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/trl | 1,510 | [question] how to apply model parallism to solve cuda memory error | hi team. I am using the SFT and PPO code to train my model, link https://github.com/huggingface/trl/tree/main/examples/scripts.
Due to long context length and 7B-level model size, I am facing cuda memory issue on my single gpu.
Is there any straightforward manner to utilize multiple gpus on my server to train the model thru SFT and PPO script ?
such as spliting the model to multiple gpus as model parallism. Is there any argument parameters I can directly pass into my training script ?
Thanks a lot.
```
export CUDA_VISIBLE_DEVICES='7'; python examples/scripts/sft_travel.py \
--model_name_or_path="mistralai/Mistral-7B-Instruct-v0.2" \
--report_to="wandb" \
--learning_rate=5e-5 \
--per_device_train_batch_size=4 \
--gradient_accumulation_steps=16 \
--logging_steps=1 \
--num_train_epochs=120 \
--lr_scheduler_type "constant" \
--max_steps=-1 \
--gradient_checkpointing \
--max_seq_length 16000 \
--output_dir "8bit" \
--overwrite_output_dir True \
--logging_strategy "epoch" \
--evaluation_strategy "no"
``` | https://github.com/huggingface/trl/issues/1510 | closed | [] | 2024-04-06T02:09:36Z | 2024-05-06T17:02:35Z | null | yanan1116 |
huggingface/dataset-viewer | 2,667 | Rename datasets-server to dataset-viewer in infra internals? | Follow-up to #2650.
Is it necessary? Not urgent in any Case.
Some elements to review:
- [ ] https://github.com/huggingface/infra
- [ ] https://github.com/huggingface/infra-deployments
- [ ] docker image tags (https://hub.docker.com/r/huggingface/datasets-server-services-search -> https://hub.docker.com/r/huggingface/dataset-viewer-services-search)
- [ ] Helm chart name
- [ ] AWS parameters
- [ ] kubernetes namespaces
- [ ] Hub app names and tokens
- [ ] https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/datasets-server
- [ ] buckets: hf-datasets-server-statics-test, hf-datasets-server-statics
- [ ] MongoDB databases
- [ ] BetterUptime
- [ ] shared directories (PARQUET_METADATA_CACHE_APPNAME)
| https://github.com/huggingface/dataset-viewer/issues/2667 | closed | [
"question",
"P2"
] | 2024-04-05T16:53:34Z | 2024-04-08T09:26:14Z | null | severo |
huggingface/dataset-viewer | 2,666 | Change API URL to dataset-viewer.huggingface.co? | Follow-up to https://github.com/huggingface/dataset-viewer/issues/2650
Should we do it?
- https://github.com/huggingface/dataset-viewer/issues/2650#issuecomment-2040217875
- https://github.com/huggingface/moon-landing/pull/9520#issuecomment-2040220911
If we change it, we would have to update:
- moon-landing
- datasets
- the docs (hub, datasets, dataset-viewer)
- other written support (blog, observable, notion...)
If so, also change the dev URL: https://datasets-server.us.dev.moon.huggingface.tech.
We should also handle the redirection from the old URL to the new one. | https://github.com/huggingface/dataset-viewer/issues/2666 | closed | [
"question",
"P2"
] | 2024-04-05T16:49:13Z | 2024-04-08T09:24:43Z | null | severo |
huggingface/huggingface.js | 609 | [Question] What is the correct way to access commit diff results via http? | Data I am interested in:
Here's the endpoint to list commits
https://huggingface.co/api/models/SimonMA/Codellama-7b-lora-rps-adapter/commits/main | https://github.com/huggingface/huggingface.js/issues/609 | closed | [] | 2024-04-05T12:00:15Z | 2024-04-09T18:40:05Z | null | madgetr |
huggingface/dataset-viewer | 2,661 | Increase the number of backfill workers? | Today, it's 8. Let's try increasing it and see if it speeds up the backfill job.
The current throughput is 577 datasets/minute. | https://github.com/huggingface/dataset-viewer/issues/2661 | open | [
"question",
"P2",
"prod"
] | 2024-04-05T10:42:11Z | 2024-04-05T16:42:13Z | null | severo |
huggingface/transformers | 30,066 | How to calculate the mAP on this network? | ### System Info
I want to evaluate my network with the mean Average Precision. I don't know how to get the class-id of my gt data. Are there any examples to calculate the mAP with this library?
I use the DetrForObjectDetection with my own dataset.
### 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
this is my code to save the loss in a csv file. I also want to save the mAP in this file.
def on_train_epoch_end(self, trainer, pl_module):
train_loss = trainer.callback_metrics.get("training_loss").item()
val_loss = trainer.callback_metrics.get("validation/loss").item()
with open(self.file_path, 'a', newline='') as csvfile:
writer = csv.writer(csvfile)
if not self.header_written:
writer.writerow(["Epoch", "Train Loss", "Validation Loss"])
self.header_written = True
writer.writerow([pl_module.current_epoch, train_loss, val_loss])
### Expected behavior
I tried to get the data with this code:
gt_boxes = []
detected_boxes = []
for batch in self.val_dataloader:
pixel_values = batch['pixel_values'].to(pl_module.device)
pixel_mask = batch['pixel_mask'].to(pl_module.device)
labels = batch['labels']
# train_idx = batch['train_idx']
outputs = pl_module(pixel_values=pixel_values, pixel_mask=pixel_mask)
target_sizes = torch.tensor([image.shape[-2:] for image in pixel_values]).to(pixel_values.device)
detections = image_processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.5)[0]
for i in range(len(detections['scores'])):
prob_score = detections['scores'][i].item()
class_pred = detections['labels'][i].item()
box = detections['boxes'][i].detach().cpu().numpy()
detected_boxes.append([class_pred, prob_score, *box])
for label in labels:
gt_box = label['boxes']
for box in gt_box:
gt_boxes.append(box)
image_height = 2048
image_width = 2048
gt_boxes_abs = []
for box in gt_boxes:
x_min, y_min, width, height = box
x_max = x_min + width
y_max = y_min + height
x_min_abs = int(x_min * image_width)
y_min_abs = int(y_min * image_height)
x_max_abs = int(x_max * image_width)
y_max_abs = int(y_max * image_height)
class_id = ???
difficult = ???
crowd = ???
gt_boxes_abs.append([x_min_abs, y_min_abs, x_max_abs, y_max_abs, class_id, difficult, crowd])
adjusted_detected_boxes = []
converted_boxes = []
for box in detected_boxes:
class_id = box[0]
confidence = box[1]
x_min = box[2]
y_min = box[3]
x_max = box[4]
y_max = box[5]
converted_boxes.append([x_min, y_min, x_max, y_max, class_id, confidence]) | https://github.com/huggingface/transformers/issues/30066 | closed | [] | 2024-04-05T08:32:31Z | 2024-06-08T08:04:08Z | null | Sebi2106 |
huggingface/optimum-quanto | 152 | How does quanto calibrate torch functions? | I have learned quanto calibrate ops in module forms by adding module hooks, but how about torch functions like `torch.sigmoid`, `torch.elu`, and `torch.log` etc?
I think the output scale of `torch.sigmoid` could be directly evaluated similarly to quanto's approach with `softmax`. Additionally, `torch.elu` might be substituted with `torch.nn.ELU`.
However, I'm uncertain how functions like `torch.log`, which are unbounded and lack explicit module forms will be calibrated within quanto. | https://github.com/huggingface/optimum-quanto/issues/152 | closed | [
"question"
] | 2024-04-05T06:49:51Z | 2024-04-11T09:41:55Z | null | shuokay |
huggingface/candle | 2,007 | How to run inference of a (very) large model across mulitple GPUs ? | It is mentioned on README that candle supports multi GPU inference, using NCCL under the hood. How can this be implemented ? I wonder if there is any available example to look at..
Also, I know PyTorch has things like DDP and FSDP, is candle support for multi GPU inference comparable to these techniques ? | https://github.com/huggingface/candle/issues/2007 | open | [] | 2024-04-04T13:52:46Z | 2024-08-12T04:53:54Z | null | jorgeantonio21 |
huggingface/candle | 2,006 | How to get different outputs for the same prompt? | I used a gemma, it always returned same outputs for same prompt.
How can I get different outputs? Is there any method or parameter for sampling? (I even doubt that `top_p` works.)
| https://github.com/huggingface/candle/issues/2006 | closed | [] | 2024-04-04T10:43:31Z | 2024-04-13T11:17:36Z | null | Hojun-Son |
huggingface/chat-ui | 975 | is it possible to hide the setting from the users? most users do not want to create assistants, and they just want to use existing ones. | In the left-hand corner of hugginchat, "Assistants" and "Settings" are visible. We are considering whether it is possible to hide these options from our users, as they have expressed no interest in creating assistants and prefer to use existing ones. Many thanks for your kind help.. Howard | https://github.com/huggingface/chat-ui/issues/975 | open | [] | 2024-04-04T07:33:25Z | 2024-04-04T07:33:25Z | 0 | hjchenntnu |
huggingface/transformers.js | 679 | Speech Recognition/Whisper word level scores or confidence output | ### Question
Hey,
Big thanks for awesome project!
It possible to add score/confidence for word level output when using Speech Recognition/Whisper model?
Would appreciate any direction/comments or suggestion where to dig to add it.
Happy to submit PR if I will success in it.
Thanks!
| https://github.com/huggingface/transformers.js/issues/679 | open | [
"question"
] | 2024-04-04T07:04:00Z | 2024-04-04T07:04:00Z | null | wobbble |
huggingface/transformers | 30,034 | What is the data file format of `run_ner.py`? | ### Feature request
What is the correct format for custom dataset in run_ner.py? Would it be possible to include a few lines on this with a helpful example?
### Motivation
I am using the example script run_ner.py from [huggingface](https://github.com/huggingface)/transformers It is not possible to use standard conll format for the model fine-tuning of run_ner.
### Your contribution
We could include this in the corresponding readme. | https://github.com/huggingface/transformers/issues/30034 | closed | [
"Good First Issue"
] | 2024-04-04T06:36:30Z | 2024-04-08T11:50:00Z | null | sahil3773mehta |
huggingface/datasets | 6,777 | .Jsonl metadata not detected | ### Describe the bug
Hi I have the following directory structure:
|--dataset
| |-- images
| |-- metadata1000.csv
| |-- metadata1000.jsonl
| |-- padded_images
Example of metadata1000.jsonl file
{"caption": "a drawing depicts a full shot of a black t-shirt with a triangular pattern on the front there is a white label on the left side of the triangle", "image": "images/212734.png", "gaussian_padded_image": "padded_images/p_212734.png"}
{"caption": "an eye-level full shot of a large elephant and a baby elephant standing in a watering hole on the left side is a small elephant with its head turned to the right of dry land, trees, and bushes", "image": "images/212735.png", "gaussian_padded_image": "padded_images/p_212735.png"}
.
.
.
I'm trying to use dataset = load_dataset("imagefolder", data_dir='/dataset/', split='train') to load the the dataset, however it is not able to load according to the fields in the metadata1000.jsonl .
please assist to load the data properly
also getting
```
File "/workspace/train_trans_vae.py", line 1089, in <module>
print(get_metadata_patterns('/dataset/'))
File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 499, in get_metadata_patterns
raise FileNotFoundError(f"The directory at {base_path} doesn't contain any metadata file") from None
FileNotFoundError: The directory at /dataset/ doesn't contain any metadata file
```
when trying
```
from datasets.data_files import get_metadata_patterns
print(get_metadata_patterns('/dataset/'))
```
### Steps to reproduce the bug
dataset Version: 2.18.0
make a similar jsonl and similar directory format
### Expected behavior
creates a dataset object with the column names, caption,image,gaussian_padded_image
### Environment info
dataset Version: 2.18.0 | https://github.com/huggingface/datasets/issues/6777 | open | [] | 2024-04-04T06:31:53Z | 2024-04-05T21:14:48Z | 5 | nighting0le01 |
huggingface/lighteval | 143 | Do an intro notebook on how to use `lighteval` | https://github.com/huggingface/lighteval/issues/143 | closed | [
"documentation"
] | 2024-04-03T07:53:25Z | 2024-12-05T10:18:42Z | null | clefourrier | |
huggingface/accelerate | 2,614 | How to I selectively apply accelerate to trainers | I have two trainers in a script, one is SFTTrainer and one is PPOTrainer, both from trl library. Is it possible to only apply accelerate to PPOTrainer? | https://github.com/huggingface/accelerate/issues/2614 | closed | [] | 2024-04-03T06:39:05Z | 2024-05-21T15:06:36Z | null | zyzhang1130 |
huggingface/sentence-transformers | 2,568 | How to improve sentence-transformers' performance on CPU? | On the CPU, I tried huggingface‘s optimization.onnx and sentence_transformers and I found that on the task of feature_extraction, optimization.onnx was not as good as sentence_transformers in batch encoding performance.
My question is, are sentence_transformers the current ceiling on CPU performance? | https://github.com/huggingface/sentence-transformers/issues/2568 | closed | [] | 2024-04-03T02:09:14Z | 2024-04-23T09:17:39Z | null | chensuo2048 |
huggingface/datasets | 6,773 | Dataset on Hub re-downloads every time? | ### Describe the bug
Hi, I have a dataset on the hub [here](https://huggingface.co/datasets/manestay/borderlines). It has 1k+ downloads, which I sure is mostly just me and my colleagues working with it. It should have far fewer, since I'm using the same machine with a properly set up HF_HOME variable. However, whenever I run the below function `load_borderlines_hf`, it downloads the entire dataset from the hub and then does the other logic:
https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80
Let me know what I'm doing wrong here, or if it's a bug with the `datasets` library itself. On the hub I have my data stored in CSVs, but several columns are lists, so that's why I have the code to map splitting on `;`. I looked into dataset loading scripts, but it seemed difficult to set up. I have verified that other `datasets` and `models` on my system are using the cache properly (e.g. I have a 13B parameter model and large datasets, but those are cached and don't redownload).
__EDIT: __ as pointed out in the discussion below, it may be the `map()` calls that aren't being cached properly. Supposing the `load_dataset()` retrieve from the cache, then it should be the case that the `map()` calls also retrieve from the cached output. But the `map()` commands re-execute sometimes.
### Steps to reproduce the bug
1. Copy and paste the function from [here](https://github.com/manestay/borderlines/blob/4e161f444661e2ebfe643f3fe149d9258d63a57d/run_gpt/lib.py#L80) (lines 80-100)
2. Run it in Python `load_borderlines_hf(None)`
3. It completes successfully, downloading from HF hub, then doing the mapping logic etc.
4. If you run it again after some time, it will re-download, ignoring the cache
### Expected behavior
Re-running the code, which calls `datasets.load_dataset('manestay/borderlines', 'territories')`, should use the cached version
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-5.14.21-150500.55.7-default-x86_64-with-glibc2.31
- Python version: 3.10.13
- `huggingface_hub` version: 0.20.3
- PyArrow version: 15.0.0
- Pandas version: 1.5.3
- `fsspec` version: 2023.10.0 | https://github.com/huggingface/datasets/issues/6773 | closed | [] | 2024-04-02T17:23:22Z | 2024-04-08T18:43:45Z | 5 | manestay |
huggingface/transformers.js | 677 | How you debug/measure Python -> Javascript ONNX Conversion | ### Question
I have converted a couple ONNX models to use ONNXRuntimeWeb from using the Python onnx version as the source. Ive spent weeks debugging though. What's your strategy for comparing tensor values, etc, with these onnx models?
Ive console log'd N# of values from the tensor/array to see if the values have diverged far but it can get fatiguing. I can't simply just dump a numpy array and compare | https://github.com/huggingface/transformers.js/issues/677 | open | [
"question"
] | 2024-04-02T16:16:22Z | 2024-04-02T16:18:03Z | null | matbeedotcom |
huggingface/transformers.js | 676 | How to use fp16 version of the model file? | ### Question
example files: https://huggingface.co/Xenova/modnet/tree/main/onnx | https://github.com/huggingface/transformers.js/issues/676 | closed | [
"question"
] | 2024-04-02T12:10:24Z | 2024-04-03T02:56:52Z | null | cyio |
huggingface/chat-ui | 969 | Display does not automatically update after receiving message | After receiving the message, the chat page does not update and is always in the loading state. The received message can only be displayed after refreshing the page or switching sessions.
| https://github.com/huggingface/chat-ui/issues/969 | open | [
"question"
] | 2024-04-02T06:14:59Z | 2024-04-03T04:26:23Z | null | w4rw4r |
huggingface/dataset-viewer | 2,654 | Tutorial about how to start/run my own local dataset server. | Hey,
I'm new to the dataset server and rookie in the Web field. I wanted to build my own dataset server however, is there any tutorial that can guide me to build my own dataset server?
Many Thanks | https://github.com/huggingface/dataset-viewer/issues/2654 | closed | [] | 2024-04-02T01:30:12Z | 2024-05-11T15:03:50Z | null | ANYMS-A |
huggingface/accelerate | 2,603 | How to load a FSDP checkpoint model | I have fine tuned gemma 2b model using FSDP and these are the below files available under the checkpoint
```
optimizer_0 pytorch_model_fsdp_0 rng_state_0.pth rng_state_1.pth scheduler.pt trainer_state.json
```
How can i load the above FSDP object?
kindly help me with this issue,
| https://github.com/huggingface/accelerate/issues/2603 | closed | [] | 2024-04-01T16:53:24Z | 2024-05-11T15:06:21Z | null | nlpkiddo-2001 |
huggingface/datasets | 6,769 | (Willing to PR) Datasets with custom python objects | ### Feature request
Hi thanks for the library! I would like to have a huggingface Dataset, and one of its column is custom (non-serializable) Python objects. For example, a minimal code:
```
class MyClass:
pass
dataset = datasets.Dataset.from_list([
dict(a=MyClass(), b='hello'),
])
```
It gives error:
```
ArrowInvalid: Could not convert <__main__.MyClass object at 0x7a852830d050> with type MyClass: did not recognize Python value type when inferring an Arrow data type
```
I guess it is because Dataset forces to convert everything into arrow format. However, is there any ways to make the scenario work? Thanks!
### Motivation
(see above)
### Your contribution
Yes, I am happy to PR!
Cross-posted: https://discuss.huggingface.co/t/datasets-with-custom-python-objects/79050?u=fzyzcjy
EDIT: possibly related https://github.com/huggingface/datasets/issues/5766 | https://github.com/huggingface/datasets/issues/6769 | open | [
"enhancement"
] | 2024-04-01T13:18:47Z | 2024-04-01T13:36:58Z | 0 | fzyzcjy |
huggingface/optimum-quanto | 146 | Question about the gradient of QTensor and QBitTensor | I am confused by the gradient of the Quantizer and QBitTensor. Take QTensor as the example:
The evaluation of forward is:
```txt
data = base / scale (1)
data = round(data) (2)
data = clamp(data, qmin, qmax) (3)
```
I think the graidents should be:
```txt
grad_div = 1 / scale (1)
grad_round = 1 (2) # refer to "straight though estimator": https://arxiv.org/abs/1308.3432
grad_clamp = 1 if qmin < data < qmax else 0 (3)
```
According to chain rule, the gradient of Quantizer should be `grad_div * grad_round * grad_clamp` which is equal to `1 / scale if qmin < base/scale < qmax else 0`
I have reached QTensor's unit test and I find that dequantize is applied to QTensor before backward. I am confused by `Quantizer. backward` and the `dequantize` behavior before backward. | https://github.com/huggingface/optimum-quanto/issues/146 | closed | [
"question"
] | 2024-03-31T14:33:10Z | 2024-04-24T13:51:20Z | null | shuokay |
huggingface/transformers.js | 673 | Is dit-base supported | ### Question
There is a [Huggingface repo](https://huggingface.co/Xenova/dit-base) for the ONNX version of the dit-base model but I can't seem to make it work.
I keep getting the following error:
Is the model currently supported? | https://github.com/huggingface/transformers.js/issues/673 | closed | [
"question"
] | 2024-03-31T01:18:42Z | 2024-03-31T01:48:24Z | null | Maxzurek |
huggingface/datatrove | 143 | Understand the output of deduplication | Hi
I have arabic split from the CC trying to deduplicate it
I used datatrove for this with a small example
I got in my output folder two files
0000.c4_dup and 0000.c4_sig
Could you help me to understand this output
I cannot read its content as it's c/00000.c4_sig is not UTF-8 encoded and seems to be binary files
where should I see the nex text deduplicated
Thanks in advance | https://github.com/huggingface/datatrove/issues/143 | closed | [
"question"
] | 2024-03-30T23:16:21Z | 2024-05-06T09:30:43Z | null | Manel-Hik |
huggingface/candle | 1,971 | How to use `topk`? | I am trying to use `topk` to implement X-LoRA in Candle, and want to perform `topk` in the last dimension. Specifically, I need the `indices` return value (as returned by [`torch.topk`](https://pytorch.org/docs/stable/generated/torch.topk.html)).
These indices will either be used to creaste a mask to zero out all the values which are _not_ in the topk, and/or used to apply scalings on the nonzero values. This is a may be hard to understand, as such please see [this](https://github.com/EricLBuehler/xlora/blob/3637d1e00854649e8b9162f8f87233248577162c/src/xlora/xlora_insertion.py#L50-L63) snippet from our X-LoRA library.
Is there a way to implement this with the current Candle functions, or is this planned to be implemented as a function?
---
After looking at the Mixtral MoE selection implementation, I cannot really understand it:
> https://github.com/huggingface/candle/blob/3144150b8d1b80b2c6b469dcab5b717598f0a458/candle-transformers/src/models/mixtral.rs#L302-L323
How does this work? Thanks! | https://github.com/huggingface/candle/issues/1971 | closed | [] | 2024-03-30T20:29:45Z | 2024-07-23T02:02:58Z | null | EricLBuehler |
huggingface/transformers.js | 671 | What is involved in upgrading to V3? | ### Question
In anticipation of being able to [generate music](https://github.com/xenova/transformers.js/issues/668) with musicGen I'm attempting to switch my project over to version 3, which I was able to build on my mac.
I noticed that when using SpeechT5, the voice sounds completely garbled. I've attached a zip with two example WAV files.
[audio_wav_examples.zip](https://github.com/xenova/transformers.js/files/14806203/audio_wav_examples.zip)
I suspect I'm overlooking something, and need to upgrade some other things too? So my question is: could you give a broad overview of all the parts I need to upgrade?
Things I've checked or tried:
- Whisper Speech to Text is still working after 'dropping in' the new version.
- Cleared caches (the JS caches)
- Grabbing 'official' package from the [link to the JSDelivr repository](https://cdn.jsdelivr.net/npm/@xenova/transformers@3.0.0-alpha.0) in the V3 readme, but that doesn't work, which I assume is just an auto-build glitch.
- Switching WAV generation code to the one in Transformers.js V3 example.
- Switching to the [example webworker](https://github.com/xenova/transformers.js/blob/v3/examples/text-to-speech-client/src/worker.js) in the V3 branch, which looks very different, but it had no effect. (The old code was basically `synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { quantized: false });`).
- The wav blob from the worker has the same issue as the raw Float32 array, so the issue is not in the way I was playing those arrays.
| https://github.com/huggingface/transformers.js/issues/671 | closed | [
"question"
] | 2024-03-29T18:09:23Z | 2024-03-31T13:50:27Z | null | flatsiedatsie |
huggingface/datasets | 6,764 | load_dataset can't work with symbolic links | ### Feature request
Enable the `load_dataset` function to load local datasets with symbolic links.
E.g, this dataset can be loaded:
├── example_dataset/
│ ├── data/
│ │ ├── train/
│ │ │ ├── file0
│ │ │ ├── file1
│ │ ├── dev/
│ │ │ ├── file2
│ │ │ ├── file3
│ ├── metadata.csv
while this dataset can't:
├── example_dataset_symlink/
│ ├── data/
│ │ ├── train/
│ │ │ ├── sym0 -> file0
│ │ │ ├── sym1 -> file1
│ │ ├── dev/
│ │ │ ├── sym2 -> file2
│ │ │ ├── sym3 -> file3
│ ├── metadata.csv
I have created an example dataset in order to reproduce the problem:
1. Unzip `example_dataset.zip`.
2. Run `no_symlink.sh`. Training should start without issues.
3. Run `symlink.sh`. You will see that all four examples will be in train split, instead of having two examples in train and two examples in dev. The script won't load the correct audio files.
[example_dataset.zip](https://github.com/huggingface/datasets/files/14807053/example_dataset.zip)
### Motivation
I have a very large dataset locally. Instead of initiating training on the entire dataset, I need to start training on smaller subsets of the data. Due to the purpose of the experiments I am running, I will need to create many smaller datasets with overlapping data. Instead of copying the all the files for each subset, I would prefer copying symbolic links of the data. This way, the memory usage would not significantly increase beyond the initial dataset size.
Advantages of this approach:
- It would leave a smaller memory footprint on the hard drive
- Creating smaller datasets would be much faster
### Your contribution
I would gladly contribute, if this is something useful to the community. It seems like a simple change of code, something like `file_path = os.path.realpath(file_path)` should be added before loading the files. If anyone has insights on how to incorporate this functionality, I would greatly appreciate your knowledge and input. | https://github.com/huggingface/datasets/issues/6764 | open | [
"enhancement"
] | 2024-03-29T17:49:28Z | 2025-04-29T15:06:28Z | 1 | VladimirVincan |
huggingface/transformers.js | 670 | Are tokenizers supposed to work in the browser? | ### Question
I'd love to use some pretrained tokenizers, right in my browser. On a number of occasions, I've tried to use this library to load and use a tokenizer in my browser, but it always fails with an error like this:
```
Uncaught (in promise) SyntaxError: JSON.parse: unexpected character at line 1 column 1 of the JSON data
getModelJSON hub.js:584
loadTokenizer tokenizers.js:62
from_pretrained tokenizers.js:4398
gv9xs tok.js:3
gv9xs tok.js:9
newRequire dev.42f35062.js:71
<anonymous> dev.42f35062.js:122
<anonymous> dev.42f35062.js:145
hub.js:584:16
gv9xs tok.js:3
AsyncFunctionThrow self-hosted:856
(Async: async)
gv9xs tok.js:9
newRequire dev.42f35062.js:71
<anonymous> dev.42f35062.js:122
<anonymous> dev.42f35062.js:145
```
Is there anything I can do to make this work? My code is rather simple:
```
import { AutoTokenizer } from '@xenova/transformers'
;(async function () {
const tokenizer = await AutoTokenizer.from_pretrained(
'Xenova/bert-base-uncased'
)
console.log(tokenizer)
const { input_ids } = await tokenizer('I love transformers!')
console.log(input_ids)
})()
```
I serve this code via a Parcel development server, but it's never worked for me. Any advice would be greatly appreciated! | https://github.com/huggingface/transformers.js/issues/670 | closed | [
"question"
] | 2024-03-29T16:10:46Z | 2024-03-29T16:53:21Z | null | Vectorrent |
huggingface/transformers.js | 669 | TinyLlama Conversion | ### Question
I ran the converter script on the tinyllama repo for both the TinyLlama models ([intermediate step 1431K 3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) and [chat v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)) and uploaded them to my repo ([intermediate step 1431K 3T](https://huggingface.co/dmmagdal/tinyllama-1.1B-intermediate-step-1431k-3T-onnx-js) [chat v1.0](https://huggingface.co/dmmagdal/tinyllama-1.1B-chat-v1.0-onnx-js); I also have uploads where the quantized flag was enabled).
When I try to run either of my converted models with the `AutoModelForCausalLM` or `pipeline`, I get the following error:
```
Error: Could not locate file: "https://huggingface.co/dmmagdal/tinyllama-1.1B-chat-v1.0-onnx-js/resolve/main/onnx/decoder_model_merged.onnx".
```
This error seems to be correct in that I do not have that file in my repo. Was there something I did wrong in the conversion process or is the model not fully supported by transformers.js?
I'm not sure how or if it relates to the TinyLlama repo you have here: https://huggingface.co/Xenova/TinyLLama-v0/tree/main | https://github.com/huggingface/transformers.js/issues/669 | closed | [
"question"
] | 2024-03-29T14:50:06Z | 2025-10-13T04:57:32Z | null | dmmagdal |
huggingface/datatrove | 142 | Deduplicating local data throws an error | Hi,
I have data in my local machine in the format of a jsonl file and I want to deduplicate it. I'm using the following example:
`sent_dedup_config = SentDedupConfig(
n_sentences=3,
split_sentences=False, # set to False to split on \n instead
only_dedup_in_index=True,
min_doc_words=50,
)
FINDER_WORKERS = 10 # this will speed up/parallelize step 2
def run_example():
pipeline_1 = [
JsonlReader("CC_data_inputs/"),
SentenceDedupSignature(output_folder="cc_output/sigs", config=sent_dedup_config, finder_workers=FINDER_WORKERS),
]
pipeline_2 = [SentenceFindDedups(data_folder="cc_output/sigs", output_folder="cc_output/dups", config=sent_dedup_config)]
pipeline_3 = [
JsonlReader(data_folder="CC_data_inputs/"),
SentenceDedupFilter(data_folder="cc_output/dups", config=sent_dedup_config),
]
executor_1: PipelineExecutor = LocalPipelineExecutor(pipeline=pipeline_1, workers=4, tasks=4)
executor_2: PipelineExecutor = LocalPipelineExecutor(pipeline=pipeline_2, workers=1, tasks=FINDER_WORKERS)
executor_3: PipelineExecutor = LocalPipelineExecutor(pipeline=pipeline_3, workers=4, tasks=4)
print(executor_1.run())
print(executor_2.run())
print(executor_3.run())
`
I edited the first pipeline to just read the jsonl file (assuming that my data is ready directly for step 2). When I run the code, it throws this error:
Traceback (most recent call last):
File "/home/ubuntu/deduplication/sentence_deduplication.py", line 4, in <module>
from datatrove.pipeline.dedup.sentence_dedup import SentDedupConfig
ImportError: cannot import name 'SentDedupConfig' from 'datatrove.pipeline.dedup.sentence_dedup' (/home/ubuntu/miniconda3/lib/python3.11/site-packages/datatrove/pipeline/dedup/sentence_dedup.py)
My data consists of a set of 5 jsonl files inside the folder CC_data_inputs. I just reinstalled the datatrove library. Could you help me figure it out? | https://github.com/huggingface/datatrove/issues/142 | closed | [
"question"
] | 2024-03-29T12:31:30Z | 2024-04-24T14:15:58Z | null | Manel-Hik |
huggingface/optimum-intel | 642 | How to apply LoRA adapter to a model loaded with OVModelForCausalLM()? | In the transformers library, we can load multiple adapters to the original model by load_adapter then switch the specified adapter with set_adapter like below.
```
# base model
model = AutoModelForCausalLM.from_pretrained(
model_name,
)
# load multiple adapters
model.load_adapter("model/adapter1/", "adapter1")
model.load_adapter("model/adapter2/", "adapter2")
# switch adapter
model.set_adapter("adapter2")
```
Now I want to apply LoRA adapters with OpenVINO, but I can't find an example of it.
Is it possible to do it with OVModelForCausalLM?
| https://github.com/huggingface/optimum-intel/issues/642 | closed | [] | 2024-03-29T01:13:44Z | 2024-08-03T12:34:21Z | null | nai-kon |
huggingface/transformers | 29,948 | How to All Utilize all GPU's when device="balanced_low_0" in GPU setting | ### System Info
I know that while loading the model in "balanced_low_0" GPU setting the model is loaded into all GPU's apart from 0: GPU. Where the 0: GPU is left to do the text inference. (i.e. text inference as in performing all the calculation to generate response inside the LLM)
So, as per the give device parameter my model is loaded onto 1,2,3 GPU's and 0: GPU is left for inference.
| ID | GPU | MEM |
| 0 | 0% | 3% |
| 1 | 0% | 83% |
| 2 | 0% | 82% |
| 3 | 0% | 76% |
Question: How can i also utilize the remaining 1,2,3 GPU's to perform text inference not only 0:GPU?
Context: "balanced_low_0" evenly splits the model on all GPUs except the first one, and only puts on GPU 0 what does not fit on the others. This option is great when you need to use GPU 0 for some processing of the outputs, like when using the generate function for Transformers models
Reference: https://huggingface.co/docs/accelerate/en/concept_guides/big_model_inference#designing-a-device-map
CC:
@gante @ArthurZucker and @younesbelkada
Apologies if the ticket is raised under different bucket
### 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
na
### Expected behavior
na | https://github.com/huggingface/transformers/issues/29948 | closed | [] | 2024-03-28T19:54:09Z | 2024-05-07T13:43:08Z | null | kmukeshreddy |
huggingface/dataset-viewer | 2,649 | Should we support /filter on columns that contain SQL commands? | See the `schema` column on https://huggingface.co/datasets/motherduckdb/duckdb-text2sql-25k. Clicking on any of the 'classes' leads to an error
<img width="1209" alt="Capture d’écran 2024-03-28 à 15 11 50" src="https://github.com/huggingface/datasets-server/assets/1676121/3aaf779f-0465-429a-bafb-1a16ff5f2901">
The erroneous URL is:
https://datasets-server.huggingface.co/filter?dataset=motherduckdb%2Fduckdb-text2sql-25k&config=default&split=train&offset=0&length=100&where=schema%3D%27CREATE+TABLE+%22venue%22+%28%0A++%22venueId%22+INTEGER+NOT+NULL%2C%0A++%22venueName%22+VARCHAR%28100%29%2C%0A++%22venueInfo%22+JSON%2C%0A++PRIMARY+KEY+%28%22venueId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22author%22+%28%0A++%22authorId%22+INTEGER+NOT+NULL%2C%0A++%22authorName%22+VARCHAR%2850%29%2C%0A++%22authorPublications%22+INT%5B%5D%2C%0A++PRIMARY+KEY+%28%22authorId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22dataset%22+%28%0A++%22datasetId%22+INTEGER+NOT+NULL%2C%0A++%22datasetName%22+VARCHAR%2850%29%2C%0A++%22datasetInfo%22+STRUCT%28v+VARCHAR%2C+i+INTEGER%29%2C%0A++PRIMARY+KEY+%28%22datasetId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22journal%22+%28%0A++%22journalId%22+INTEGER+NOT+NULL%2C%0A++%22journalName%22+VARCHAR%28100%29%2C%0A++%22journalInfo%22+MAP%28INT%2C+DOUBLE%29%2C%0A++PRIMARY+KEY+%28%22journalId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22keyphrase%22+%28%0A++%22keyphraseId%22+INTEGER+NOT+NULL%2C%0A++%22keyphraseName%22+VARCHAR%2850%29%2C%0A++%22keyphraseInfo%22+VARCHAR%2850%29%5B%5D%2C%0A++PRIMARY+KEY+%28%22keyphraseId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22paper%22+%28%0A++%22paperId%22+INTEGER+NOT+NULL%2C%0A++%22title%22+VARCHAR%28300%29%2C%0A++%22venueId%22+INTEGER%2C%0A++%22year%22+INTEGER%2C%0A++%22numCiting%22+INTEGER%2C%0A++%22numCitedBy%22+INTEGER%2C%0A++%22journalId%22+INTEGER%2C%0A++%22paperInfo%22+UNION%28num+INT%2C+str+VARCHAR%29%2C%0A++PRIMARY+KEY+%28%22paperId%22%29%2C%0A++FOREIGN+KEY%28%22journalId%22%29+REFERENCES+%22journal%22%28%22journalId%22%29%2C%0A++FOREIGN+KEY%28%22venueId%22%29+REFERENCES+%22venue%22%28%22venueId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22cite%22+%28%0A++%22citingPaperId%22+INTEGER+NOT+NULL%2C%0A++%22citedPaperId%22+INTEGER+NOT+NULL%2C%0A++%22citeInfo%22+INT%5B%5D%2C%0A++PRIMARY+KEY+%28%22citingPaperId%22%2C%22citedPaperId%22%29%2C%0A++FOREIGN+KEY%28%22citedpaperId%22%29+REFERENCES+%22paper%22%28%22paperId%22%29%2C%0A++FOREIGN+KEY%28%22citingpaperId%22%29+REFERENCES+%22paper%22%28%22paperId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22paperDataset%22+%28%0A++%22paperId%22+INTEGER%2C%0A++%22datasetId%22+INTEGER%2C%0A++%22paperDatasetInfo%22+JSON%2C%0A++PRIMARY+KEY+%28%22datasetId%22%2C+%22paperId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22paperKeyphrase%22+%28%0A++%22paperId%22+INTEGER%2C%0A++%22keyphraseId%22+INTEGER%2C%0A++%22paperKeyphraseInfo%22+JSON%2C%0A++PRIMARY+KEY+%28%22keyphraseId%22%2C%22paperId%22%29%2C%0A++FOREIGN+KEY%28%22paperId%22%29+REFERENCES+%22paper%22%28%22paperId%22%29%2C%0A++FOREIGN+KEY%28%22keyphraseId%22%29+REFERENCES+%22keyphrase%22%28%22keyphraseId%22%29%0A%29%3B%0A%0ACREATE+TABLE+%22writes%22+%28%0A++%22paperId%22+INTEGER%2C%0A++%22authorId%22+INTEGER%2C%0A++%22writesInfo%22+JSON%2C%0A++PRIMARY+KEY+%28%22paperId%22%2C%22authorId%22%29%2C%0A++FOREIGN+KEY%28%22paperId%22%29+REFERENCES+%22paper%22%28%22paperId%22%29%2C%0A++FOREIGN+KEY%28%22authorId%22%29+REFERENCES+%22author%22%28%22authorId%22%29%0A%29%3B%27
```json
{"error":"Parameter 'where' contains invalid symbols"}
```
It's because the content includes some of the forbidden symbols:
https://github.com/huggingface/datasets-server/blob/4dddea2e6a476d52ba5be0c7c64fb8eca9827935/services/search/src/search/routes/filter.py#L53
Do you think it's possible to support the above query? Or should we handle the error on the Hub (not easy to do more than currently)? | https://github.com/huggingface/dataset-viewer/issues/2649 | open | [
"question",
"api",
"P2"
] | 2024-03-28T14:14:01Z | 2024-03-28T14:24:34Z | null | severo |
huggingface/accelerate | 2,593 | How to use training function rather than training scripts in multi GPUs and multi node? | I confirmed that the Multi-gpu launcher is executed based on the training function using the PrepareForLaunch function in "accelerate/examples/multigpu_remote_launcher.py".
Usually, the "accelerate launch" or "python -m torch.distributed.run" command is used for multi-node, but is there a way to utilize a training function like the PrepareForLaunch function? | https://github.com/huggingface/accelerate/issues/2593 | closed | [] | 2024-03-28T07:05:50Z | 2024-05-05T15:06:26Z | null | wlsghks4043 |
huggingface/alignment-handbook | 144 | Can we please add the option to work with a tokenized dataset, escpailly for the CPT task. | Since we have the CPT task now, it would be nice to have the ability to feel a tokenized and packed dataset directly. | https://github.com/huggingface/alignment-handbook/issues/144 | open | [] | 2024-03-27T18:31:58Z | 2025-02-27T16:23:06Z | 1 | shamanez |
huggingface/transformers.js | 668 | Is it possible to run a music / sounds generation model? | ### Question
I'd love to create a browser-based music generation tool, or one that can turn text into sound effects. Is that supported?
I guess my more general question is: can Transformers.js run pretty much any .onnx I throw at it, or does each model require some level of implementation before it can be used? | https://github.com/huggingface/transformers.js/issues/668 | closed | [
"question"
] | 2024-03-27T18:22:31Z | 2024-05-13T21:17:54Z | null | flatsiedatsie |
huggingface/optimum-quanto | 139 | Dequantizing tensors using quanto | I noticed the quantized models have these 4 additional features, for every weight in the original, e.g:
```
model.layers.0.mlp.down_proj.activation_qtype,
model.layers.0.mlp.down_proj.input_scale,
model.layers.0.mlp.down_proj.output_scale,
model.layers.0.mlp.down_proj.weight_qtype
```
I guess `qtype` refers to the quantized datatype, and `scale` probably refers to the scaling factor used during quantization? Although what is the difference between `input_scale` and `output scale`? Is it possible to recreate the exact original tensor using these values and the quantized weight?
If yes, then what would the formula be for the dequantization? | https://github.com/huggingface/optimum-quanto/issues/139 | closed | [
"question"
] | 2024-03-27T18:00:34Z | 2024-04-11T09:22:29Z | null | raunaks13 |
huggingface/safetensors | 458 | Safetensors uses excessive RAM when saving files | Safetensors uses around twice the RAM that `torch.save`:
```python
import resource
import torch
from safetensors.torch import save_file
torch.save({'tensor': torch.randn((500000000))}, 'test.torch')
print(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
save_file({'tensor': torch.randn((500000000))}, 'test.safetensors')
print(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
```
Output:
```
2308324
4261528
```
I believe this is because safetensors loads the full tensor in the `prepare` function instead of streaming it. Is it possible to stream the writes instead? For instance, having a `prepare_metadata` function that generates the metadata first, writing that first, then each individual tensor. | https://github.com/huggingface/safetensors/issues/458 | closed | [
"Stale"
] | 2024-03-27T12:11:38Z | 2024-05-02T01:47:32Z | 1 | sheepymeh |
huggingface/transformers | 29,897 | How to finetune a language model after extent token embeddings? | If I add some new tokens for a language model, I will get some random initialized weights in embeddings and lm_head. Is there any official way to train only these new weights? Or all I can do is adding hooks to the tensors to zero the gradient for weights I do not want to change? | https://github.com/huggingface/transformers/issues/29897 | closed | [] | 2024-03-27T08:20:24Z | 2024-03-27T15:01:04Z | null | bluewanderer |
huggingface/text-generation-inference | 1,677 | how to get the latest version number? | In the document, I use "docker run ghcr.io/huggingface/text-generation-inference:latest" to run the latest version of tgi. But in a production environment, I need to fix the version number. I can't find any webpage similar to [docker hub](https://hub.docker.com/r/pytorch/manylinux-cuda102). So how can I use docker command line to get the version list of huggingface/text-generation-inference? | https://github.com/huggingface/text-generation-inference/issues/1677 | closed | [] | 2024-03-27T05:43:49Z | 2024-03-29T02:30:10Z | null | fancyerii |
huggingface/optimum-quanto | 134 | Should quanto use int dtype in AffineQuantizer instead of uint? | According to code in https://github.com/huggingface/quanto/blob/main/quanto/tensor/qbitstensor.py#L34 I find quanto use uint dtype to store the quantized value in affine quantizer, while in symmetric quantizer it is int dtype
https://github.com/huggingface/quanto/blob/main/quanto/tensor/qtensor.py#L62.
Taking hardware into consideration, If we quantize both weight and activation to int types, will it save the cost of GPU or NPU since this only requires integer-type MAC arrays | https://github.com/huggingface/optimum-quanto/issues/134 | closed | [
"question"
] | 2024-03-26T14:21:25Z | 2024-04-11T09:25:09Z | null | shuokay |
huggingface/hub-docs | 1,257 | Add section about deprecation of script-based datasets? | Asked here: https://github.com/huggingface/datasets-server/issues/2385#issuecomment-2017984722
> Perhaps a little bit of suggestion from me is to include a disclaimer in the docs so that others are aware that developing a custom script is not supported.
It would also help answer the discussions + we could link in the error message directly.
---
On the other hand, maybe we just want to deprecate it sooner than later, and not spend too much time on this. | https://github.com/huggingface/hub-docs/issues/1257 | open | [
"question"
] | 2024-03-26T13:20:27Z | 2024-03-26T17:49:50Z | null | severo |
huggingface/candle | 1,941 | [help] how to update a portion of a long tensor | I'm aware of the closed issue(#1163 ) and understand that Var is mutable and Tensor is immutable by design. But I find it hard to impl some logic if it's impossible to update a portion of a Tensor.
For example, how can I generate a pairwise combination from two 2d tensors:
```rust
let a = Tensor::new(&[[1.0], [2.0]], &device)?;
let b = Tensor::new(&[[3.0], [4.0]], &device)?;
// how to generate a tensor that is the pair combination of the two?
// [[1, 3], [1, 4], [2, 3], [2, 4]]
let c = Tensor::zeros(&[2, 2, 1], DType::F32, &device)?;
for i in 0..a.dim(0)? {
for j in 0..b.dim(0)? {
// won't work!
// here we cannot set the content of the tensor via `set`
c.i((i, j)).set(Tensor::cat(&[&a, &b], 0)?);
}
}
```
| https://github.com/huggingface/candle/issues/1941 | closed | [] | 2024-03-26T11:47:56Z | 2024-04-07T15:42:45Z | null | michael8090 |
huggingface/optimum | 1,776 | How to convert a model(tf_model.h5) with tokenizer folder to the onnx format | ### Feature request
I have trained the TensorFlow model using the Transformers library and saved the trained model and tokenizer in a folder named MODEL_WITH_TOKENIZER. The model is stored inside the folder in a **.h5** format - **tf_model.h5**
Here is the folder structure.
I want to convert the model to .onnx format
Should I convert the entire MODEL_WITH_TOKENIZER folder to .onnx or only the tf_model.h5 file to onnx?
what are the steps
### Motivation
Hi, I have trained the TensorFlow model using the Transformers library and saved the trained model and tokenizer in a folder named MODEL_WITH_TOKENIZER. The model is stored in the **.h5** format - **model.h5**
Here is the folder structure.
I want to convert the model to .onnx format
Should I convert the entire MODEL_WITH_TOKENIZER folder to .onnx or only the tf_model.h5 file to onnx?
what are the steps
### Your contribution
I have trained the TensorFlow model using the Transformers library and saved the trained model and tokenizer in a folder named MODEL_WITH_TOKENIZER. The model is stored in the **.h5** format - **tf_model.h5**
Here is the folder structure.
I want to convert the model to .onnx format
Should I convert the entire MODEL_WITH_TOKENIZER folder to .onnx or only the tf_model.h5 file to onnx?
what are the steps | https://github.com/huggingface/optimum/issues/1776 | open | [
"onnx"
] | 2024-03-26T10:48:02Z | 2024-10-14T13:35:13Z | null | pradeepdev-1995 |
huggingface/alignment-handbook | 142 | Efficient dialog data format for KTO training | I have dialogs in the shareGPT format (see below) and for each `gpt` turn a label (thumbs up or thumbs down). But for KTO training, I have only seen datasets with the columns `prompt`, `completion` and `label` (see e.g. https://huggingface.co/datasets/trl-lib/kto-mix-14k).
Do I need to unwind my shareGPT dialogs (see below) for KTO training, or is there some more efficient format I can use?
How should the dialog history be encoded in the `prompt` column (see below)?
shareGPT-Format:
```
{"conversations":[
{"from":"system","value":"You are a friendly assistant for ....\n"},
{"from":"human","value":"Hello, I am Sam and ..."},
{"from":"gpt","value":"Welcome Sam, so you ...."},
{"from":"human","value":"Yes, but ...."},
{"from":"gpt","value":"Then ..."}
]}
```
Transformed to KTO, with `prompt` column as close as possible to https://huggingface.co/datasets/trl-lib/kto-mix-14k:
```
prompt, completion, label
[ { "content": "You are a friendly assistant for ....\n", "role": "system" }, { "content": "Hello, I am Sam and ...", "role": "human" }], {"role":"gpt","content":"Welcome Sam, so you ...."}, true
[ { "content": "You are a friendly assistant for ....\n", "role": "system" }, { "content": "Hello, I am Sam and ...", "role": "human" }, {"role":"gpt","content":"Welcome Sam, so you ...."}, {"role":"human","content":"Yes, but ...."}], {"role":"gpt","content":"Then ..."}, false
`` | https://github.com/huggingface/alignment-handbook/issues/142 | open | [] | 2024-03-26T10:29:38Z | 2024-03-26T10:30:08Z | 0 | DavidFarago |
huggingface/transformers.js | 664 | How to confirm if webgpu actually working in the backend with inferencing | ### Question
Hi Team,
Thanks for the awsome library.
Recently I am experimenting to run background remove model in the client side using webgpu. I came across this solution https://huggingface.co/spaces/Xenova/remove-background-webgpu.
Tried to replicate the same in my local using your V3 branch.
The way I have used it is as below.
```
const model = await AutoModel.from_pretrained('briaai/RMBG-1.4', {
// Do not require config.json to be present in the repository
config: { model_type: 'custom' },
device: 'webgpu',
dtype: 'fp32'
})
```
I can see significant improvement while enabling `device: 'webgpu',` instead of wasm.
Question 1:
How can I confirm if the webgpu is being used in the backend while inferencing as I can see in both of the case (with webgpu and without webgpu) the `ort-wasm-simd.jsep.wasm` file is getting loaded. why we are not loading `ort.webgpu.min`?
SS
Question 2:
It would be helpfull if you can share the repo for this `https://huggingface.co/spaces/Xenova/remove-background-webgpu ` as the code in huggingface is bundled.
Thanks in advance!!
| https://github.com/huggingface/transformers.js/issues/664 | open | [
"question"
] | 2024-03-26T08:17:05Z | 2024-07-24T06:13:50Z | null | abiswas529 |
huggingface/dataset-viewer | 2,630 | Take spawning.io opted out URLs into account in responses? | In particular, for images (assets / cached-assets).
Raised internally: https://huggingface.slack.com/archives/C040J3VPJUR/p1702578556307069?thread_ts=1702577137.311409&cid=C040J3VPJUR | https://github.com/huggingface/dataset-viewer/issues/2630 | open | [
"question",
"P2"
] | 2024-03-25T11:49:49Z | 2024-03-25T11:49:58Z | null | severo |
huggingface/datasets | 6,756 | Support SQLite files? | ### Feature request
Support loading a dataset from a SQLite file
https://huggingface.co/datasets/severo/test_iris_sqlite/tree/main
### Motivation
SQLite is a popular file format.
### Your contribution
See discussion on slack: https://huggingface.slack.com/archives/C04L6P8KNQ5/p1702481859117909 (internal)
In particular: a SQLite file can contain multiple tables, which might be matched to multiple configs. Maybe the detail of splits and configs should be defined in the README YAML, or use the same format as for ZIP files: `Iris.sqlite::Iris`.
See dataset here: https://huggingface.co/datasets/severo/test_iris_sqlite
Note: should we also support DuckDB files? | https://github.com/huggingface/datasets/issues/6756 | closed | [
"enhancement"
] | 2024-03-25T11:48:05Z | 2024-03-26T16:09:32Z | 3 | severo |
huggingface/dataset-viewer | 2,629 | Detect when a new commit only changes the dataset card? | Ideally, when we change the contents of the dataset card (not the YAML part), the responses computed by the datasets server should not be recomputed, because they will lead to the same results.
asked here (private slack channel): https://huggingface.slack.com/archives/C04N96UGUFM/p1701862863691809
> Sometimes I don't modify the dataset cards of datasets that have too many configs because I don't want to break the viewer for too long. I think we can detect when the change is only about the content dataset card and the dataset itself didn't change ?
| https://github.com/huggingface/dataset-viewer/issues/2629 | closed | [
"question",
"improvement / optimization",
"P2"
] | 2024-03-25T10:57:36Z | 2024-06-19T16:02:33Z | null | severo |
huggingface/dataset-viewer | 2,627 | Replace our custom "stale bot" action with the GitHub's one? | See `actions/stale@v5`
```yaml
name: Mark inactive issues as stale
on:
schedule:
- cron: "30 1 * * *"
jobs:
close-issues:
runs-on: ubuntu-latest
permissions:
issues: write
pull-requests: write
steps:
- uses: actions/stale@v5
with:
days-before-issue-stale: 30
days-before-issue-close: -1
stale-issue-label: "stale"
stale-issue-message: "This issue is stale because it has been open for 30 days with no activity."
close-issue-message: "This issue was closed because it has been inactive for X days since being marked as stale."
days-before-pr-stale: -1
days-before-pr-close: -1
repo-token: ${{ secrets.GITHUB_TOKEN }}
```
from https://huggingface.slack.com/archives/C493XH5FX/p1701942940388579?thread_ts=1701932787.319359&cid=C493XH5FX | https://github.com/huggingface/dataset-viewer/issues/2627 | open | [
"question",
"ci",
"P2"
] | 2024-03-25T10:48:47Z | 2024-03-25T10:49:02Z | null | severo |
huggingface/candle-paged-attention | 1 | How to use candle-paged-attention in candle models? | Could you provide an example of candle-paged-attention for actual usage in candle models (candle-examples)? Is this crate ready to be used in candle? i.e., tested in end2end model inference? I'm a little bit confused about the construction of block_tables and context_lens. | https://github.com/huggingface/candle-paged-attention/issues/1 | open | [] | 2024-03-25T09:09:24Z | 2024-03-25T12:07:13Z | null | guoqingbao |
huggingface/optimum | 1,769 | Accuracy change with BetterTransformer | When transforming the model into BetterTransformer model I'm seeing accuracy drop on the models.
The output scores changes considerably (upto 1-2 decimal points of precision).
**Is accuracy change expected when switching to BetterTransformer ?** I'm not performing any ORT compilation or quantization on the model.
From what I know FlashAttention is not supposed to change any accuracy since it is an exact attention score algorithm. Hence I'm not sure what is causing this change in score.
Steps to reproduce
```
from transformers import AutoModelForSequenceClassification , AutoTokenizer
from optimum.bettertransformer import BetterTransformer
tokenizer=AutoTokenizer.from_pretrained("BAAI/bge-reranker-large")
original_model = AutoModelForSequenceClassification.from_pretrained("BAAI/bge-reranker-large").to('cuda:0')
transformed_model = BetterTransformer.transform(original_model, keep_original_model=True).to('cuda:0')
sentences_batch=[['do you like fox cookies', 'fox big brown fox']]
inputs = tokenizer(sentences_batch,padding=True,truncation=True,return_tensors="pt",max_length=512,).to('cuda:0')
better_transformer_scores = transformed_model(**inputs, return_dict=True).logits.view(-1).float()
print(f"BetterTransfomer output: {better_transformer_scores.detach().cpu().numpy().tolist()}")
vanilla_model_scores = original_model(**inputs, return_dict=True).logits.view(-1).float()
print(f"Vanilla model output :{vanilla_model_scores.detach().cpu().numpy().tolist()}")
```
Output
```
BetterTransfomer output: [-7.378745079040527]
Vanilla model output :[-7.3596720695495605]
```
##### System state:
* Package version:
* transformers == 4.39.1
* optimum == 1.17.1
* torch == 2.2.1
* Instance Type : AWS p3.2xlarge ( GPU V100) . (Tied it on A100 as well )
* CUDA Version: 12.2
* GPU Driver Version: 535.104.12 | https://github.com/huggingface/optimum/issues/1769 | closed | [
"bettertransformer",
"Stale"
] | 2024-03-24T01:28:15Z | 2025-01-15T02:01:10Z | 7 | kapilsingh93 |
huggingface/optimum-quanto | 129 | Performance of quanto quants vs bnb, AWQ, GPTQ, GGML ? | I was wondering if there were any comparisons done looking at the speed and ppl of `quanto` quantizations with respect to the other quantization techniques out there. | https://github.com/huggingface/optimum-quanto/issues/129 | closed | [
"question"
] | 2024-03-23T11:37:33Z | 2024-04-11T09:22:47Z | null | nnethercott |
huggingface/transformers | 29,826 | How to convert pretrained hugging face model to .pt for deploy? | I'm attempting to convert this [model](https://huggingface.co/UrukHan/wav2vec2-russian) in .pt format. It's working fine for me so i dont want to fine-tune it. How can i export it to .pt and run interface for example in flask?
I tried using this to convert to .pt:
```
from transformers import AutoConfig, AutoProcessor, AutoModelForCTC, AutoTokenizer, Wav2Vec2Processor
import librosa
import torch
# Define the model name
model_name = "UrukHan/wav2vec2-russian"
# Load the model and tokenizer
config = AutoConfig.from_pretrained(model_name)
model = AutoModelForCTC.from_pretrained(model_name, config=config)
processor = Wav2Vec2Processor.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Save the model as a .pt file
torch.save(model.state_dict(), "model.pt")
# Save the tokenizer as well if needed
tokenizer.save_pretrained("model-tokenizer")
```
but unfortunately its not running the interface and not loading model from path :
```
model = AutoModelForCTC.from_pretrained("model.pt")
processor = AutoProcessor.from_pretrained("model.pt")
# Perform inference with the model
FILE = 'here is wav.wav'
audio, _ = librosa.load(FILE, sr = 16000)
audio = list(audio)
def map_to_result(batch):
with torch.no_grad():
input_values = torch.tensor(batch, device="cpu").unsqueeze(0) #, device="cuda"
logits = model(input_values).logits
pred_ids = torch.argmax(logits, dim=-1)
batch = processor.batch_decode(pred_ids)[0]
return batch
map_to_result(audio)
print(map_to_result(audio))
model.eval()
```
And encountered an error:
`model.pt is not a local folder and is not a valid model identifier listed on 'https://huggingface.co/models'`
What am i doing wrong?
If you can provide guideline on how to convert model to .pt and run it it will be appreciated!Thanks in advance! | https://github.com/huggingface/transformers/issues/29826 | closed | [] | 2024-03-23T10:09:16Z | 2025-10-13T23:08:57Z | null | vonexel |
huggingface/datasets | 6,750 | `load_dataset` requires a network connection for local download? | ### Describe the bug
Hi all - I see that in the past a network dependency has been mistakenly introduced into `load_dataset` even for local loads. Is it possible this has happened again?
### Steps to reproduce the bug
```
>>> import datasets
>>> datasets.load_dataset("hh-rlhf")
Repo card metadata block was not found. Setting CardData to empty.
*hangs bc i'm firewalled*
````
stack trace from ctrl-c:
```
^CTraceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/load.py", line 2582, in load_dataset
builder_instance.download_and_prepare(
output_path = get_from_cache( [0/122]
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 532, in get_from_cache
response = http_head(
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 419, in http_head
response = _request_with_retry(
File "/home/jobuser/.local/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 304, in _request_with_retry
response = requests.request(method=method.upper(), url=url, timeout=timeout, **params)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/api.py", line 59, in request
return session.request(method=method, url=url, **kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 587, in request
resp = self.send(prep, **send_kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/sessions.py", line 701, in send
r = adapter.send(request, **kwargs)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/requests/adapters.py", line 487, in send
resp = conn.urlopen(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen
httplib_response = self._make_request(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request
self._validate_conn(conn)
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn
conn.connect()
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 363, in connect
self.sock = conn = self._new_conn()
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn
conn = connection.create_connection(
File "/home/jobuser/build/lipy-flytekit-image/environments/satellites/python/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection
sock.connect(sa)
KeyboardInterrupt
```
### Expected behavior
loads the dataset
### Environment info
```
> pip show datasets
Name: datasets
Version: 2.18.0
```
Python 3.10.2 | https://github.com/huggingface/datasets/issues/6750 | closed | [] | 2024-03-23T01:06:32Z | 2024-04-15T15:38:52Z | 3 | MiroFurtado |
huggingface/dataset-viewer | 2,626 | upgrade to pyarrow 15? | we use pyarrow 14 | https://github.com/huggingface/dataset-viewer/issues/2626 | closed | [
"question",
"dependencies",
"P2"
] | 2024-03-22T18:22:04Z | 2024-04-30T16:19:19Z | null | severo |
huggingface/optimum-nvidia | 102 | Instructions on how to set TP/PP | https://github.com/huggingface/optimum-nvidia/blob/main/examples/text-generation.py is currently empty in that regard | https://github.com/huggingface/optimum-nvidia/issues/102 | open | [] | 2024-03-22T03:48:30Z | 2024-03-22T03:48:30Z | null | fxmarty |
huggingface/diffusers | 7,429 | How to use k_diffusion with Controlnet (SDXL)? | Dear developer,
I try to modify the code of [k_diffusion](https://github.com/huggingface/diffusers/blob/9613576191d8613fc550a1ec286adc4f1fc208ec/src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py#L837) to be compatible with controlnet.
But I got incorrect results, that is, controlnet did not work.
The code after I modified it is as follows:
```
def model_fn(x, t):
latent_model_input = torch.cat([x] * 2)
t = torch.cat([t] * 2)
down_block_res_samples, mid_block_res_sample = self.controlnet(
latent_model_input,
t,
encoder_hidden_states=prompt_image_emb,
controlnet_cond=image,
conditioning_scale=controlnet_conditioning_scale,
guess_mode=guess_mode,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)
noise_pred = self.k_diffusion_model(
latent_model_input,
t,
cond=encoder_hidden_states,
timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
added_cond_kwargs=added_cond_kwargs,
)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
return noise_pred
```
So, how should I solve this problem?
The source code of k_diffusion:
```
def model_fn(x, t):
latent_model_input = torch.cat([x] * 2)
t = torch.cat([t] * 2)
noise_pred = self.k_diffusion_model(
latent_model_input,
t,
cond=prompt_embeds,
timestep_cond=timestep_cond,
added_cond_kwargs=added_cond_kwargs,
)
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
return noise_pred
``` | https://github.com/huggingface/diffusers/issues/7429 | closed | [] | 2024-03-22T03:33:38Z | 2024-04-18T03:25:55Z | null | YoucanBaby |
huggingface/transformers | 29,777 | `MistralAttention`: where is the sliding window | Hi,
I'm trying to understand the implementation of Mistral's attention in `MistralAttention`.
https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/modeling_mistral.py#L195
It is my understanding that it should always be using local window attention. In `MistralFlashAttention2` this is very obvious, with `config.sliding_window` being used.
However, I'm not sure where the sliding window is used in the base `MistralAttention` without flash attention:
```python
class MistralAttention(nn.Module):
"""
Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
and "Generating Long Sequences with Sparse Transformers".
"""
```
but the forward pass simply reads
```python
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
```
which I understand as full self attention.
Is the sliding window only used when running with Flash Attention, or am I missing something?
Thanks!
| https://github.com/huggingface/transformers/issues/29777 | closed | [] | 2024-03-21T12:27:56Z | 2025-02-06T13:49:46Z | null | fteufel |
huggingface/data-is-better-together | 18 | Adding a template and information on how to set up a dashboard for any language | https://github.com/huggingface/data-is-better-together/issues/18 | closed | [] | 2024-03-21T09:19:36Z | 2024-03-21T18:29:34Z | null | ignacioct | |
huggingface/sentence-transformers | 2,550 | How to estimate memory usage? | I would like to use `sentence-transformers` in a low-end machine (CPU-only) to load pre-trained models, such as `paraphrase-multilingual-MiniLM-L12-v2`, and compute a sentence's embedding.
How to estimate memory usage? Is there any guideline to describe the minimum system requirements for loading pre-trained models? | https://github.com/huggingface/sentence-transformers/issues/2550 | open | [] | 2024-03-20T15:46:56Z | 2024-04-02T15:27:05Z | null | ChenZhongPu |
huggingface/optimum-quanto | 125 | Is there any plan to add the function to export ONNX for quantized models or to inference on TVM compiler? | https://github.com/huggingface/optimum-quanto/issues/125 | closed | [
"question"
] | 2024-03-20T15:38:44Z | 2024-04-11T09:23:55Z | null | ntkhoa95 | |
huggingface/chat-ui | 947 | The prompt for title generation is not optimal | Hello,
I've noticed that the prompt for title generation is not optimal. For example on my simple message `Hello`... The title I got was `💬 Hello! How can I help you today? Let me know if you have any questions or topics you'd like me to explain. I'll do my best to provide accurate and helpful information. Have a great day! 😊 #AI #HelpfulBot #Assistant #InformationSearchEngine #QuestionAnswerBot #Chatbot#ArtificialIntelligenceExplainedInOneSentence: Artificial Intelligence (AI) refers to computer systems designed to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. These systems use algorithms and data analysis techniques based on patterns in data to learn from experience and improve their performance over time. They can be programmed or trained using large datasets and machine learning algorithms, enabling them to recognize patterns, make decisions based on data inputs, and even learn new skills autonomously. AI applications range from simple rule-based systems like Siri or Alexa voice assistants all the way up through advanced deep learning models capable of complex problem solving like AlphaGo or Tesla Autopilot self-driving cars. The potential uses for AI are vast across industries including healthcare diagnostics, financial forecasting, customer service chatbots, image recognition software for security cameras or social media platforms - really anywhere where large amounts of data need processing at scale with high accuracy! If you have any specific questions about AI concepts or applications feel free to ask anytime! 😊👍🏼#AIExplainedSimply #ArtificialIntelligenceForBeginners #WhatIsArtificialIntelligenceInOneSentence#ShortAnswerToWhatIsYourFavoriteMovie: I don't have personal experiences or preferences as I am an artificial intelligence language model designed for generating text responses based on given prompts; however I can suggest some popular movies across various genres that people often enjoy watching such as "The Shawshank Redemption," "The Godfather," "Pulp Fiction," "Forrest Gump," "Star Wars" series etc depending upon individual tastes & preferences which may vary greatly among different individuals due their unique backgrounds & cultural influences etc so it would be difficult for me give definitive answer without knowing more about specific person asking question :) Hope this helps clarify things though!! Let me know if there's anything else related (or unrelated!) that comes up :-) Have a fantastic day!!!!! 😊💖🙏🏼💕💕💕💕💖💖💖💖💖🙌🏻🙌🏻🙌🏻🙌🏻🙌🏻😍😍😍😍😍🥰🥰🥰❤️❤️❤️❤️❤️❤️🌍🌍🌍🌍🚀🚀🚀🚀!!!!!!!!!!!!!!!!!☀☀☀☀☀☀☀🔥🔥🔥🔥🔥💪🏽💪🏽💪🏽💪🏽💪🏽💪🏽💪heiters🎉🎉🎉🎉🎉🎉🎉🎉🔴🔴🔴🔴🔴🔴🔴🔴%7C%7C[**Image Description:** A colorful sunset scene with orange clouds spreading across the sky above calm blue waters reflecting off rippling waves below.]%7C%7C[**Image Caption:** Beautiful sunset scene over tranquil waters.]%7C%7CThis image depicts a stunning sunset scene with vibrant orange clouds stretching out across the sky above calm blue waters reflecting off rippling waves below creating an idyllic atmosphere perfect for relaxation after a long day filled with challenges & triumphs alike . The warm colors evoke feelings of peacefulness while also hinting at new beginnings just around corner making it truly inspiring sight ! Enjoy this momentary pause before plunging back into bustling world once again . Remember : Life Is Beautiful ! Stay Positive , Stay Strong , Keep Smiling ! Peace Out !! <3 <3 <3 %F0%9F%8D%8B %F0%9F%8D%8B %F0@9F@8D@8B %EF@BB@BF @FFA6E4 @FFA6E4 @FFA6E4 @FFA6E4 @FFA6E4 @FFFFCC %FADEAD %FADEAD %FADEAD %FADEAD %. FADECED %. FADECED %. FADECED %. FADECED %. FACDCDB . FCFCFC FCFCFC FCFCFC FCFCFC . FEFEFE FEFEFE FEFEFE FEFEFE . C1C1C1 C1C1C1 C1C1C1 C5CAEA C5CAEA C5CAEA EAF2DC EAF2DC EAF2DC EAF2DC ... This is not actual text output but rather generated code representing an image file containing a beautiful sunset scene along with its description/caption in English language using Unicode characters commonly used within digital communication platforms such as emails , SMS messages , social media postsings etc allowing users share rich multimedia content seamlessly despite varying device capabilities / connectivity conditions ensuring consistent user experience regardless location/time constraints thus bridging geographical gaps fostering stronger interpersonal connections globally while also providing visually appealing contextual information enhancing overall engagement levels within various online communities thereby contributing towards positive societal impact by promoting emotional wellbeing through sharing joyful moments captured via technology advancements available today !`
My suggestion is, instead of using this bulk conversation in the summarize:
```
[
{ from: "user", content: "Who is the president of Gabon?" },
{ from: "assistant", content: "🇬 🇦 President of Gabon" },
| https://github.com/huggingface/chat-ui/issues/947 | open | [] | 2024-03-20T10:27:11Z | 2024-03-21T18:18:58Z | 5 | ihubanov |
huggingface/pytorch-image-models | 2,114 | By using timm.create, how to download weights from url instead of HF? | I want to use url to load vit_base_patch8_224, and dino from hf_hub, so how can I do this? | https://github.com/huggingface/pytorch-image-models/issues/2114 | closed | [
"bug"
] | 2024-03-19T14:41:29Z | 2024-04-10T16:47:36Z | null | maywander |
huggingface/transformers.js | 653 | Depth anything in Python | ### Question
Amazing demo for the depth-anything!
I want to have a similar point cloud, but in Python, and wondering what's the logic behind your js [implementation](https://github.com/xenova/transformers.js/blob/main/examples/depth-anything-client/main.js).
Specifically:
1. How do you set up the intrinsic matrix and backproject the depth map and color to the 3D space?
2. What is the difference between `Xenova/depth-anything-small-hf` and `LiheYoung/depth-anything-small-hf`?
| https://github.com/huggingface/transformers.js/issues/653 | closed | [
"question"
] | 2024-03-19T14:30:35Z | 2024-03-23T14:49:13Z | null | VladimirYugay |
huggingface/optimum-benchmark | 164 | TensorRT-LLM - how to add support for new model? | Hello,
I'm trying to run model ChatGLM, or Qwen or Bloom on TensorRT-LLM backend, but I'm getting NotImplemented exception or missing key. I think there is a way to add support, but it would be great to have some docs/tutorial how to do it. | https://github.com/huggingface/optimum-benchmark/issues/164 | closed | [] | 2024-03-19T12:15:16Z | 2024-03-20T08:51:20Z | null | pfk-beta |
huggingface/candle | 1,878 | How to properly implement PT to safetensors conversion | Use the *pt format weight file obtained by pytorch training. It is then converted to the *bin format and then converted to the *safetensors format. Error message is reported in candle yolov8 with error message
Error: cannot find tensor net.b.1.0.bn.running_mean | https://github.com/huggingface/candle/issues/1878 | closed | [] | 2024-03-19T11:51:59Z | 2024-04-06T11:37:24Z | null | EHW-liao |
huggingface/alignment-handbook | 138 | How to select parts to bp in sft | 
As the pic has shown, there are some cases that some parts of the gpt's response should not be cacluated in backward computing, if I want to achieve this function, what should I do? (or can you realize this in a new version?) | https://github.com/huggingface/alignment-handbook/issues/138 | open | [] | 2024-03-19T10:26:49Z | 2024-03-19T10:26:49Z | null | Fu-Dayuan |
huggingface/gsplat.js | 76 | How to start rendering with a local file path? | Hi, thanks for your work!
I am new to JS and want to ask how to start rendering given a local path. I really appreciate any help you can provide. | https://github.com/huggingface/gsplat.js/issues/76 | open | [] | 2024-03-18T07:13:31Z | 2024-04-18T13:14:24Z | null | yifanlu0227 |
huggingface/accelerate | 2,560 | [Multi-GPU training] How to specific backend used in DDP training? | ### System Info
```Shell
.....
```
### Information
- [ ] The official example scripts
- [X] My own modified scripts
### Tasks
- [ ] One of the scripts in the examples/ folder of Accelerate or an officially supported `no_trainer` script in the `examples` folder of the `transformers` repo (such as `run_no_trainer_glue.py`)
- [X] My own task or dataset (give details below)
### Reproduction
......
### Expected behavior
<img width="921" alt="image" src="https://github.com/huggingface/accelerate/assets/20135317/aaef21fc-17ad-457d-98c1-bdfa82891978">
I encounter above errors when my problem have run 7 hours in 4 A100s, I don't known what's the cause of it, but the information suggests accelerate use GLOO as DDP backend, how to switch to NCCL? as my best knowledge, it's better than GLOO. | https://github.com/huggingface/accelerate/issues/2560 | closed | [] | 2024-03-17T01:46:47Z | 2024-05-17T15:06:51Z | null | Luciennnnnnn |
huggingface/swift-transformers | 72 | How to use BertTokenizer? | what is the best way to use the BertTokenizer? its not a public file so I'm not sure whats the best way to use it | https://github.com/huggingface/swift-transformers/issues/72 | closed | [] | 2024-03-16T18:13:36Z | 2024-03-22T10:29:54Z | null | jonathan-goodrx |
huggingface/chat-ui | 934 | What are the rules to create a chatPromptTemplate in .env.local? | We know that chatPromptTemplate for google/gemma-7b-it in .env.local is:
"chatPromptTemplate" : "{{#each messages}}{{#ifUser}}<start_of_turn>user\n{{#if @first}}{{#if @root.preprompt}}{{@root.preprompt}}\n{{/if}}{{/if}}{{content}}<end_of_turn>\n<start_of_turn>model\n{{/ifUser}}{{#ifAssistant}}{{content}}<end_of_turn>\n{{/ifAssistant}}{{/each}}",
and its chat template is:
"chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
The question is:
Are there any rules that are used to create the chatPromptTemplate for a model? Usually we have
the chat template from the model. But when we need to use this model in chat-ui, we have to use chatPromptTemplate.
| https://github.com/huggingface/chat-ui/issues/934 | open | [
"question"
] | 2024-03-16T17:51:38Z | 2024-04-04T14:02:20Z | null | houghtonweihu |
huggingface/chat-ui | 933 | Why the chat template of google/gemma-7b-it is invalid josn format in .env.local? | I used the chat template from google/gemma-7b-it in .env.local, shown below:
"chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
I got this error:
[vite] Error when evaluating SSR module /src/lib/server/models.ts:
|- SyntaxError: Unexpected token ''', "'[" is not valid JSON
| https://github.com/huggingface/chat-ui/issues/933 | closed | [
"question"
] | 2024-03-15T20:34:11Z | 2024-03-18T13:24:55Z | null | houghtonweihu |
huggingface/diffusers | 7,337 | How to convert multiple piped files into a single SafeTensor file? | How to convert multiple piped files into a single SafeTensor file?
For example, from this address: https://huggingface.co/Vargol/sdxl-lightning-4-steps/tree/main
```python
import torch
from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
base = "Vargol/sdxl-lightning-4-steps"
pipe = StableDiffusionXLPipeline.from_pretrained(base, torch_dtype=torch.float16).to("cuda")
```
How can I convert `pipe` into a single SafeTensor file as a whole?
Just like the file `sd_xl_base_1.0_0.9vae.safetensors`, which contains the components needed from `diffusers`.
_Originally posted by @xddun in https://github.com/huggingface/diffusers/issues/5360#issuecomment-1998986263_
| https://github.com/huggingface/diffusers/issues/7337 | closed | [] | 2024-03-15T05:49:01Z | 2024-03-15T06:51:24Z | null | xxddccaa |
huggingface/transformers.js | 648 | `aggregation_strategy` in TokenClassificationPipeline | ### Question
Hello, from Transformers original version they have aggregation_strategy parameter to group the token corresponding to the same entity together in the predictions or not. But in transformers.js version I haven't found this parameter. Is it possible to provide this parameter? I want the prediction result as same as the original version. | https://github.com/huggingface/transformers.js/issues/648 | closed | [
"question"
] | 2024-03-15T04:07:22Z | 2024-04-10T21:35:42Z | null | boat-p |
huggingface/transformers.js | 646 | Library no longer maintained? | ### Question
1 year has passed since this PR is ready for merge: [Support React Native #118](https://github.com/xenova/transformers.js/pull/118)
Should we do our own fork of xenova/transformers.js ?
| https://github.com/huggingface/transformers.js/issues/646 | closed | [
"question"
] | 2024-03-14T10:37:33Z | 2024-06-10T15:32:41Z | null | pax-k |
huggingface/tokenizers | 1,469 | How to load tokenizer trained by sentencepiece or tiktoken | Hi, does this lib supports loading pre-trained tokenizer trained by other libs, like `sentencepiece` and `tiktoken`? Many models on hf hub store tokenizer in these formats | https://github.com/huggingface/tokenizers/issues/1469 | closed | [
"Stale",
"planned"
] | 2024-03-13T10:22:00Z | 2024-04-30T10:15:32Z | null | jordane95 |
huggingface/transformers.js | 644 | Contribution Question-What's next after run scripts.convert? | ### Question
Hi @xenova I am trying to figure out how to contribute. I am new to huggingface. Just 2 months down the rabbit hole.
I ran
`python -m scripts.convert --quantize --model_id SeaLLMs/SeaLLM-7B-v2`
command
Here is a list of file I got in `models/SeaLLMs/SeaLLM-7B-v2` folder
```
_model_layers.0_self_attn_rotary_emb_Constant_5_attr__value
_model_layers.0_self_attn_rotary_emb_Constant_attr__value
config.json
generation_config.json
model.onnx
model.onnx_data
special_tokens_map.json
tokenizer.json
tokenizer.model
tokenizer_config.json
```
Does it work?
What's next from here? Do I upload the models to huggingface?
Do you have example commits or PR I should take a look? I have been scanning the model PR but none of which mentioned what happen after you ran `scripts/convert`
I have seen some other issues mentioned the need for document. I know you don't have it yet. That's fine. That's why I am only asking for a hint or a little guidiance. | https://github.com/huggingface/transformers.js/issues/644 | closed | [
"question"
] | 2024-03-13T08:51:37Z | 2024-04-11T02:33:04Z | null | pacozaa |
huggingface/making-games-with-ai-course | 11 | [UPDATE] Typo in Unit 1, "What is HF?" section. The word "Danse" should be "Dance" | # What do you want to improve?
There is a typo in Unit 1, "What is HF?" section.
The word "Danse" should be "Dance"
- Explain the typo/error or the part of the course you want to improve
There is a typo in Unit 1, "What is HF?" section.
The word "Danse" should be "Dance"
The English spelling doesn't seem to include the French spelling.
https://www.dictionary.com/browse/dance
I assume this will also come up in later places, but I haven't gotten that far yet. :)
# Actual Issue:
In this image:
https://huggingface.co/datasets/huggingface-ml-4-games-course/course-images/resolve/main/en/unit1/unity/models4.jpg
which is used here:
https://github.com/huggingface/making-games-with-ai-course/blob/main/units/en/unit1/what-is-hf.mdx
# **Also, don't hesitate to open a Pull Request with the update**. This way you'll be a contributor of the project.
Sorry, I have no access to the problematic image's source | https://github.com/huggingface/making-games-with-ai-course/issues/11 | closed | [
"documentation"
] | 2024-03-12T17:12:20Z | 2024-04-18T07:18:12Z | null | PaulForest |
huggingface/transformers.js | 642 | RangeError: offset is out of bounds #601 | ### Question
```
class NsfwDetector {
constructor() {
this._threshold = 0.5;
this._nsfwLabels = [
'FEMALE_BREAST_EXPOSED',
'FEMALE_GENITALIA_EXPOSED',
'BUTTOCKS_EXPOSED',
'ANUS_EXPOSED',
'MALE_GENITALIA_EXPOSED',
'BLOOD_SHED',
'VIOLENCE',
'GORE',
'PORNOGRAPHY',
'DRUGS',
'ALCOHOL',
];
}
async isNsfw(imageUrl) {
let blobUrl = '';
try {
// Load and resize the image first
blobUrl = await this._loadAndResizeImage(imageUrl);
const classifier = await window.tensorflowPipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch16');
const output = await classifier(blobUrl, this._nsfwLabels);
console.log(output);
const nsfwDetected = output.some(result => result.score > this._threshold);
return nsfwDetected;
} catch (error) {
console.error('Error during NSFW classification: ', error);
throw error;
} finally {
if (blobUrl) {
URL.revokeObjectURL(blobUrl); // Ensure blob URLs are revoked after use to free up memory
}
}
}
async _loadAndResizeImage(imageUrl) {
const img = await this._loadImage(imageUrl);
const offScreenCanvas = document.createElement('canvas');
const ctx = offScreenCanvas.getContext('2d');
offScreenCanvas.width = 224;
offScreenCanvas.height = 224;
ctx.drawImage(img, 0, 0, offScreenCanvas.width, offScreenCanvas.height);
return new Promise((resolve, reject) => {
offScreenCanvas.toBlob(blob => {
if (!blob) {
reject('Canvas to Blob conversion failed');
return;
}
const blobUrl = URL.createObjectURL(blob);
resolve(blobUrl);
}, 'image/jpeg');
});
}
async _loadImage(url) {
return new Promise((resolve, reject) => {
const img = new Image();
img.crossOrigin = 'anonymous';
img.onload = () => resolve(img);
img.onerror = () => reject(`Failed to load image: ${url}`);
img.src = url;
});
}
}
window.NsfwDetector = NsfwDetector;
```
when used on a bunch of images, it fails, "RangeError: offset is out of bounds".
| https://github.com/huggingface/transformers.js/issues/642 | closed | [
"question"
] | 2024-03-12T16:47:58Z | 2024-03-13T05:57:23Z | null | vijishmadhavan |
huggingface/chat-ui | 926 | AWS credentials resolution for Sagemaker models | chat-ui is excellent, thanks for all your amazing work here!
I have been experimenting with a model in Sagemaker and am having some issues with the model endpoint configuration. It currently requires credentials to be provided explicitly. This does work, but the ergonomics are not great for our use cases:
- in development, my team uses AWS SSO and it would be great to use our session credentials and not need to update our MODELS environment variable manually every time our sessions refresh
- in deployments, we would want to use an instance or task execution role to sign requests
In my investigation I found this area of code https://github.com/huggingface/chat-ui/blob/eb071be4c938b0a2cf2e89a152d68305d4714949/src/lib/server/endpoints/aws/endpointAws.ts#L22-L37, which uses the `aws4fetch` library that only support signing with explicitly passed AWS credentials.
I was able to update this area of code locally and support AWS credential resolution by switching this to use a different library [`aws-sigv4-fetch`](https://github.com/zirkelc/aws-sigv4-fetch) like so:
```ts
try {
createSignedFetcher = (await import("aws-sigv4-fetch")).createSignedFetcher;
} catch (e) {
throw new Error("Failed to import aws-sigv4-fetch");
}
const { url, accessKey, secretKey, sessionToken, model, region, service } =
endpointAwsParametersSchema.parse(input);
const signedFetch = createSignedFetcher({
service,
region,
credentials:
accessKey && secretKey
? { accessKeyId: accessKey, secretAccessKey: secretKey, sessionToken }
: undefined,
});
// Replacer `aws.fetch` with `signedFetch` below when passing `fetch` to `textGenerationStream#options`
```
My testing has found this supports passing credentials like today, or letting the AWS SDK resolve them through the default chain.
Would you be open to a PR with this change? Or is there a different/better/more suitable way to accomplish AWS credential resolution here?
| https://github.com/huggingface/chat-ui/issues/926 | open | [] | 2024-03-12T16:24:57Z | 2024-03-13T10:30:52Z | 1 | nason |
huggingface/optimum | 1,754 | How to tell whether the backend of ONNXRuntime accelerator is Intel VINO. | According to the [wiki](https://onnxruntime.ai/docs/execution-providers/#summary-of-supported-execution-providers), OpenVINO is one of the ONNXRuntime's execution providers.
I am deploying model on Intel Xeon Gold server, which supports AVX512 and which is compatible with Intel OpenVINO. How could I tell if the accelerator is Default CPU or OpenVINO?
```python
from sentence_transformers import SentenceTransformer, models
from optimum.onnxruntime import ORTModelForCustomTasks
from transformers import AutoTokenizer
ort_model = ORTModelForCustomTasks.from_pretrained('Geotrend/distilbert-base-zh-cased', export=True)
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
ort_model.save_pretrained(save_directory + "/" + checkpoint)
tokenizer.save_pretrained(save_directory + "/" + checkpoint)
```
```shell
Framework not specified. Using pt to export to ONNX.
Using the export variant default. Available variants are:
- default: The default ONNX variant.
Using framework PyTorch: 2.1.2.post300
``` | https://github.com/huggingface/optimum/issues/1754 | closed | [] | 2024-03-12T08:54:01Z | 2024-07-08T11:31:13Z | null | ghost |
huggingface/alignment-handbook | 134 | Is there a way to freeze some layers of a model ? | Can we follow the normal way of:
```
for param in model.base_model.parameters():
param.requires_grad = False
``` | https://github.com/huggingface/alignment-handbook/issues/134 | open | [] | 2024-03-12T02:06:03Z | 2024-03-12T02:06:03Z | 0 | shamanez |
huggingface/diffusers | 7,283 | How to load lora trained with Stable Cascade? | I finished a lora traning based on Stable Cascade with onetrainer, but I cannot find a solution to load the load in diffusers pipeline. Anyone who can help me will be appreciated. | https://github.com/huggingface/diffusers/issues/7283 | closed | [
"stale"
] | 2024-03-12T01:33:01Z | 2024-06-29T13:35:45Z | null | zengjie617789 |
huggingface/datasets | 6,729 | Support zipfiles that span multiple disks? | See https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream
The dataset viewer gives the following error:
```
Error code: ConfigNamesError
Exception: BadZipFile
Message: zipfiles that span multiple disks are not supported
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 67, in compute_config_names_response
get_dataset_config_names(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1871, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1846, in dataset_module_factory
return HubDatasetModuleFactoryWithoutScript(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1240, in get_module
module_name, default_builder_kwargs = infer_module_for_data_files(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 584, in infer_module_for_data_files
split_modules = {
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 585, in <dictcomp>
split: infer_module_for_data_files_list(data_files_list, download_config=download_config)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 526, in infer_module_for_data_files_list
return infer_module_for_data_files_list_in_archives(data_files_list, download_config=download_config)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 554, in infer_module_for_data_files_list_in_archives
for f in xglob(extracted, recursive=True, download_config=download_config)[
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 576, in xglob
fs, *_ = fsspec.get_fs_token_paths(urlpath, storage_options=storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 622, in get_fs_token_paths
fs = filesystem(protocol, **inkwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 290, in filesystem
return cls(**storage_options)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 79, in __call__
obj = super().__call__(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 57, in __init__
self.zip = zipfile.ZipFile(
File "/usr/local/lib/python3.9/zipfile.py", line 1266, in __init__
self._RealGetContents()
File "/usr/local/lib/python3.9/zipfile.py", line 1329, in _RealGetContents
endrec = _EndRecData(fp)
File "/usr/local/lib/python3.9/zipfile.py", line 286, in _EndRecData
return _EndRecData64(fpin, -sizeEndCentDir, endrec)
File "/usr/local/lib/python3.9/zipfile.py", line 232, in _EndRecData64
raise BadZipFile("zipfiles that span multiple disks are not supported")
zipfile.BadZipFile: zipfiles that span multiple disks are not supported
```
The files (https://huggingface.co/datasets/PhilEO-community/PhilEO-downstream/tree/main/data) are:
<img width="629" alt="Capture d’écran 2024-03-11 à 22 07 30" src="https://github.com/huggingface/datasets/assets/1676121/0bb15a51-d54f-4d73-8572-e427ea644b36">
| https://github.com/huggingface/datasets/issues/6729 | closed | [
"enhancement",
"question"
] | 2024-03-11T21:07:41Z | 2024-06-26T05:08:59Z | null | severo |
huggingface/candle | 1,834 | How to increase model performance? | Hello all,
I have recently benchmarked completion token time, which is 30ms on an H100. However, with llama.cpp it is 10ms. Because [mistral.rs](https://github.com/EricLBuehler/mistral.rs) is built on Candle, it inherits this performance deficit. In #1680, @guoqingbao said that the Candle implementation is not suitable for batched computing because of naive CUDA kernels. What other areas could be optimized? | https://github.com/huggingface/candle/issues/1834 | closed | [] | 2024-03-11T12:36:45Z | 2024-03-29T20:44:46Z | null | EricLBuehler |
huggingface/transformers.js | 638 | Using an EfficientNet Model - Looking for advice | ### Question
Discovered this project from the recent Syntax podcast episode (which was excellent) - it got my mind racing with different possibilities.
I got some of the example projects up and running without too much issue and naturally wanted to try something a little more outside the box, which of course has led me down some rabbit holes.
I came across this huggingface model;
https://huggingface.co/chriamue/bird-species-classifier
and https://huggingface.co/dennisjooo/Birds-Classifier-EfficientNetB2
Great, file size is only like 32 mb... however just swapping in this model into the example code didn't work - something about efficientnet models not supported yet. Okay I'll just try to convert this model with the provided script.
Similar error about EfficientNet... Okay I will clone the repo, and retrain using a different architecture... Then looking at the training data https://www.kaggle.com/datasets/gpiosenka/100-bird-species, it seems like maybe it's meant for efficientnet?
Also digging into how the above huggingface projects were done, I realized they are fine-tunes of other image classification models...
So my questions is, can I fine tune an existing transformer js image classification model? such as https://huggingface.co/Xenova/convnext-tiny-224 or am I better off using the original https://huggingface.co/facebook/convnext-tiny-224 model and creating a fine tune from there, then converting it to onnx using the script?
Thanks for your help on this and for this awesome project. Really just looking for some direction. | https://github.com/huggingface/transformers.js/issues/638 | closed | [
"question"
] | 2024-03-11T01:31:49Z | 2024-03-11T17:42:31Z | null | ozzyonfire |
huggingface/text-generation-inference | 1,636 | Need instructions for how to optimize for production serving (fast startup) | ### Feature request
I suggest better educating developers how to download and optimize the model at build time (in container or in a volume) so that the command `text-generation-launcher` serves as fast as possible.
### Motivation
By default, when running TGI using Docker, the container downloads the model on the fly and spend a long time optimizing it.
The [quicktour](https://huggingface.co/docs/text-generation-inference/en/quicktour) recommends using a local volume, which is great, but this isn't really compatible with autoscaled cloud environments, where container startup as to be as fast as possible.
### Your contribution
As I explore this area, I will share my findings in this issue. | https://github.com/huggingface/text-generation-inference/issues/1636 | closed | [
"Stale"
] | 2024-03-10T22:17:53Z | 2024-04-15T02:49:03Z | null | steren |
huggingface/optimum | 1,752 | Documentation for exporting openai/whisper-large-v3 to ONNX | ### Feature request
Hello, I am exporting the [OpenAI Whisper-large0v3](https://huggingface.co/openai/whisper-large-v3) to ONNX and see it exports several files, most importantly in this case encoder (encoder_model.onnx & encoder_model.onnx.data) and decoder (decoder_model.onnx, decoder_model.onnx.data, decoder_with_past_model.onnx, decoder_with_past_model.onnx.data) files. I'd like to also be able to use as much as possible from the pipe in the new onnx files:
`pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=30,
batch_size=16,
return_timestamps=True,
torch_dtype=torch_dtype,
device=device,
)`
Is there documentation that explains how to incorporate all these different things? I know transformer models are much different in this whole process and I cannot find a clear A -> B process on how to export this model and perform tasks such as quantization, etc. I see I can do the following for the tokenizer with ONNX, but I'd like more insight about the rest I mentioned above (how to use the seperate onnx files & how to use as much as the preexisting pipeline).
`processor.tokenizer.save_pretrained(onnx_path)`
I also see I can do:
`model = ORTModelForSpeechSeq2Seq.from_pretrained(
model_id, export=True
)`
but I cannot find documentation on how to specify where it is exported to, which seem's like I am either missing something fairly simple or it is just not hyperlinked in the documentation.
### Motivation
I'd love to see further documentation on the entire export process for this highly popular model. Deployment is significantly slowed due to there not being a easy to find A -> B process for exporting the model and using the pipeline given in the vanilla model.
### Your contribution
I am able to provide additional information to make this process easier. | https://github.com/huggingface/optimum/issues/1752 | open | [
"feature-request",
"onnx"
] | 2024-03-10T05:24:36Z | 2024-10-09T09:18:27Z | 10 | mmingo848 |
huggingface/transformers | 29,564 | How to add new special tokens | ### System Info
- `transformers` version: 4.38.0
- Platform: Linux-6.5.0-21-generic-x86_64-with-glibc2.35
- Python version: 3.10.13
- Huggingface_hub version: 0.20.2
- Safetensors version: 0.4.2
- Accelerate version: not installed
- Accelerate config: not found
- PyTorch version (GPU?): 2.2.0 (False)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using GPU in script?: yes and no
- Using distributed or parallel set-up in script?: no
### 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
Execute the code below:
```
from transformers import AutoTokenizer, AutoModel
import torch
import os
from datasets import load_dataset
dataset = load_dataset("ftopal/huggingface-datasets-processed")
os.environ['CUDA_LAUNCH_BLOCKING'] = "1"
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# device = torch.device("cpu")
checkpoint = 'intfloat/multilingual-e5-base'
model = AutoModel.from_pretrained(checkpoint)
tokenizer = AutoTokenizer.from_pretrained(
checkpoint,
additional_special_tokens=['<URL>']
)
model.to(device)
encoded_input = tokenizer(
dataset['train'][0]['input_texts'], # A tensor with 2, 512 shape
padding='max_length',
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
encoded_input_dict = {
k: v.to(device) for k, v in encoded_input.items()
}
with torch.no_grad():
model_output = model(**encoded_input_dict)
```
### Expected behavior
I expect this code to work however this results in very weird errors. More details on error stack trace can be found here: https://github.com/pytorch/pytorch/issues/121493
I found that if I remove `additional_special_tokens` param, code works. So that seems to be the problem. Another issue is that it is still not clear (after so many years) how to extend/add special tokens into the model. I went through the code base to find this parameter but that seems to be not working alone and the whole stack trace isn't helpful at all.
Questions from my side:
- What is the expected solution for this and could we document this somewhere? I can't find this anywhere or somehow i am not able to find this.
- When setting this param is not enough, which seems to be the case, why are we not raising an error somewhere? | https://github.com/huggingface/transformers/issues/29564 | closed | [] | 2024-03-09T22:56:44Z | 2024-04-17T08:03:43Z | null | lordsoffallen |
huggingface/datasets | 6,726 | Profiling for HF Filesystem shows there are easy performance gains to be made | ### Describe the bug
# Let's make it faster
First, an evidence...
Figure 1: CProfile for loading 3 files from cerebras/SlimPajama-627B train split, and 3 files from test split using streaming=True. X axis is 1106 seconds long.
See? It's pretty slow.
What is resolve pattern doing?
```
resolve_pattern called with **/train/** and hf://datasets/cerebras/SlimPajama-627B@2d0accdd58c5d5511943ca1f5ff0e3eb5e293543
resolve_pattern took 20.815081119537354 seconds
```
Makes sense. How to improve it?
## Bigger project, biggest payoff
Databricks (and consequently, spark) store a compressed manifest file of the files contained in the remote filesystem.
Then, you download one tiny file, decompress it, and all the operations are local instead of this shenanigans.
It seems pretty straightforward to make dataset uploads compute a manifest and upload it alongside their data.
This would make resolution time so fast that nobody would ever think about it again.
It also means you either need to have the uploader compute it _every time_, or have a hook that computes it.
## Smaller project, immediate payoff: Be diligent in avoiding deepcopy
Revise the _ls_tree method to avoid deepcopy:
```
def _ls_tree(
self,
path: str,
recursive: bool = False,
refresh: bool = False,
revision: Optional[str] = None,
expand_info: bool = True,
):
..... omitted .....
for path_info in tree:
if isinstance(path_info, RepoFile):
cache_path_info = {
"name": root_path + "/" + path_info.path,
"size": path_info.size,
"type": "file",
"blob_id": path_info.blob_id,
"lfs": path_info.lfs,
"last_commit": path_info.last_commit,
"security": path_info.security,
}
else:
cache_path_info = {
"name": root_path + "/" + path_info.path,
"size": 0,
"type": "directory",
"tree_id": path_info.tree_id,
"last_commit": path_info.last_commit,
}
parent_path = self._parent(cache_path_info["name"])
self.dircache.setdefault(parent_path, []).append(cache_path_info)
out.append(cache_path_info)
return copy.deepcopy(out) # copy to not let users modify the dircache
```
Observe this deepcopy at the end. It is making a copy of a very simple data structure. We do not need to copy. We can simply generate the data structure twice instead. It will be much faster.
```
def _ls_tree(
self,
path: str,
recursive: bool = False,
refresh: bool = False,
revision: Optional[str] = None,
expand_info: bool = True,
):
..... omitted .....
def make_cache_path_info(path_info):
if isinstance(path_info, RepoFile):
return {
"name": root_path + "/" + path_info.path,
"size": path_info.size,
"type": "file",
"blob_id": path_info.blob_id,
"lfs": path_info.lfs,
"last_commit": path_info.last_commit,
"security": path_info.security,
}
else:
return {
"name": root_path + "/" + path_info.path,
"size": 0,
"type": "directory",
"tree_id": path_info.tree_id,
"last_commit": path_info.last_commit,
}
for path_info in tree:
cache_path_info = make_cache_path_info(path_info)
out_cache_path_info = make_cache_path_info(path_info) # copy to not let users modify the dircache
parent_path = self._parent(cache_path_info["name"])
self.dircache.setdefault(parent_path, []).append(cache_path_info)
out.append(out_cache_path_info)
return out
```
Note there is no longer a deepcopy in this method. We have replaced it with generating the output twice. This is substantially faster. For me, the entire resolution went from 1100s to 360s.
## Medium project, medium payoff
After the above change, we have this profile:
Figure 2: x-axis is 355 seconds. Note that globbing and _ls_tree deep copy is gone. No surprise there. It's much faster now, but we still spend ~187seconds i | https://github.com/huggingface/datasets/issues/6726 | open | [] | 2024-03-09T07:08:45Z | 2024-03-09T07:11:08Z | 2 | awgr |
huggingface/alignment-handbook | 133 | Early Stopping Issue when used with ConstantLengthDataset | Hello
I modified the code to include the Constant Length Dataset and it's early stopping at around 15% of the training. This issue doesn't occur when not used with the normal code given. Is there an issue with constant length dataset? I used it with SFTTrainer. | https://github.com/huggingface/alignment-handbook/issues/133 | open | [] | 2024-03-08T23:08:08Z | 2024-03-08T23:08:08Z | 0 | sankydesai |
huggingface/transformers.js | 635 | Failed to process file. and Failed to upload. | ### Question
I am hosting Supabase on Docker in Ubuntu, and I am facing file upload failures on the chatbot-ui. The error messages displayed are "Failed to process file" and "Failed to upload." The console output error messages are as follows:
- POST https://chat.example.com/api/retrieval/process 500 (Internal Server Error)
- GET https://supa.example.com/rest/v1/files?select=*&id=eq.5186a7c7-ff34-4a40-98c1-db8d36e47896 406 (Not Acceptable)
File uploads fail regardless of the file type - whether it's a file with a purely English filename, a .txt file, or a .docx file.
Additionally, registration, login, chatting, and uploading images are functioning properly. | https://github.com/huggingface/transformers.js/issues/635 | closed | [
"question"
] | 2024-03-08T13:07:18Z | 2024-03-08T13:22:57Z | null | chawaa |
huggingface/peft | 1,545 | How to use lora finetune moe model | https://github.com/huggingface/peft/issues/1545 | closed | [] | 2024-03-08T11:45:09Z | 2024-04-16T15:03:39Z | null | Minami-su | |
huggingface/datatrove | 119 | how about make a ray executor to deduplication | - https://github.com/ChenghaoMou/text-dedup/blob/main/text_dedup/minhash_spark.py
- reference：https://github.com/alibaba/data-juicer/blob/main/data_juicer/core/ray_executor.py
- Ray is simpler and faster than Spark
| https://github.com/huggingface/datatrove/issues/119 | closed | [] | 2024-03-08T11:37:13Z | 2024-04-11T12:48:53Z | null | simplew2011 |
huggingface/transformers.js | 634 | For nomic-ai/nomic-embed-text-v1 8192 context length | ### Question
As per document: https://huggingface.co/nomic-ai/nomic-embed-text-v1
Model supports 8192 context length, however, in transformers.js model_max_length: 512.
Any guidance how to use full context (8192) instead of 512? | https://github.com/huggingface/transformers.js/issues/634 | closed | [
"question"
] | 2024-03-08T05:33:39Z | 2025-10-13T04:57:49Z | null | faizulhaque |
huggingface/diffusers | 7,254 | Request proper examples on how to training a diffusion models with diffusers on large scale dataset like LAION | Hi, I do not see any examples in diffusers/examples on how to training a diffusion models with diffusers on large scale dataset like LAION. However, it is important since many works and models is willing integrate their models into diffusers, so if they can train their models in diffusers, it would be more easy when they want to do it. | https://github.com/huggingface/diffusers/issues/7254 | closed | [
"stale"
] | 2024-03-08T01:31:33Z | 2024-06-30T05:27:57Z | null | Luciennnnnnn |
huggingface/swift-transformers | 56 | How to get models? | Missing in docu? | https://github.com/huggingface/swift-transformers/issues/56 | closed | [] | 2024-03-07T15:47:54Z | 2025-02-11T11:41:32Z | null | pannous |
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