraction discrepancies across different platforms | I'm observing discrepancies in feature-extraction results across different platforms. Here's the code:
```js
import { pipeline, env } from '@xenova/transformers'
const extractor = await pipeline('feature-extraction', 'Xenova/gte-small', {
quantized: false,
cache_dir: './.cache',
local_files_only: false,
})
const text = 'hello'
const embedding = await extractor(text, { pooling: 'mean', normalize: true })
const response = Array.from(embedding.data)
console.log(JSON.stringify(response, null, 2))
// Node v20
// "@xenova/transformers": "^2.9.0"
```
The results differ between macOS 13 (Apple Silicon/Arm) and Ubuntu 23.1 (Raspberry Pi/Arm). I've tried various configurations (e.g., pooling, normalize, with and without Array.from) and still observe different results. It's worth noting that sequential calls on the same platform produce consistent results.
I have a few questions:
1. Is this discrepancy expected due to the nature of float32 precision and rounding, even though the calculations are performed on ARM architecture?
2. Given that the difference is extremely small, could it still impact accuracy in any significant way?
[mean-nonorm-mac-01.json](https://github.com/xenova/transformers.js/files/13530082/mean-nonorm-mac-01.json)
[mean-nonorm-pi-01.json](https://github.com/xenova/transformers.js/files/13530083/mean-nonorm-pi-01.json)
[mean-norm-mac-01.json](https://github.com/xenova/transformers.js/files/13530084/mean-norm-mac-01.json)
[mean-norm-pi-01.json](https://github.com/xenova/transformers.js/files/13530086/mean-norm-pi-01.json)
| https://github.com/huggingface/transformers.js/issues/426 | closed | [
"question"
] | 2023-12-01T17:12:04Z | 2023-12-05T18:51:03Z | null | devfacet |
huggingface/chat-ui | 604 | "Invalid State: Controller is already closed" error when trying to use chat-ui locally with llama.cpp | HELP NEEDED
**What is the issue?**
Not able to use chat-ui locally to get the response back when using the llama.cpp as a server.
I can load the chat-ui after installing it via npm install and npm run dev. The env.local file is also configured and UI allows to send the request. However, the response never comes back in UI, and 'Sorry, something went wrong. Please try again' is shown.
On checking the logs in chat-ui, the error shown is:
TypeError [ERR_INVALID_STATE]: Invalid state: Controller is already closed
at new NodeError (node:internal/errors:399:5)
at ReadableStreamDefaultController.enqueue (node:internal/webstreams/readablestream:1036:13)
at update (/home/devuser/development/chat-ui-main/src/routes/conversation/[id]/+server.ts:158:20)
at eval (/home/devuser/development/chat-ui-main/src/routes/conversation/[id]/+server.ts:168:13)
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async Object.start (/home/devuser/development/chat-ui-main/src/routes/conversation/[id]/+server.ts:260:7) {
code: 'ERR_INVALID_STATE'
I also tested the llama.cpp server response via curl and the response came back correctly, so it's not an issue with llama.cpp.
Versions:
chat-ui code is latest from master.
llama.cpp code is latest from master and build locally.
Tried with Node 20 and then with Node 19, but issue still remains.
env.local:
MONGODB_URL=mongodb://localhost:27017
MONGODB_DB_NAME=chat-ui
MONGODB_DIRECT_CONNECTION=false
USE_LOCAL_WEBSEARCH=true
HF_ACCESS_TOKEN=test
MODELS=`[
{
"name": "Zephyr",
"chatPromptTemplate": "<|system|>\n{{preprompt}}</s>\n{{#each messages}}{{#ifUser}}<|user|>\n{{content}}</s>\n<|assistant|>\n{{/ifUser}}{{#ifAssistant}}{{content}}</s>\n{{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.7,
"top_p": 0.95,
"repetition_penalty": 1.1,
"top_k": 50,
"truncate": 1000,
"max_new_tokens": 2048,
"stop": ["</s>"]
},
"endpoints": [
{
"url": "http://localhost:8080",
"type": "llamacpp"
}
]
}
]`
Am I missing anything in terms of installation steps? Any help here will be appreciated.
| https://github.com/huggingface/chat-ui/issues/604 | closed | [] | 2023-11-30T16:42:06Z | 2023-11-30T17:41:19Z | 1 | ManasInd |
huggingface/optimum | 1,556 | RuntimeError: Cannot infer the task from a local directory yet, please specify the task manually. | ### System Info
windows 10 - ryzen 3600x - 16 gb ddr4-3000 - python 3.10 - latest optimum inside a venv
### Who can help?
_No response_
### Information
When I try to convert a model to openvino using
optimum-cli export openvino -m "d:\sdxl\LCMphoton" "d:\sdxl\LCMphotonov"
I have this error :
RuntimeError: Cannot infer the task from a local directory yet, please specify the task manually.
I am converting standard sd1.5 models to lcm with lora locally and want to convert that to openvino. I have local models which are not present on huggingface and it takes forever for me to upload there (only 1-2 megabytes max) Can we somehow use local models that have the same directory structure as hf ?
```
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction (minimal, reproducible, runnable)
optimum-cli export openvino -m "d:\sdxl\LCMphoton" "d:\sdxl\LCMphotonov"
### Expected behavior
I want to be able to convert local models without having to download from huggingface. | https://github.com/huggingface/optimum/issues/1556 | closed | [
"bug"
] | 2023-11-30T16:09:24Z | 2023-12-09T22:37:44Z | 2 | patientx |
huggingface/safetensors | 396 | [Feature request] How about support async save to disk? | ### Feature request
How about support async save to disk?
### Motivation
the weight or optimizer is vary large for LLMs，so，it will waste a lot of time for tensor from cpu to disk。
If we can support async save to disk, it will be vary helpful.
### Your contribution
. | https://github.com/huggingface/safetensors/issues/396 | closed | [
"Stale"
] | 2023-11-30T02:55:25Z | 2024-02-13T01:46:40Z | null | ZHUI |
huggingface/transformers.js | 424 | [Question] Batch inference for vit | It seems like all the tests in the repository related to processors and image models use one image per input.
1. Do the models support feeding a batch of images as input during inference? Is there a speed benefit from this?
2. Are there any other optimization/parallelization tools in transformers.js that I can use to process a set of images?
Used model: vit base (google/vit-base-patch16-224-in21k), tiny and small distillations (WinKawaks/vit-tiny-patch16-224), exported in onnx format with optimum
| https://github.com/huggingface/transformers.js/issues/424 | closed | [
"question"
] | 2023-11-29T09:52:16Z | 2023-12-05T14:49:36Z | null | arseniymerkulov |
huggingface/transformers | 27,755 | How to inference the model with 200k length context | ### Model description
I want to test Yi-34B-200k, Although I ran through the model, as the context length increased, OOM appeared, and I wondered how I could test to 200k context length with sufficient GPU resources.
### Open source status
- [X] The model implementation is available
- [X] The model weights are available
### Provide useful links for the implementation
_No response_ | https://github.com/huggingface/transformers/issues/27755 | closed | [] | 2023-11-29T07:37:06Z | 2024-05-24T07:24:56Z | null | taishan1994 |
huggingface/transformers.js | 423 | Not able to load local classification onnx model | Was trying to follow the instruction of this page to load local custom model, but failed to find local path https://huggingface.co/docs/transformers.js/custom_usage
the code snippet
`
import { env, AutoTokenizer, AutoModelForSequenceClassification } from '@xenova/transformers';
env.useFS = true;
env.localModelPath = '/path/to/local/file'
env.allowRemoteModels = false;
let tokenizer = await AutoTokenizer.from_pretrained('tinybert');
let model = await AutoModelForSequenceClassification.from_pretrained('tinybert');
let inputs = await tokenizer('I love transformers!');
let { logits } = await model(inputs);
`
here is the file structure:
models
└── tinybert
├── config.json
├── onnx
│ ├── model.onnx
│ └── model_quantized.onnx
├── ort_config.json
├── special_tokens_map.json
├── tokenizer.json
├── tokenizer_config.json
└── vocab.txt
error:
(node:36959) ExperimentalWarning: stream/web is an experimental feature. This feature could change at any time
(Use `node --trace-warnings ...` to show where the warning was created)
Unable to load from local path "/Users/hzhang14/pete/2023_H1_spam/models/tinybert/tokenizer.json": "ReferenceError: Headers is not defined"
Unable to load from local path "/Users/hzhang14/pete/2023_H1_spam/models/tinybert/tokenizer_config.json": "ReferenceError: Headers is not defined"
file:///Users/hzhang14/pete/2023_H1_spam/node_modules/@xenova/transformers/src/utils/hub.js:462
throw Error(`\`local_files_only=true\` or \`env.allowRemoteModels=false\` and file was not found locally at "${localPath}".`);
^
Error: `local_files_only=true` or `env.allowRemoteModels=false` and file was not found locally at "/Users/hzhang14/pete/2023_H1_spam/models/tinybert/tokenizer.json".
at getModelFile (file:///Users/hzhang14/pete/2023_H1_spam/node_modules/@xenova/transformers/src/utils/hub.js:462:27)
at async getModelJSON (file:///Users/hzhang14/pete/2023_H1_spam/node_modules/@xenova/transformers/src/utils/hub.js:575:18)
at async Promise.all (index 0)
at async loadTokenizer (file:///Users/hzhang14/pete/2023_H1_spam/node_modules/@xenova/transformers/src/tokenizers.js:52:16)
at async Function.from_pretrained (file:///Users/hzhang14/pete/2023_H1_spam/node_modules/@xenova/transformers/src/tokenizers.js:3890:48)
at async file:///Users/hzhang14/pete/2023_H1_spam/js/test.mjs:9:17 | https://github.com/huggingface/transformers.js/issues/423 | closed | [
"question"
] | 2023-11-29T06:40:09Z | 2023-11-30T07:27:27Z | null | purezhanghan |
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