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/diffusers | 6,804 | How to only offload some parts but not whole model into cpu? | Using enable_cpu_offload() will offload the whole model into cpu, which can occupy a large part of cpu memory. How can I just offload a part of model into cpu? | https://github.com/huggingface/diffusers/issues/6804 | closed | [] | 2024-02-01T07:43:04Z | 2024-02-02T04:59:43Z | null | blx0102 |
huggingface/transformers.js | 553 | How to convert BAAI/bge-m3 for Transformers.js? | ### Question
I tried to convert https://huggingface.co/BAAI/bge-m3 to ONNX using the instructions at https://github.com/xenova/transformers.js?tab=readme-ov-file#convert-your-models-to-onnx but I'm getting errors.
```shell
$ python -m scripts.convert --model_id BAAI/bge-m3
Framework not specified. Using pt to export to ONNX.
Automatic task detection to feature-extraction (possible synonyms are: default, mask-generation, sentence-similarity).
Using the export variant default. Available variants are:
- default: The default ONNX variant.
Using framework PyTorch: 2.0.1
Overriding 1 configuration item(s)
- use_cache -> False
================ Diagnostic Run torch.onnx.export version 2.0.1 ================
verbose: False, log level: Level.ERROR
======================= 0 NONE 0 NOTE 0 WARNING 0 ERROR ========================
Saving external data to one file...
Post-processing the exported models...
Deduplicating shared (tied) weights...
Validating ONNX model models/BAAI/bge-m3/model.onnx...
-[✓] ONNX model output names match reference model (last_hidden_state)
- Validating ONNX Model output "last_hidden_state":
-[✓] (2, 16, 1024) matches (2, 16, 1024)
-[✓] all values close (atol: 0.0001)
The ONNX export succeeded and the exported model was saved at: models/BAAI/bge-m3
```
```shell
cat test.js
```
```js
import { pipeline } from './src/transformers.js'
const extractor = await pipeline('feature-extraction', 'BAAI/bge-m3', {
quantized: false,
cache_dir: './models',
local_files_only: true,
})
const embedding = await extractor('hello there', { pooling: 'mean', normalize: true })
console.log(JSON.stringify(Array.from(embedding.data), null, 2))
```
```shell
2024-01-31 20:35:16.548 node[64946:11650151] 2024-01-31 20:35:16.548343 [E:onnxruntime:, inference_session.cc:1532 operator()] Exception during initialization: /Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.cc:31 onnxruntime::Initializer::Initializer(const onnx::TensorProto &, const onnxruntime::Path &) !model_path.IsEmpty() was false. model_path must not be empty. Ensure that a path is provided when the model is created or loaded.
Error: Exception during initialization: /Users/runner/work/1/s/onnxruntime/core/optimizer/initializer.cc:31 onnxruntime::Initializer::Initializer(const onnx::TensorProto &, const onnxruntime::Path &) !model_path.IsEmpty() was false. model_path must not be empty. Ensure that a path is provided when the model is created or loaded.
at new OnnxruntimeSessionHandler (***/transformers.js/node_modules/onnxruntime-node/dist/backend.js:27:92)
at ***/transformers.js/node_modules/onnxruntime-node/dist/backend.js:64:29
at process.processTicksAndRejections (node:internal/process/task_queues:77:11)
Something went wrong during model construction (most likely a missing operation). Using `wasm` as a fallback.
Aborted(Error: ENOENT: no such file or directory, open '***/transformers.js/dist/ort-wasm-simd-threaded.wasm')
failed to asynchronously prepare wasm: RuntimeError: Aborted(Error: ENOENT: no such file or directory, open '***/transformers.js/dist/ort-wasm-simd-threaded.wasm'). Build with -sASSERTIONS for more info.
Aborted(RuntimeError: Aborted(Error: ENOENT: no such file or directory, open '***/transformers.js/dist/ort-wasm-simd-threaded.wasm'). Build with -sASSERTIONS for more info.)
***/transformers.js/node_modules/onnxruntime-web/dist/ort-web.node.js:6
...
...
...
Error: no available backend found. ERR: [wasm] RuntimeError: Aborted(Error: ENOENT: no such file or directory, open '***/transformers.js/dist/ort-wasm-simd-threaded.wasm'). Build with -sASSERTIONS for more info.
at ***/transformers.js/node_modules/onnxruntime-common/dist/ort-common.node.js:6:11822
at process.processTicksAndRejections (node:internal/process/task_queues:95:5)
at async m.create (***/transformers.js/node_modules/onnxruntime-common/dist/ort-common.node.js:6:11480)
at async constructSession (file://***/transformers.js/src/models.js:140:16)
at async Promise.all (index 1)
at async XLMRobertaModel.from_pretrained (file://***/transformers.js/src/models.js:793:20)
at async AutoModel.from_pretrained (file://***/transformers.js/src/models.js:5166:20)
at async Promise.all (index 1)
at async loadItems (file://***/transformers.js/src/pipelines.js:3116:5)
at async pipeline (file://***/transformers.js/src/pipelines.js:3056:21)
Node.js v20.9.0
``` | https://github.com/huggingface/transformers.js/issues/553 | closed | [
"question"
] | 2024-02-01T01:40:02Z | 2024-02-08T22:17:29Z | null | devfacet |
huggingface/diffusers | 6,785 | How to finetune stable diffusion img2img(like instructpix2pix or controlnet) model with only one input channel? | Hello, experts!
I want to finetune stable diffusion img2img(like instructpix2pix or controlnet) model with only one input channel or greyscale image? I saw official docs says it is ok to increase the input channel from 4 to 9, but I want to know that is this ok to decrease the input channel to be one for finetuning?
Thanks in advance! | https://github.com/huggingface/diffusers/issues/6785 | closed | [] | 2024-01-31T09:17:56Z | 2024-01-31T09:27:43Z | null | sapkun |
huggingface/accelerate | 2,399 | How to use vscode to debug the acceleration program with breakpoints? I checked a lot of information, but still didn't find a solution | How to use vscode to debug the acceleration program with breakpoints? I checked a lot of information, but still didn't find a solution
| https://github.com/huggingface/accelerate/issues/2399 | closed | [] | 2024-01-31T09:00:32Z | 2024-03-10T15:05:56Z | null | kejia1 |
huggingface/datatrove | 72 | Tokenization in Minhash deduplication | Hi,
I have noticed that the tokenization is different from those adopted by previous papers.
For example, this [paper](https://arxiv.org/abs/2107.06499) uses space tokenization, [refinedweb](https://arxiv.org/abs/2306.01116) states that they used GPT-2 tokenizer, while datatrove adopts nltk to extract n-grams.
I'm wondering whether the results obtained by different tokenization methods are consistent. | https://github.com/huggingface/datatrove/issues/72 | closed | [
"question"
] | 2024-01-31T02:33:17Z | 2024-02-01T15:36:24Z | null | jordane95 |
huggingface/peft | 1,419 | How to torch.jit.trace a peft model | ### Feature request
Need an example of how to trace a peft model.
### Motivation
Hi, I'm trying to deploy a Lora-finetuned llama model on Nvidia Triton server. For that I need to `traced_model = torch.jit.trace(model, model_input_dict, strict=False)`, however I encountered issues like `Tracing failed sanity checks! ERROR: Graphs differed across invocations!`
and terminal output was like:
```
/python3.10/site-packages/transformers/models/llama/modeling_llama.py:598: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if input_shape[-1] > 1:
/python3.10/site-packages/bitsandbytes/autograd/_functions.py:300: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if prod(A.shape) == 0:
/python3.10/site-packages/bitsandbytes/autograd/_functions.py:322: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization
warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
/python3.10/site-packages/bitsandbytes/functional.py:2016: TracerWarning: Converting a tensor to a Python number might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
nnz = nnz_row_ptr[-1].item()
/python3.10/site-packages/bitsandbytes/functional.py:1714: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
assert prod(list(shapeA)) > 0, f'Input tensor dimensions need to be > 0: {shapeA}'
/python3.10/site-packages/bitsandbytes/functional.py:1717: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if shapeA[0] == 0 and dimsA == 2:
/python3.10/site-packages/bitsandbytes/functional.py:1719: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
elif shapeA[1] == 0 and dimsA == 3:
/python3.10/site-packages/bitsandbytes/functional.py:1741: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
shapeA[-1] == shapeB[-1]
/python3.10/site-packages/bitsandbytes/functional.py:1826: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
new_row_stats.shape[0] == row_stats.shape[0]
/python3.10/site-packages/bitsandbytes/functional.py:1829: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
new_col_stats.shape[0] == col_stats.shape[0]
/python3.10/site-packages/transformers/models/llama/modeling_llama.py:120: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if seq_len > self.max_seq_len_cached:
/python3.10/site-packages/transformers/models/llama/modeling_llama.py:350: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
/python3.10/site-packages/transformers/models/llama/modeling_llama.py:357: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python value | https://github.com/huggingface/peft/issues/1419 | closed | [] | 2024-01-30T22:56:10Z | 2024-02-06T09:16:07Z | null | dcy0577 |
huggingface/gsplat.js | 56 | how to change the camera clipping - and a feature request: add rotate control | Hello and thank you for your great work!
I am a coding noob but managed to use the jsfiddle example to set up a page on which I can display my splats.
Is it possible to change the clipping (and other) settings for the camera? If so, where should I look??
And for the request; never mind, I was not paying attention
Thanks again!! | https://github.com/huggingface/gsplat.js/issues/56 | closed | [] | 2024-01-30T19:20:35Z | 2024-01-31T16:51:30Z | null | murcje |
huggingface/accelerate | 2,395 | Question: how to apply device map to a paired model | Hello everybody,
I have been experimenting with Mistral models and have written a small second model to be paired with it. However, I have a machine with 2 GPUs and would like to use both. I am aware that the parallelization `accelerate` uses is based on splitting the data by batches. How can I apply the device map from the Mistral model to my small second model?
## Additional information
The second model which I have written injects a signal into the Mistral model at a strategic layer. However, this is done in a way that removes the possibility of inlining as I do not want to rewrite the model. How can I apply the same device map from the Mistral model? | https://github.com/huggingface/accelerate/issues/2395 | closed | [] | 2024-01-30T19:17:52Z | 2024-02-01T19:18:08Z | null | EricLBuehler |
huggingface/diffusers | 6,755 | how to train a lora in inpainting model? | Is there a script to train Lora in SD 1.5 inpainting?
Is there any script to train Lora in SD 1.5 inpainting that works?
try this
https://github.com/huggingface/diffusers/tree/main/examples/research_projects/dreambooth_inpaint
but it gives error
`RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn`
@thedarkzeno @patil-suraj | https://github.com/huggingface/diffusers/issues/6755 | closed | [
"stale"
] | 2024-01-29T21:14:57Z | 2024-11-22T01:39:54Z | null | loboere |
huggingface/optimum-benchmark | 116 | How to use optimum-benchmark for custom testing of my model | I am currently using Intel® Extension for Transformers to quantize a model, and I wonder if it is possible to utilize optimum-benchmark for testing the model. Alternatively, if there are other methods to load large models, could I conduct tests using optimum-benchmark after loading the model? Many thanks; this has been a real challenge for me, as I'm unsure how to properly test an optimized large-scale model.
| https://github.com/huggingface/optimum-benchmark/issues/116 | closed | [] | 2024-01-29T04:07:36Z | 2024-02-19T16:07:06Z | null | WCSY-YG |
huggingface/chat-ui | 747 | .env.local config for llama-2-7b.Q4_K_S.gguf with llama.cpp server | I am using the following .env.local with llama-2-7b.Q4_K_S.gguf and llama prompt template
```
MODELS=`[
{
"name": "llama-2-7b.Q4_K_S.gguf",
"chatPromptTemplate": "<s>[INST] <<SYS>>\n{{preprompt}}\n<</SYS>>\n\n{{#each messages}}{{#ifUser}}{{content}} [/INST] {{/ifUser}}{{#ifAssistant}}{{content}} </s><s>[INST] {{/ifAssistant}}{{/each}}",
"parameters": {
"temperature": 0.1,
"top_p": 0.95,
"repetition_penalty": 1.2,
"top_k": 50,
"truncate": 1000,
"max_new_tokens": 2048,
"stop": ["</s>"]
},
"endpoints": [
{
"url": "http://127.0.0.1:8080",
"type": "llamacpp"
}
]
}
]`
```
I am trying to get this work with chat-ui and it doesn't work and chat-ui is frozen. However server is receiving request from client.
<img width="1171" alt="image" src="https://github.com/huggingface/chat-ui/assets/106691906/e15147c5-5178-46b4-bc8c-d66bf4cfe1e3">
| https://github.com/huggingface/chat-ui/issues/747 | open | [
"support"
] | 2024-01-29T00:54:19Z | 2024-02-22T14:54:08Z | 3 | smamindl |
huggingface/chat-ui | 746 | settings page does not reflect selected Theme | Settings page is always light/white regardless of the Theme selected (Dark or Light).
Is this intentional or we just did not have time to respect the selected Theme?
If we need to fix this, how much work load do you expect? Just small change on the main settings page (settings/+layout.svelte) or do we need to change every UI piece in settings? I might want to fix this if this is not huge.
thanks | https://github.com/huggingface/chat-ui/issues/746 | open | [
"question",
"front"
] | 2024-01-28T23:09:38Z | 2024-01-29T11:48:59Z | null | hungryalgo |
huggingface/transformers.js | 547 | Text to speech generation using Xenova/mms-tts-por | ### Question
Hi! First of all, thank you for the awesome library, it's been handy so far!
I've got 2 questions regarding TTS:
- I'm using the model above to create a Brazilian Portuguese spoken audio and would like to know if there are options for this model, eg.: changing the voice from male to female, and the intonation.
- I discovered another model `facebook/mms-tts-por` in the compatible languages list, but I'm getting the following error: "'Could not locate file: "https://huggingface.co/facebook/mms-tts-por/resolve/main/tokenizer.json".'". Is transformer.js compatible with it?
Thanks in advance | https://github.com/huggingface/transformers.js/issues/547 | closed | [
"question"
] | 2024-01-28T13:51:21Z | 2025-01-13T22:15:35Z | null | Darksoulsong |
huggingface/diffusers | 6,739 | how to generate images based on the text token embedding outputted from CLIP. token_embedding module? | how to generate images based on the text token embedding outputted from CLIP. token_embedding module? | https://github.com/huggingface/diffusers/issues/6739 | closed | [
"stale",
"should-move-to-discussion"
] | 2024-01-28T08:51:45Z | 2024-11-19T09:27:00Z | null | FlyGreyWolf |
huggingface/transformers.js | 546 | header is not define | ### Question
| https://github.com/huggingface/transformers.js/issues/546 | closed | [
"question"
] | 2024-01-28T07:59:10Z | 2024-01-28T09:28:27Z | null | BipulRahi |
huggingface/datasets | 6,624 | How to download the laion-coco dataset | The laion coco dataset is not available now. How to download it
https://huggingface.co/datasets/laion/laion-coco | https://github.com/huggingface/datasets/issues/6624 | closed | [] | 2024-01-28T03:56:05Z | 2024-02-06T09:43:31Z | null | vanpersie32 |
huggingface/datasets | 6,623 | streaming datasets doesn't work properly with multi-node | ### Feature request
Let’s say I have a dataset with 5 samples with values [1, 2, 3, 4, 5], with 2 GPUs (for DDP) and batch size of 2. This dataset is an `IterableDataset` since I am streaming it.
Now I split the dataset using `split_dataset_by_node` to ensure it doesn’t get repeated. And since it’s already splitted, I don’t have to use `DistributedSampler` (also they don't work with iterable datasets anyway)?
But in this case I noticed that the:
First iteraton:
first GPU will get → [1, 2]
first GPU will get → [3, 4]
Second iteraton:
first GPU will get → [5]
first GPU will get → Nothing
which actually creates an issue since in case of `DistributedSampler`, the samples are repeated internally to ensure non of the GPUs at any iteration is missing any data for gradient sync.
So my questions are:
1. Here since splitting is happening before hand, how to make sure each GPU get’s a batch at each iteration to avoid gradient sync issues?
2. Do we need to use `DistributedSampler`? If yes, how?
3. in the docstrings of `split_dataset_by_node`, this is mentioned: *"If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples."* Can you explain the last part here?
4. If `dataset.n_shards % world_size != 0`, is it possible to shard the streaming dataset on the fly to avoid the case where data is missing?
### Motivation
Somehow streaming datasets should work with DDP since for big LLMs a lot of data is required and DDP/multi-node is mostly used to train such models and streaming can actually help solve the data part of it.
### Your contribution
Yes, I can help in submitting the PR once we get mutual understanding on how it should behave. | https://github.com/huggingface/datasets/issues/6623 | open | [
"enhancement"
] | 2024-01-27T23:46:13Z | 2025-12-08T12:26:20Z | 29 | rohitgr7 |
huggingface/unity-api | 23 | I need to specify text or text_target in text classification | I try calling the api by huggingfaceapi.textclassification("some string", response =>...) but got the error"you need to specify text or text_target". Where can I specify that in my unity C# code? | https://github.com/huggingface/unity-api/issues/23 | open | [
"question"
] | 2024-01-27T19:24:25Z | 2024-01-27T19:24:25Z | null | helenawsu |
huggingface/transformers.js | 543 | Converting a model to onnx using given script is hard(fails most of the time) | ### Question
I have tried to use starcoder model by bundling it using your ONNX script but it failed with some exception.
Model: https://huggingface.co/HuggingFaceH4/starchat-beta
or
https://huggingface.co/bigcode/starcoderbase
logs:
```bash
$ python -m scripts.convert --quantize --model_id HuggingFaceH4/starchat-beta
Framework not specified. Using pt to export to ONNX.
model-00001-of-00004.safetensors: 3%|█▏ | 346M/9.96G [03:20<1:33:01, 1.72MB/s]
Downloading shards: 0%| | 0/4 [03:23<?, ?it/s]
Loading TensorFlow model in PyTorch before exporting.
Traceback (most recent call last):
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/urllib3/response.py", line 712, in _error_catcher
yield
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/urllib3/response.py", line 833, in _raw_read
raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
urllib3.exceptions.IncompleteRead: IncompleteRead(351738674 bytes read, 9606258302 more expected)
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/requests/models.py", line 816, in generate
yield from self.raw.stream(chunk_size, decode_content=True)
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/urllib3/response.py", line 934, in stream
data = self.read(amt=amt, decode_content=decode_content)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/urllib3/response.py", line 905, in read
data = self._raw_read(amt)
^^^^^^^^^^^^^^^^^^^
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/urllib3/response.py", line 811, in _raw_read
with self._error_catcher():
File "/usr/lib/python3.11/contextlib.py", line 155, in __exit__
self.gen.throw(typ, value, traceback)
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/urllib3/response.py", line 729, in _error_catcher
raise ProtocolError(f"Connection broken: {e!r}", e) from e
urllib3.exceptions.ProtocolError: ('Connection broken: IncompleteRead(351738674 bytes read, 9606258302 more expected)', IncompleteRead(351738674 bytes read, 9606258302 more expected))
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/optimum/exporters/tasks.py", line 1708, in get_model_from_task
model = model_class.from_pretrained(model_name_or_path, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py", line 563, in from_pretrained
return model_class.from_pretrained(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/transformers/modeling_utils.py", line 2876, in from_pretrained
resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/transformers/utils/hub.py", line 1040, in get_checkpoint_shard_files
cached_filename = cached_file(
^^^^^^^^^^^^
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/transformers/utils/hub.py", line 429, in cached_file
resolved_file = hf_hub_download(
^^^^^^^^^^^^^^^^
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 1457, in hf_hub_download
http_get(
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/huggingface_hub/file_download.py", line 524, in http_get
for chunk in r.iter_content(chunk_size=DOWNLOAD_CHUNK_SIZE):
File "/home/username/Desktop/transformers.js/scripts/venv/lib/python3.11/site-packages/requests/models.py", line 818, in generate
raise ChunkedEncodingError(e)
requests.exceptions.ChunkedEncodingError: ('Connection broken: IncompleteRead(351738674 bytes read, 9606258302 more expected)', IncompleteRead(351738674 bytes read, 9606258302 more expected))
During handling of the above exception, another except | https://github.com/huggingface/transformers.js/issues/543 | open | [
"question"
] | 2024-01-27T07:32:42Z | 2024-01-30T06:48:44Z | null | bajrangCoder |
huggingface/candle | 1,624 | How to run the quantized Solar model? | I am trying to run the Solar model, but I am constantly failing. Here are my attempts:
1. [quantized] example (modified) with the Quantized Solar model (local)
: Failed. It only outputs nonsense that is unrelated to the question.
2. [llama] example with the Quantized Solar model (local)
: Failed. The process was Killed. Either because of ①a "Quantized" model or ②a low-spec PC (16GB of RAM, etc.).
3. [llama] example with the Solar model
: Failed. The process was Killed. The most likely cause is ①a low-spec PC.
4. oobabooga with the Quantized Solar model (local)
: Success. Confirmed that my PC can run the Quantized Solar model.
5. oobabooga with the Solar model
: Failed. The process was Killed. Confirmed that my PC cannot run the Solar model.
Conclusion: Is there any way to run the Quantized Solar model? I know I only wrote about 5 attempts, but I actually tried several different variations of the code in step 1. I also downloaded the model several times in my poor internet speed. | https://github.com/huggingface/candle/issues/1624 | open | [] | 2024-01-27T04:57:50Z | 2024-01-27T22:41:12Z | null | 555cider |
huggingface/peft | 1,401 | Where is `self.generation_config`coming from? | https://github.com/huggingface/peft/blob/1c1c7fdaa6e6abaa53939b865dee1eded82ad032/src/peft/peft_model.py#L1136
`self.generation` variable is not initialized in the model, it is also not part of a class up in the inheritance hierarchy.
So I assume it is retrieved from the base model via the implemented `\_\_getattr\_\_` method.
If that's the case, doesn't this make the code redundant? Also, how could this code work if we have to go down 1 level deeper? The only reason I can imagine doing this if `generation_config` is set after the model was initialized, but why would you need to do this?
Could you help me with this and explain how `generation_config` is supposed to be initialized and used?
Thank you :) Best
Simon | https://github.com/huggingface/peft/issues/1401 | closed | [] | 2024-01-27T02:02:30Z | 2024-03-11T15:04:29Z | null | simon-lund |
huggingface/transformers.js | 541 | Sharpe Linux-x86 | ### Question
Hi,
Firstly, many thanks for all your work.
My use case is to generate sentence embeddings for semantic matching. I develop on Mac but deploy to AWS Lambda.
Your package runs fine out the box on my Mac but fails to load Sharp on Lambda. I spent a couple of days trying lots of different things (fetching and building for Linux x86 and moving files around), but I never got it to work. In the end I removed the dependency on Sharp and it worked.
All's well, at present, but I do have a requirement in the future to embed images.
Sorry, I realise this may be more of a NPM issue (or more likely my knowledge of it), but any help would be appreciated.
Thanks
Dave | https://github.com/huggingface/transformers.js/issues/541 | closed | [
"question"
] | 2024-01-26T11:36:05Z | 2024-10-18T13:30:10Z | null | Damibu |
huggingface/text-generation-inference | 1,487 | How to run docker on a DPO model | ### Discussed in https://github.com/huggingface/text-generation-inference/discussions/1481
<div type='discussions-op-text'>
<sup>Originally posted by **tamanna-mostafa** January 24, 2024</sup>
1. I fine-tuned mistral 7b model with preference data (32k).
2. Then I ran DPO on the fine tuned model with 12k data.
This is the command I used to run docker:
```
accelerate launch --config_file ./accelerate_configs/ds_zero3.yaml rlhf_dpo.py \
--model_name_or_path="/mnt/efs/data/tammosta/files_t/output_sft_32k" \
--output_dir="/mnt/efs/data/tammosta/files_t/DPO_output_mistral_32k" \
--data_path="/mnt/efs/data/tammosta/files_t/DPO_data_rbs_clean_AIF.json" \
--use_lamma2_peft_config False \
--beta 0.1 \
--optimizer_type adamw_hf \
--learning_rate 1e-6 \
--warmup_steps 50 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--gradient_accumulation_steps 8 \
--lora_alpha 16 \
--lora_dropout 0.05 \
--lora_r 8 \
--max_prompt_length 2048 \
--max_length 4096 \
--num_train_epochs 4 \
--logging_steps 20 \
--save_steps 100 \
--save_total_limit 8 \
--eval_steps 50 \
--gradient_checkpointing True \
--report_to "wandb"
```
3. Now, I need to run inference on the DPO model.
I ran the following commands for this:
```
model=/data/DPO_output_mistral_32k
volume=/mnt/efs/data/tammosta/files_t:/data
num_shard=8
docker run --gpus all --shm-size 1g -p 172.31.8.218:80:80 -v $volume ghcr.io/huggingface/text-generation-inference:1.1.0 --model-id $model --num-shard $num_shard --max-input-length 4095 --max-total-tokens 12000
```
However, the docker failed to initialize the model with the following error:
`OSError: /data/DPO_output_mistral_32k does not appear to have a file named config.json. Checkout ' https://huggingface.co//data/DPO_output_mistral_32k/None ' for available files.`
Does anyone know how to create/find the config.json file?
I'll highly appreciate any help.</div> | https://github.com/huggingface/text-generation-inference/issues/1487 | closed | [] | 2024-01-25T17:11:52Z | 2024-01-31T16:44:32Z | null | tamanna-mostafa |
huggingface/transformers.js | 539 | How can i use this Model? | ### Question
How can i use this Model? https://huggingface.co/shibing624/macbert4csc-base-chinese | https://github.com/huggingface/transformers.js/issues/539 | closed | [
"question"
] | 2024-01-25T13:12:08Z | 2025-10-13T04:58:48Z | null | wfk007 |
huggingface/text-generation-inference | 1,483 | how to pdb text-generation-server | ### System Info
```
2024-01-25T09:10:08.096040Z INFO text_generation_launcher: Runtime environment:
Target: x86_64-unknown-linux-gnu
Cargo version: 1.70.0
Commit sha: 9f18f4c00627e1a0ad696b6774e5ad7ca8f4261c
Docker label: sha-9f18f4c
nvidia-smi:
Thu Jan 25 09:10:08 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.113.01 Driver Version: 535.113.01 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA GeForce RTX 3090 Off | 00000000:1A:00.0 Off | N/A |
| 30% 28C P8 24W / 350W | 5MiB / 24576MiB | 0% Default |
| | | N/A |
```
### Information
- [X] Docker
- [ ] The CLI directly
### Tasks
- [X] An officially supported command
- [ ] My own modifications
### Reproduction
When i add `pdb.set_trace()` in .py of text-generation-server, text-generation-launcher repeats the following log and seems to be stuck:
```
2024-01-25T09:07:04.875448Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:07:14.894477Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:07:24.911704Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:07:34.928347Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:07:44.947306Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:07:54.965355Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:08:04.984481Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:08:15.004175Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:08:25.022317Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:08:35.041246Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:08:45.059839Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:08:55.078293Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:09:05.097024Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:09:15.117255Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:09:25.136635Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:09:35.156270Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:09:45.175864Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:09:55.194405Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
2024-01-25T09:10:05.214396Z INFO shard-manager: text_generation_launcher: Waiting for shard to be ready... rank=0
```
### Expected behavior
I want to know how to debug .py of text-generation-server except logger? | https://github.com/huggingface/text-generation-inference/issues/1483 | closed | [] | 2024-01-25T09:21:32Z | 2024-02-19T07:23:14Z | null | jessiewiswjc |
huggingface/datasets | 6,614 | `datasets/downloads` cleanup tool | ### Feature request
Splitting off https://github.com/huggingface/huggingface_hub/issues/1997 - currently `huggingface-cli delete-cache` doesn't take care of cleaning `datasets` temp files
e.g. I discovered having millions of files under `datasets/downloads` cache, I had to do:
```
sudo find /data/huggingface/datasets/downloads -type f -mtime +3 -exec rm {} \+
sudo find /data/huggingface/datasets/downloads -type d -empty -delete
```
could the cleanup be integrated into `huggingface-cli` or a different tool provided to keep the folders tidy and not consume inodes and space
e.g. there were tens of thousands of `.lock` files - I don't know why they never get removed - lock files should be temporary for the duration of the operation requiring the lock and not remain after the operation finished, IMHO.
Also I think one should be able to nuke `datasets/downloads` w/o hurting the cache, but I think there are some datasets that rely on files extracted under this dir - or at least they did in the past - which is very difficult to manage since one has no idea what is safe to delete and what not.
Thank you
@Wauplin (requested to be tagged) | https://github.com/huggingface/datasets/issues/6614 | open | [
"enhancement"
] | 2024-01-24T18:52:10Z | 2024-01-24T18:55:09Z | 0 | stas00 |
huggingface/transformers | 28,663 | How to set stopping criteria in model.generate() when a certain word appear | ### Feature request
stopping criteria in model.generate() when a certain word appear
The word I need to stop the generation when found is : [/SENTENCE]
But the model doesn't generate the word itself, instead, it generates the subwords
[ [/,SEN,TE,NC,E] ]
like this .
corresponding ids from the tokenizer are,
( Id and subword word)
28792 => [
28748 => /
28759 => SEN
2654 => TE
1197 => NC
28793 => E]
so how can i put the condition in **StoppingCriteriaList** that i should stop the generation when the [/SENTENCE] found.
### Motivation
stopping criteria in model.generate() when a certain word appear
The word I need to stop the generation when found is : [/SENTENCE]
But the model doesn't generate the word itself, instead, it generates the subwords
[ [/,SEN,TE,NC,E] ]
like this .
corresponding ids from the tokenizer are,
( Id and subword word)
28792 => [
28748 => /
28759 => SEN
2654 => TE
1197 => NC
28793 => E]
so how can i put the condition in **StoppingCriteriaList** that i should stop the generation when the [/SENTENCE] found.
### Your contribution
stopping criteria in model.generate() when a certain word appear
The word I need to stop the generation when found is : [/SENTENCE]
But the model doesn't generate the word itself, instead, it generates the subwords
[ [/,SEN,TE,NC,E] ]
like this .
corresponding ids from the tokenizer are,
( Id and subword word)
28792 => [
28748 => /
28759 => SEN
2654 => TE
1197 => NC
28793 => E]
so how can i put the condition in **StoppingCriteriaList** that i should stop the generation when the [/SENTENCE] found. | https://github.com/huggingface/transformers/issues/28663 | closed | [] | 2024-01-23T15:16:38Z | 2024-03-02T08:03:44Z | null | pradeepdev-1995 |
huggingface/dataset-viewer | 2,333 | Replace TypedDict with dataclass? | Do we want to replace the TypedDict objects with dataclasses?
If so: note that the objects we serialize should be serialized too without any change by orjson, at the price of a small overhead (15% in their example: https://github.com/ijl/orjson#dataclass)
| https://github.com/huggingface/dataset-viewer/issues/2333 | closed | [
"good first issue",
"question",
"refactoring / architecture",
"P2"
] | 2024-01-23T10:49:52Z | 2024-06-19T14:30:53Z | null | severo |
huggingface/optimum | 1,664 | Bitsandbytes integration in ORTModelForCausalLM.from_pretrained() | ### System Info
```shell
optimum==1.17.0.dev0
```
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction (minimal, reproducible, runnable)
The given code
```
from optimum.onnxruntime import ORTModelForCausalLM
from transformers import BitsAndBytesConfig
finetuned_model_name = "path"
import torch
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=False)
ort_model = ORTModelForCausalLM.from_pretrained(
finetuned_model_name,
use_io_binding=True,
quantization_config=bnb_config,
export=True,
use_cache=True,
from_transformers=True
)
```
shows the errror
```
TypeError: _from_transformers() got an unexpected keyword argument 'quantization_config'
```
so how to do quantization while loading with **ORTModelForCausalLM**
### Expected behavior
The given code
```
from optimum.onnxruntime import ORTModelForCausalLM
from transformers import BitsAndBytesConfig
finetuned_model_name = "path"
import torch
compute_dtype = getattr(torch, "float16")
bnb_config = BitsAndBytesConfig(load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=False)
ort_model = ORTModelForCausalLM.from_pretrained(
finetuned_model_name,
use_io_binding=True,
quantization_config=bnb_config,
export=True,
use_cache=True,
from_transformers=True
)
```
shows the errror
```
TypeError: _from_transformers() got an unexpected keyword argument 'quantization_config'
```
so how to do quantization while loading with **ORTModelForCausalLM** | https://github.com/huggingface/optimum/issues/1664 | open | [
"bug"
] | 2024-01-23T08:56:45Z | 2024-01-23T08:56:45Z | 0 | pradeepdev-1995 |
huggingface/peft | 1,382 | How to set a predefined weight for LoRA and the linear layer | Hi,
Thanks for your great job!
I have a question: When adding LoRA on a linear layer, how to set a predefined weight for LoRA and the linear layer, instead of just 0.5 : 0.5 ?
| https://github.com/huggingface/peft/issues/1382 | closed | [] | 2024-01-22T13:24:31Z | 2024-02-06T08:37:49Z | null | quqxui |
huggingface/accelerate | 2,367 | how to prevent accelerate from concatenating tensors in batch? | My `collate_fn` in dataloader returns a list of image tensors with different height and width. After using `accelerator.prepare(model, optimizer, dataloader)`, I noticed that accelerate seems to automatically concatenate the tensors during `for step, batch in enumerate(train_dataloader)` iteration, and the size-mismatch leads to Exceptions.
Is there any parameter to prevent the auto-concatenating?
Or, should I remove `dataloader` from `accelerator.prepare` params? | https://github.com/huggingface/accelerate/issues/2367 | closed | [] | 2024-01-22T11:26:06Z | 2024-01-23T03:24:08Z | null | feiyangsuo |
huggingface/trl | 1,264 | How to train the model and ref_model on multiple GPUs with averaging? | For example,I have two RTX 3090 GPUs, and both the model and ref_model are 14 billion parameter models. I need to distribute these two models evenly across the two cards for training.
this is my code,but have an error:
```
"""
CUDA_VISIBLE_DEVICES=0 python Sakura_DPO.py \
--base_model Qwen-14B-Chat \
--ref_model Qwen-14B-Chat \
--data-path distilabel-intel-orca-dpo-pairs.json \
--output_dir distilabel-intel-orca-dpo-pairs \
--num_epochs 1 \
--batch_size 16 \
--micro_batch_size 1 \
--learning_rate 1e-6 \
--lora_r 32 \
--lora_alpha 32 \
--lora_dropout 0.05 \
--lr_scheduler 'cosine' \
--warmup_ratio 0.1 \
--cutoff_len 768
##########################
transformers
bitsandbytes
evaluate
peft
transformers_stream_generator
tiktoken
fire
trl
accelerate
deepspeed
"""
import os
import sys
from typing import List
import fire
import torch
import transformers
#import kosy_transformers
from datasets import load_dataset, Dataset
from transformers import TrainerCallback, TrainingArguments, TrainerState, TrainerControl
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
from torch.nn import functional as F
from peft import (
LoraConfig,
get_peft_model,
prepare_model_for_kbit_training,
set_peft_model_state_dict
)
from transformers import LlamaForCausalLM, LlamaTokenizer
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from trl import DPOTrainer
import bitsandbytes as bnb
#torch.autograd.set_detect_anomaly(True)
def find_all_linear_names(model):
#cls = bnb.nn.Linear8bitLt
cls = bnb.nn.Linear4bit
lora_module_names = set()
for name, module in model.named_modules():
if isinstance(module, cls):
names = name.split('.')
lora_module_names.add(names[0] if len(names) == 1 else names[-1])
if 'lm_head' in lora_module_names: # needed for 16-bit
lora_module_names.remove('lm_head')
return list(lora_module_names)
#os.environ["TOKENIZERS_PARALLELISM"] = "false"
from accelerate import Accelerator
from accelerate import PartialState
def train(
# model/data params
base_model: str = "",
ref_model: str = "None",
data_path: str = "",
output_dir: str = "",
# training hyperparams
batch_size: int = 128,
micro_batch_size: int = 8,
num_epochs: int = 1,
learning_rate: float = 3e-4,
cutoff_len: int = 4096,
val_set_size: int = 0,
lr_scheduler: str = "cosine",
warmup_ratio: float = 0.1,
# lora hyperparams
lora_r: int = 16,
lora_alpha: int = 16,
lora_dropout: float = 0.05,
# from peft docs: ["q_proj", "k_proj", "v_proj", "o_proj", "fc_in", "fc_out", "wte", "gate_proj", "down_proj", "up_proj"]
lora_target_modules: List[str] = ["gate_proj", "down_proj", "up_proj"],
# llm hyperparams
train_on_inputs: bool = False, # if False, masks out inputs in loss
add_eos_token: bool = False,
group_by_length: bool = False, # faster, but produces an odd training loss curve
gradient_checkpointing: bool = True,
# wandb params
#wandb_project: str = "",
#wandb_run_name: str = "",
#wandb_watch: str = "", # options: false | gradients | all
#wandb_log_model: str = "", # options: false | true
resume_from_checkpoint: str = None, # either training checkpoint or final adapter
prompt_template_name: str = "alpaca",
# NEFTune params
noise_alpha: int = 5
):
if int(os.environ.get("LOCAL_RANK", 0)) == 0:
print(
f"Params using prompt template {prompt_template_name}:\n"
f"base_model: {base_model}\n"
f"ref_model: {ref_model}\n"
f"data_path: {data_path}\n"
f"output_dir: {output_dir}\n"
f"batch_size: {batch_size}\n"
f"micro_batch_size: {micro_batch_size}\n"
f"num_epochs: {num_epochs}\n"
f"learning_rate: {learning_rate}\n"
f"cutoff_len: {cutoff_len}\n"
f"val_set_size: {val_set_size}\n"
f"lr_scheduler: {lr_scheduler}\n"
f"warmup_ratio: {warmup_ratio}\n"
f"lora_r: {lora_r}\n"
f"lora_alpha: {lora_alpha}\n"
f"lora_dropout: {lora_dropout}\n"
f"lora_target_modules: {lora_target_modules}\n"
f"train_on_inputs: {train_on_inputs}\n"
f"add_eos_token: {add_eos_token}\n"
f"group_by_length: {group_by_length}\n"
f"gradient_checkpointing: {gradient_checkpointing}\n"
#f"wandb_project: {wandb_project}\n"
#f"wandb_run_name: {wandb_run_name}\n"
#f"wandb_watch: {wandb_watch}\n"
#f"wandb_log_model: {wandb_log_model}\n"
f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
)
assert (
base_model
), "Please spe | https://github.com/huggingface/trl/issues/1264 | closed | [] | 2024-01-22T07:54:18Z | 2024-08-27T16:08:49Z | null | Minami-su |
huggingface/transformers.js | 528 | Preloading / Lazy loading model before generate requested | ### Question
Hi @xenova
I've been looking around for this type of functionality for ages and didn't realize you had this type of front-end inferencing locked down in such awesome fashion on browsers. Brilliant!!!
In the demo at https://xenova.github.io/transformers.js/, the model is loaded one-time when sending the first request/inference.
I want to pre-load a model in the background when a user opens the page, but not sure on the whether there is a method in your API for https://cdn.jsdelivr.net/npm/@xenova/transformers@2.14.0, or whether model loading is purely contingent on a first inference.
I've checked your API link: https://huggingface.co/docs/transformers.js/api/env, and nothing there that I can see so I'm assuming it requires a first run.
If it requires a first-run I can think of a couple workarounds, but wanted to check with you before heading down that rabbit hole.
Cheers | https://github.com/huggingface/transformers.js/issues/528 | closed | [
"question"
] | 2024-01-20T23:09:13Z | 2024-01-29T23:23:44Z | null | gidzr |
huggingface/sentence-transformers | 2,429 | How to additional special tokens using CrossEncoder? | I am using cross encoder.
I would like add a new special token (e.g., '[EOT]') on top of the pre-trained model & tokenizer (e.g., 'bert-base-uncased').
I am wondering what is the best way to do it? | https://github.com/huggingface/sentence-transformers/issues/2429 | open | [] | 2024-01-20T15:52:39Z | 2024-01-20T16:25:00Z | null | mucun1988 |
huggingface/optimum | 1,658 | TextStreamer not supported for ORTCausalLM? | ### System Info
```shell
System: IBM Power10
`5.14.0-362.13.1.el9_3.ppc64le`
OS: RHEL 9.3
Framework versions:
optimum==1.16.2
transformers==4.36.2
torch==2.0.1
onnx==1.13.1
onnxruntime==1.15.1
```
### Who can help?
@JingyaHuang @echarlaix
### 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 (minimal, reproducible, runnable)
This is a minimal repoducable example based on the official huggingface streamer example:
https://huggingface.co/docs/transformers/internal/generation_utils#transformers.TextStreamer.example
I exported the model before using `optimum-cli`:
`optimum-cli export onnx --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 /data/LLMs/onnx/tinyllama_onnx/`
```python
from transformers import AutoTokenizer, TextStreamer
from optimum.onnxruntime import ORTModelForCausalLM
model_id = "/data/LLMs/onnx/tinyllama_onnx"
tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
tokenizer.pad_token = tokenizer.eos_token
model = ORTModelForCausalLM.from_pretrained(model_id, use_cache=True, use_merged=False, use_io_binding=False)
text = "My name is William and I live in"
inp = tokenizer(text, return_tensors="pt", padding=True)
streamer = TextStreamer(inp)
_ = model.generate(**inp, streamer=streamer, max_new_tokens=256)
```
Error Message:
```python
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
File ~/micromamba/envs/gen-ai/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:266, in BatchEncoding.__getattr__(self, item)
265 try:
--> 266 return self.data[item]
267 except KeyError:
KeyError: 'decode'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
Cell In[7], line 5
3 inp = tokenizer(text, return_tensors="pt", padding=True)
4 streamer = TextStreamer(inp)
----> 5 _ = model.generate(**inp, streamer=streamer, max_new_tokens=256)
File ~/micromamba/envs/gen-ai/lib/python3.10/site-packages/torch/utils/_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
File ~/micromamba/envs/gen-ai/lib/python3.10/site-packages/transformers/generation/utils.py:1611, in GenerationMixin.generate(self, inputs, generation_config, logits_processor, stopping_criteria, prefix_allowed_tokens_fn, synced_gpus, assistant_model, streamer, negative_prompt_ids, negative_prompt_attention_mask, **kwargs)
1608 input_ids = inputs_tensor if model_input_name == "input_ids" else model_kwargs.pop("input_ids")
1610 if streamer is not None:
-> 1611 streamer.put(input_ids.cpu())
1613 # 6. Prepare `max_length` depending on other stopping criteria.
1614 input_ids_length = input_ids.shape[-1]
File ~/micromamba/envs/gen-ai/lib/python3.10/site-packages/transformers/generation/streamers.py:97, in TextStreamer.put(self, value)
95 # Add the new token to the cache and decodes the entire thing.
96 self.token_cache.extend(value.tolist())
---> 97 text = self.tokenizer.decode(self.token_cache, **self.decode_kwargs)
99 # After the symbol for a new line, we flush the cache.
100 if text.endswith("\n"):
File ~/micromamba/envs/gen-ai/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:268, in BatchEncoding.__getattr__(self, item)
266 return self.data[item]
267 except KeyError:
--> 268 raise AttributeError
AttributeError:
```
### Expected behavior
I would expect a streaming of tokens instead of waiting for the whole text to be processed/generated upfront :) | https://github.com/huggingface/optimum/issues/1658 | closed | [
"bug"
] | 2024-01-20T11:50:11Z | 2024-01-29T12:28:40Z | 1 | mgiessing |
huggingface/optimum | 1,657 | Clarity on the convert.py for a model to ONNX.py.. documentation issue | ### Feature request
I need some help understanding how this script is supposed to be run / implemented?
https://github.com/huggingface/optimum/blob/main/optimum/exporters/onnx/convert.py
Questions:
1. is this already included when I pip install optimum? .. which is implemented using the instructions at:
https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization#quantizing-a-model-to-be-used-with-optimums-cli
2. or is it the script that's called on from the modal.save when inferencing/calling onnx model?
3. or is this a separate script that can be called independently like the convert.py that xenova has?
Also, in order to run the optimum/exporters/onnx/convert.py script, do I need to download the full exporters folder, just the onnx folder, or can I just copy-paste the script and run that indepdently?
Much appreciated
### Motivation
Deeper understanding to use the resources in this github
### Your contribution
None | https://github.com/huggingface/optimum/issues/1657 | closed | [] | 2024-01-20T04:59:10Z | 2024-02-07T04:13:20Z | 2 | gidzr |
huggingface/candle | 1,608 | How to keep the model loaded in memory? | Hi guys,
I'm trying to setup a local instance of Phi-2 to use it as an autocomplete provider for my text editor.
The problem that I have is that each time I call the command to complete a text, the files have to be retrieved and the model loaded - which is a lot of time wasted for real time autocompletion.
`/.../candle/target/release/examples$ ./phi --model 2 --quantized --sample-len 12 --prompt "$(cat text-to-complete.md)"`
avx: false, neon: true, simd128: false, f16c: false
temp: 0.00 repeat-penalty: 1.10 repeat-last-n: 64
retrieved the files in 455.042µs
loaded the model in 2.127639167s
starting the inference loop
# The World History
Have you ever wondered how people lived in the past? ...
Do you know how to keep the model loaded in memory?
Like... Is there a possibility to start a server accepting post requests with prompts to complete - or something like this?
Thanks | https://github.com/huggingface/candle/issues/1608 | open | [] | 2024-01-19T19:16:54Z | 2024-01-20T00:27:22Z | null | tdkbzh |
huggingface/peft | 1,374 | How to activate, and keep frozen, multiple adapters? | Hello all,
I have been working on multiple adapters and part of my project requires that I activate all the loaded adapters. However, they must be frozen. I am running this code:
```python
adapters_items = iter(tqdm.tqdm(adapters.items()))
first_item = next(adapters_items)
model_peft = PeftModel.from_pretrained(model, first_item[1], first_item[0], is_trainable=False)
for adapter_name, model_id in adapters_items:
model_peft.load_adapter(model_id, adapter_name, is_trainable=False)
model_peft.base_model.set_adapter(list(adapters.keys()))
```
After some debugging, I see that the adapters are frozen (requires_grad=False) until the last line where I set the active adapters. After they are set to be active, requires_grad=True.
I see that `set_adapter` calls this function on all the LoraLayers, and how it sets the adapters to trainable.
> https://github.com/huggingface/peft/blob/ebbff4023ad276cbcb2466fd7e99be7d3ae0ae11/src/peft/tuners/tuners_utils.py#L464-L484
How can I set the active adapter(s) while keeping them frozen? | https://github.com/huggingface/peft/issues/1374 | closed | [] | 2024-01-19T11:28:15Z | 2024-02-07T11:13:24Z | null | EricLBuehler |
huggingface/text-generation-inference | 1,457 | How to use a finetuned model from my local directory | ### System Info
text-generation 0.6.1
### Information
- [ ] Docker
- [X] The CLI directly
### Tasks
- [X] An officially supported command
- [ ] My own modifications
### Reproduction
```
from text_generation import InferenceAPIClient
client = InferenceAPIClient( "/mylocalpath/finetunedmodel")
test_prompt = """sample prompt"""
text = client.generate(test_prompt).generated_text
print(text)
```
it showing the
```
NotFoundError: Model "/mylocalpath/finetunedmodel" does not exist
```
This finetuned model is tuned in the base model - Mistral
### Expected behavior
Expect to load the finetuned model from the local path | https://github.com/huggingface/text-generation-inference/issues/1457 | closed | [
"Stale"
] | 2024-01-19T06:18:41Z | 2024-03-10T01:45:51Z | null | pradeepdev-1995 |
huggingface/transformers | 28,598 | what is the correct format of input when fine-tuning GPT2 for text generation with batch input? | ### System Info
- `transformers` version: 4.33.0
- Platform: Windows-10-10.0.19045-SP0
- Python version: 3.10.12
- Huggingface_hub version: 0.16.4
- Safetensors version: 0.3.3
- Accelerate version: 0.22.0
- Accelerate config: not found
- PyTorch version (GPU?): 2.0.1+cpu (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?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
### Who can help?
@ArthurZucker
@younesbelkada
### 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
I want to fine-tune GPT2 for text generation with batch input. And I use follow code to format batch input:
```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained(r'E:\pythonWork\models\gpt2')
max_length = 8
datas = [
"The dog.",
"The cute dog.",
]
model_input = tokenizer(datas)
print('original input:\n', model_input)
# prepare for batch input
# I add bos token at the start and eos token at the end, and add pad token at the right to pad the sentences to the
# same length. bos_token_id=eos_token_id=50256, and there is not a pad token, so i also use 50256 as pad token.
labels_list = []
for i in range(len(datas)):
input_ids = [tokenizer.bos_token_id] + model_input['input_ids'][i] + [tokenizer.eos_token_id] # add bos and eos token
input_ids = input_ids + max(0, max_length-len(input_ids))*[tokenizer.eos_token_id] # add padding token
attention_mask = [1] + model_input['attention_mask'][i] + [1] # atten bos and eos token
attention_mask = attention_mask + max(0, max_length - len(attention_mask)) * [0] # dose't atten padding token
labels = [tokenizer.bos_token_id] + model_input['input_ids'][i] + [tokenizer.eos_token_id] # take loss for bos and eos
labels = labels + max(0, max_length - len(labels)) * [-100] # padding dose't take loss
model_input['input_ids'][i] = input_ids
model_input['attention_mask'][i] = attention_mask
labels_list.append(labels)
model_input['labels'] = labels_list
print('batch input:\n', model_input)
```
print message
```
original input:
{'input_ids': [[464, 3290, 13], [464, 13779, 3290, 13]],
'attention_mask': [[1, 1, 1], [1, 1, 1, 1]]}
batch input:
{'input_ids': [[50256, 464, 3290, 13, 50256, 50256, 50256, 50256], [50256, 464, 13779, 3290, 13, 50256, 50256, 50256]],
'attention_mask': [[1, 1, 1, 1, 1, 0, 0, 0], [1, 1, 1, 1, 1, 1, 0, 0]],
'labels': [[50256, 464, 3290, 13, 50256, -100, -100, -100], [50256, 464, 13779, 3290, 13, 50256, -100, -100]]}
``
### Expected behavior
my question:
1. the method I take to format batch input, is it right?
2. why can't gpt2 tokenizer auto format batch input like bert tokenzier do?
3. in this pre-training [demo](https://huggingface.co/learn/nlp-course/en/chapter7/6?fw=pt#preparing-the-dataset),
I found that it dose't add bos and eos tokens, and add pad token only at the end of the sequence.
So I think, in the pre-training time only need to add pad token to keep the sequence length consistent.
But when it comes to fine-tuning, additional eos tokens need to be added, and eos needs take loss because the model needs to learn when to stop generating.
Am I right? | https://github.com/huggingface/transformers/issues/28598 | closed | [] | 2024-01-19T06:17:29Z | 2024-01-22T01:49:43Z | null | minmie |
huggingface/transformers | 28,597 | How to find or create the `model_state_dict.bin` file for the `convert_llava_weights_to_hf.py` script | Hi @younesbelkada,
Following up on the [fix to the LLaVA convert script](https://github.com/huggingface/transformers/pull/28570) and thanks for all the help with the PR!
I encountered some issue with the convert script and wanted to ask about the recommended way to create the `model_state_dict.bin` file specified here: https://github.com/huggingface/transformers/blob/772307be7649e1333a933cfaa229dc0dec2fd331/src/transformers/models/llava/convert_llava_weights_to_hf.py#L74
In order to create the `model_state_dict.bin` I tried something like the following with the original https://github.com/haotian-liu/LLaVA code:
```python
import torch
from llava.model.language_model.llava_llama import LlavaLlamaForCausalLM
# load model
kwargs = {"device_map": "auto", "torch_dtype": torch.float16}
model = LlavaLlamaForCausalLM.from_pretrained("liuhaotian/llava-v1.5-7b", low_cpu_mem_usage=True, **kwargs)
# load vision tower
model.get_vision_tower().load_model()
# Save state dict
torch.save(model.state_dict(), "tmp/hf_models/llava-v1.5-7b/model_state_dict.bin")
```
It works but when I used the convert script I had to make the following changes:
* Remove keys that ended with `.inv_freq` (e.g. `language_model.model.layers.0.self_attn.rotary_emb.inv_freq`)
* Comment out the update to the `model.config.vocab_size` and `model.config.text_config.vocab_size` with the `pad_shape` here: https://github.com/huggingface/transformers/blob/772307be7649e1333a933cfaa229dc0dec2fd331/src/transformers/models/llava/convert_llava_weights_to_hf.py#L96-L97 otherwise, when I would try to load the converted model, it will error with the following:
```python
from transformers import AutoProcessor, LlavaForConditionalGeneration
model_id = "Shopify/llava-1.5-7b"
model = LlavaForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
```
```console
ValueError: Trying to set a tensor of shape torch.Size([32064, 5120]) in "weight" (which has shape torch.Size([32128, 5120])), this look incorrect.
```
Am I doing something wrong when I create the `model_state_dict.bin` file or am I missing something else?
Thanks again in advance. | https://github.com/huggingface/transformers/issues/28597 | closed | [] | 2024-01-19T02:38:31Z | 2024-01-22T14:28:20Z | null | isaac-vidas |
huggingface/chat-ui | 708 | Add support for other API endpoints | It would be nice if HuggingChat could be used locally, but calling other remote LLM endpoints other than OpenAI.
For instance, this could be mistral.ai 's API endpoints (same as OpenAI - only difference is model name), or a custom server configured for it.
Perhaps just adding a variable in the .env file defining the server? This seems like an easy feature, I could try implementing it myself if I get the time to look a bit more into the code (for instance, figuring out where the model name can be change)
https://github.com/huggingface/chat-ui/blob/ee47ff37fddb70f78d1ef8a293d8ed3fbcd24ff9/src/lib/server/endpoints/openai/endpointOai.ts#L13C1-L13C65 | https://github.com/huggingface/chat-ui/issues/708 | open | [
"support",
"models"
] | 2024-01-18T18:27:27Z | 2024-01-25T17:28:28Z | 4 | fbarbe00 |
huggingface/text-generation-inference | 1,451 | How to run text generation inference locally | ### System Info
I completed the steps for local installation of Text Generation Inference as in here: https://github.com/huggingface/text-generation-inference#local-install
I did all the installation on my local Linux (WSL). The model endpoint that I want to draw inference from is on my EC2. (I trained Mistral 7b model).
When I run `text-generation-launcher --env` , I get the following:
```
(text-generation-inference) tammosta@SEA-1801247735:~/text-generation-inference$ text-generation-launcher --env
error: invalid value 'True' for '--disable-custom-kernels'
[possible values: true, false]
tip: a similar value exists: 'true'
For more information, try '--help'.
(text-generation-inference) tammosta@SEA-1801247735:~/text-generation-inference$ export DISABLE_CUSTOM_KERNELS=true
(text-generation-inference) tammosta@SEA-1801247735:~/text-generation-inference$ text-generation-launcher --env
2024-01-17T19:54:02.802338Z INFO text_generation_launcher: Runtime environment:
Target: x86_64-unknown-linux-gnu
Cargo version: 1.70.0
Commit sha: 0eabc83541225979209ff7183b4b4442e47adf92
Docker label: N/A
nvidia-smi:
N/A
2024-01-17T19:54:02.802403Z INFO text_generation_launcher: Args { model_id: "bigscience/bloom-560m", revision: None, validation_workers: 2, sharded: None, num_shard: None, quantize: None, speculate: None, dtype: None, trust_remote_code: false, max_concurrent_requests: 128, max_best_of: 2, max_stop_sequences: 4, max_top_n_tokens: 5, max_input_length: 1024, max_total_tokens: 2048, waiting_served_ratio: 1.2, max_batch_prefill_tokens: 4096, max_batch_total_tokens: None, max_waiting_tokens: 20, hostname: "0.0.0.0", port: 3000, shard_uds_path: "/tmp/text-generation-server", master_addr: "localhost", master_port: 29500, huggingface_hub_cache: None, weights_cache_override: None, disable_custom_kernels: true, cuda_memory_fraction: 1.0, rope_scaling: None, rope_factor: None, json_output: false, otlp_endpoint: None, cors_allow_origin: [], watermark_gamma: None, watermark_delta: None, ngrok: false, ngrok_authtoken: None, ngrok_edge: None, env: true }
2024-01-17T19:54:02.802591Z INFO download: text_generation_launcher: Starting download process.
2024-01-17T19:54:09.019117Z INFO text_generation_launcher: Download file: model.safetensors
2024-01-17T19:54:51.649553Z INFO text_generation_launcher: Downloaded /home/tammosta/.cache/huggingface/hub/models--bigscience--bloom-560m/snapshots/ac2ae5fab2ce3f9f40dc79b5ca9f637430d24971/model.safetensors in 0:00:42.
2024-01-17T19:54:51.649696Z INFO text_generation_launcher: Download: [1/1] -- ETA: 0
2024-01-17T19:54:52.249742Z INFO download: text_generation_launcher: Successfully downloaded weights.
2024-01-17T19:54:52.250108Z INFO shard-manager: text_generation_launcher: Starting shard rank=0
2024-01-17T19:54:56.525795Z WARN text_generation_launcher: We're not using custom kernels.
2024-01-17T19:54:56.534344Z WARN text_generation_launcher: Could not import Flash Attention enabled models: No module named 'vllm'
2024-01-17T19:55:01.117291Z INFO text_generation_launcher: Server started at unix:///tmp/text-generation-server-0
2024-01-17T19:55:01.167200Z INFO shard-manager: text_generation_launcher: Shard ready in 8.916023926s rank=0
2024-01-17T19:55:01.265832Z INFO text_generation_launcher: Starting Webserver
2024-01-17T19:55:01.366710Z INFO text_generation_router: router/src/main.rs:178: Using the Hugging Face API
2024-01-17T19:55:01.366788Z INFO hf_hub: /home/tammosta/.cargo/registry/src/index.crates.io-6f17d22bba15001f/hf-hub-0.3.2/src/lib.rs:55: Token file not found "/home/tammosta/.cache/huggingface/token"
2024-01-17T19:55:02.294337Z INFO text_generation_router: router/src/main.rs:416: Serving revision ac2ae5fab2ce3f9f40dc79b5ca9f637430d24971 of model bigscience/bloom-560m
2024-01-17T19:55:02.294415Z INFO text_generation_router: router/src/main.rs:234: Using the Hugging Face API to retrieve tokenizer config
2024-01-17T19:55:02.315279Z INFO text_generation_router: router/src/main.rs:277: Warming up model
2024-01-17T19:55:46.211550Z ERROR shard-manager: text_generation_launcher: Shard complete standard error output:
/home/tammosta/anaconda3/envs/text-generation-inference/lib/python3.9/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
warn("The installed version of bitsandbytes was compiled without GPU support. "
config.json: 100%|██████████| 693/693 [00:00<00:00, 189kB/s]
tokenizer_config.json: 100%|██████████| 222/222 [00:00<00:00, 106kB/s]
tokenizer.json: 100%|██████████| 14.5M/14.5M [00:00<00:00, 23.4MB/s]
special_tokens_map.json: 100%|██████████| 85.0/85.0 [00:00<00:00, 34.9kB/s]
/home/tammosta/text-generation-inference/server/text_generation_server/models/custom_modeling/bloom_modeling.py:882: FutureWarning: `position_ids` have no functionalit | https://github.com/huggingface/text-generation-inference/issues/1451 | closed | [
"Stale"
] | 2024-01-17T20:12:35Z | 2024-02-22T01:44:26Z | null | tamanna-mostafa |
huggingface/diffusers | 6,614 | How to train text_to_image with images which is resolution of 512x768 ? | I want to finetune the sd1.5 with 50k images, all the image is resolution of 512x768. But I got error like this:
`train_text_to_image.py:` error: argument --resolution: invalid int value: '[512,768]'`
so, how to train text_to_image with images which is resolution of 512x768? | https://github.com/huggingface/diffusers/issues/6614 | closed | [] | 2024-01-17T13:51:16Z | 2024-01-25T14:28:01Z | null | lingxuan630 |
huggingface/accelerate | 2,347 | How to load model to specified GPU devices? | I'm trying a large model LLaVA1.5.
I know that if I set the parameter `device_map='auto'` in `LlavaMPTForCausalLM.from_pretrained`, the model will be loaded on all visible GPUs (FSDP).
Now I hope to load LLaVA1.5 on some of the visible GPUs, still in the FSDP mode, and automatically decide device_map like `device_map='auto'`. Note that the GPUs can be **arbitrarily assigned**, i.e. GPU 2, 3, 4, but not starting with GPU 0.
I try to achieve this by passing a `max_memory`, like
`model = LlavaMPTForCausalLM.from_pretrained(model_path,device_map='auto', max_memory={2: 33271054336, 3: 33271054336, 4: 33271054336})`
However, an error occured
I think the loop should be modified?
Or is there are any simpler ways to achieve my goal? | https://github.com/huggingface/accelerate/issues/2347 | closed | [] | 2024-01-17T09:23:04Z | 2024-02-26T15:06:36Z | null | davidluciolu |
huggingface/transformers | 28,546 | How to use fp32 and qLora to fine-tune models | ### System Info
I'm using transformers version 4.32.0 and I want to fine-tune the Qwen/Qwen-VL-Chat-Int4 model, but my 1080ti GPU doesn't support fp16. When I want to use "training_args.fp16 = False" to modify the parameters, the error "dataclasses.FrozenInstanceError: cannot assign to field fp16" will be reported. I guess this parameter cannot be changed manually. What should I do besides changing the GPU so that it can use fp16?
### 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
I am using the fine-tuning code given by Qwen：
```python
parser = transformers.HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments, LoraArguments)
)
(
model_args,
data_args,
training_args,
lora_args,
) = parser.parse_args_into_dataclasses()
if getattr(training_args, 'deepspeed', None) and getattr(lora_args, 'q_lora', False):
training_args.distributed_state.distributed_type = DistributedType.DEEPSPEED
training_args.fp16 = False
compute_dtype = (
torch.float16
if training_args.fp16
else (torch.bfloat16 if training_args.bf16 else torch.float32)
)
local_rank = training_args.local_rank
device_map = None
world_size = int(os.environ.get("WORLD_SIZE", 1))
ddp = world_size != 1
if lora_args.q_lora:
device_map = {"": int(os.environ.get("LOCAL_RANK") or 0)} if ddp else None
if len(training_args.fsdp) > 0 or deepspeed.is_deepspeed_zero3_enabled():
logging.warning(
"FSDP or ZeRO3 are not incompatible with QLoRA."
)
# Set RoPE scaling factor
config = transformers.AutoConfig.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
trust_remote_code=True,
)
config.use_cache = False
# Load model and tokenizer
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
config=config,
cache_dir=training_args.cache_dir,
device_map=device_map,
trust_remote_code=True,
quantization_config=GPTQConfig(
bits=4, disable_exllama=True
)
if training_args.use_lora and lora_args.q_lora
else None,
）
```
### Expected behavior
I want a solution | https://github.com/huggingface/transformers/issues/28546 | closed | [] | 2024-01-17T07:16:11Z | 2024-02-26T08:04:39Z | null | guoyunqingyue |
huggingface/sentence-transformers | 2,416 | How to specify class weights in model training? | I am having a very imbalanced training dataset. Is there a way I could specify class weights (e.g., class 0: 0.1, class 1: 1) for cross encoder training? | https://github.com/huggingface/sentence-transformers/issues/2416 | closed | [] | 2024-01-16T21:00:27Z | 2024-01-20T15:49:54Z | null | mucun1988 |
huggingface/chat-ui | 697 | Add streaming support for SageMaker endpoints | Would be nice to have support for streaming tokens from sagemaker. here are some ressources from my conversation with @philschmid
### Code sample (Python Code)
```
body = {"inputs": "what is life", "parameters": {"max_new_tokens":400}}
resp = smr.invoke_endpoint_with_response_stream(EndpointName=endpoint_name, Body=json.dumps(body), ContentType="application/json")
event_stream = resp['Body']
for line in LineIterator(event_stream):
resp = json.loads(line)
print(resp.get("outputs")[0], end='')
```
### Docs (JS)
https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/sagemaker-runtime/command/InvokeEndpointWithResponseStreamCommand/ | https://github.com/huggingface/chat-ui/issues/697 | open | [
"enhancement",
"back"
] | 2024-01-16T10:59:47Z | 2024-01-16T11:00:32Z | 0 | nsarrazin |
huggingface/transformers.js | 522 | Is it possible to fine-tune the hosted pretrained models? | ### Question
Hello,
If we have a large dataset in our domain, can we use it to fine-tune the hosted pretrained models(for example: Xenova/nllb-200-distilled-600M) with optimum? or is it possible to convert our own translation Pytorch model to ONNX which can be compatible with transformer.js? | https://github.com/huggingface/transformers.js/issues/522 | open | [
"question"
] | 2024-01-16T03:55:39Z | 2024-01-16T12:54:53Z | null | lhohoz |
huggingface/datasets | 6,594 | IterableDataset sharding logic needs improvement | ### Describe the bug
The sharding of IterableDatasets with respect to distributed and dataloader worker processes appears problematic with significant performance traps and inconsistencies wrt to distributed train processes vs worker processes.
Splitting across num_workers (per train process loader processes) and world_size (distributed training processes) appears inconsistent.
* worker split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1266-L1283
* distributed split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1335-L1356
In the case of the distributed split, there is a modulus check that flips between two very different behaviours, why is this different than splitting across the data loader workers? For IterableDatasets the DataLoaders worker processes are independent, so whether it's workers within one train process or across a distributed world the shards should be distributed the same, across `world_size * num_worker` independent workers in either case...
Further, the fallback case when the `n_shards % world_size == 0` check fails is a rather extreme change. I argue it is not desirable to do that implicitly, it should be an explicit case for specific scenarios (ie reliable validation). A train scenario would likely be much better handled with improved wrapping / stopping behaviour to eg also fix #6437. Changing from stepping shards to stepping samples means that every single process reads ALL of the shards. This was never an intended default for sharded training, shards gain their performance advantage in large scale distributed training by explicitly avoiding the need to have every process overlapping in the data they read, by default, only the data allocated to each process via their assigned shards should be read in each pass of the dataset.
Using a large scale CLIP example, some of the larger datasets have 10-20k shards across 100+TB of data. Training with 1000 GPUs we are switching between reading 100 terabytes per epoch to 100 petabytes if say change 20k % 1000 and drop one gpu-node to 20k % 992.
The 'step over samples' case might be worth the overhead in specific validation scenarios where gaurantees of at least/most once samples seen are more important and do not make up a significant portion of train time or are done in smaller world sizes outside of train.
### Steps to reproduce the bug
N/A
### Expected behavior
We have an iterable dataset with N shards, to split across workers
* shuffle shards (same seed across all train processes)
* step shard iterator across distributed processes
* step shard iterator across dataloader worker processes
* shuffle samples in every worker via shuffle buffer (different seed in each worker, but ideally controllable (based on base seed + worker id + epoch).
* end up with (possibly uneven) number of shards per worker but each shard only ever accessed by 1 worker per pass (epoch)
### Environment info
N/A | https://github.com/huggingface/datasets/issues/6594 | open | [] | 2024-01-15T22:22:36Z | 2025-11-10T14:55:20Z | 7 | rwightman |
huggingface/alignment-handbook | 103 | Does QLora DPO Training support reference model? | Hello! Thanks for your awesome work!
I meet an issue when I run dpo with qlora. I notice there is a setting:
```
if model_args.use_peft is True:
ref_model = None
ref_model_kwargs = None
```
I also notice that the `use_peft` is set to true only in config_qlora.yaml. This means if we use qlora to do dpo training, we do not use reference model at all.
I wonder if this code support qlora training with reference model? Thanks! | https://github.com/huggingface/alignment-handbook/issues/103 | open | [] | 2024-01-15T09:22:32Z | 2024-01-15T09:27:08Z | 0 | Harry-mic |
huggingface/swift-coreml-diffusers | 91 | How to import new .SAFETENSORS model? | How can I import a safetensor formatted model into the diffusers app?
I tried copying the safetensor file to the folder loaded by the dropdown menu. But when I relaunch the app, it doesn't show the new model in the menu. | https://github.com/huggingface/swift-coreml-diffusers/issues/91 | open | [] | 2024-01-15T08:24:53Z | 2024-07-07T09:03:27Z | null | mcandre |
huggingface/candle | 1,585 | Extension request: How to construct Tensor for n-dimensional Vec | How do I best create a Tensor from a &Vec<Vec<u8>> type? Everything above 1D is quite hard to manage for index based value setting. | https://github.com/huggingface/candle/issues/1585 | closed | [] | 2024-01-14T17:46:57Z | 2025-11-23T20:22:09Z | null | BDUG |
huggingface/nanotron | 21 | Save checkpoint before terminating the training run | Why don't we save a model checkpoint before terminating the training run? [[link]](https://github.com/huggingface/nanotron/blob/fd99571e3769cb1876d5c9d698b512e85a6e4896/src/nanotron/trainer.py#L429)
<img width="769" alt="image" src="https://github.com/huggingface/nanotron/assets/22252984/9eb78431-4df9-4795-8ac7-6947f71f6bae">
| https://github.com/huggingface/nanotron/issues/21 | closed | [
"question"
] | 2024-01-13T11:28:20Z | 2024-01-13T11:28:54Z | null | xrsrke |
huggingface/accelerate | 2,331 | How to share non-tensor data between processes? | I am running a training on 2 GPUs on the same machine. I need a way to share some float values and maybe dicts between the two processes. I saw that there is a `gather` method, but this only works for tensors.
Is there any way to do inter-process communication that is not directly related to the training?
EDIT: What I want to do is log the AVERAGE training error of my model after each epoch. The problem is that the process I am logging from only sees the training error that was computed in this process | https://github.com/huggingface/accelerate/issues/2331 | closed | [] | 2024-01-12T19:13:27Z | 2024-01-16T11:36:34Z | null | simonhessner |
huggingface/transformers | 28,476 | How to avoid the peak RAM memory usage of a model when I want to load to GPU | ### System Info
- `transformers` version: 4.36.2
- Platform: Linux-5.10.201-191.748.amzn2.x86_64-x86_64-with-glibc2.31
- Python version: 3.10.13
- Huggingface_hub version: 0.20.2
- Safetensors version: 0.4.1
- Accelerate version: 0.26.0
- Accelerate config: not found
- PyTorch version (GPU?): 2.1.0 (True)
- 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?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>
### Who can help?
I am using transformers to load a model into GPU, and I observed that before moving the model to GPU there is a peak of RAM usage that later gets unused. I assume the model is loaded into CPU before moving into GPU.
In GPU model takes around 4Gi and to load it I need more than 7Gi of RAM which seems weird.
Is there a way to load it direcly to the GPU without spending so much RAM?
I have tried with the `low_cpu_mem_usage` and `device_map` parameter to `cuda` and `auto` but no luck.
```python
from transformers import AutoModel; m = AutoModel.from_pretrained("jinaai/jina-embeddings-v2-base-en", trust_remote_code=True, low_cpu_mem_usage=True, device_map="auto")
```
### 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
```python
from transformers import AutoModel; m = AutoModel.from_pretrained("jinaai/jina-embeddings-v2-base-en", trust_remote_code=True, low_cpu_mem_usage=True, device_map="auto")
```
### Expected behavior
Not having such a memory peak | https://github.com/huggingface/transformers/issues/28476 | closed | [] | 2024-01-12T11:39:52Z | 2024-02-12T08:08:17Z | null | JoanFM |
huggingface/datasets | 6,584 | np.fromfile not supported | How to do np.fromfile to use it like np.load
```python
def xnumpy_fromfile(filepath_or_buffer, *args, download_config: Optional[DownloadConfig] = None, **kwargs):
import numpy as np
if hasattr(filepath_or_buffer, "read"):
return np.fromfile(filepath_or_buffer, *args, **kwargs)
else:
filepath_or_buffer = str(filepath_or_buffer)
return np.fromfile(xopen(filepath_or_buffer, "rb", download_config=download_config).read(), *args, **kwargs)
```
this is not work
| https://github.com/huggingface/datasets/issues/6584 | open | [] | 2024-01-12T09:46:17Z | 2024-01-15T05:20:50Z | 6 | d710055071 |
huggingface/distil-whisper | 73 | I want to confirm how the knowledge organization is implemented？ | I don't quite understand how knowledge distillation is implemented here.
Whisper is trained on 680,000 hours of untagged data for autoregression. According to the content of the fourth section of the paper, our model is trained on 21,170 hours of data with pseudo-labels generated by Whisper, with the first and 32nd layer parameters frozen based on Whisper. **This means that our model only needs to go through 21,170 hours of data with pseudo-labels and a model structure similar to Whisper, freezing the first and 32nd layers, using weighted KL divergence and label cross-entropy to achieve good results？**
If this is the case, it is indeed a significant discovery, indicating that we can always reduce the model's parameters and inference time after pre-training the model using similar methods, without significant loss of accuracy.
Thank you in advance | https://github.com/huggingface/distil-whisper/issues/73 | open | [] | 2024-01-12T07:43:21Z | 2024-01-17T16:57:31Z | null | hxypqr |
huggingface/transformers.js | 516 | How to access attentions matrix for MarianMT? | ### Question
Hey, I've been trying to access the attentions output by the MarianMT like so (please excuse the unorthodox config argument, tidying up is next on my todo list):
```
const model_name = "Xenova/opus-mt-en-fr";
const tokenizer = await MarianTokenizer.from_pretrained(model_name, {
config: {
output_hidden_states: true,
output_attentions: true
}
})
const tokens = (await tokenizer(text)).input_ids;
const model = await MarianMTModel.from_pretrained(model_name, {
config: {
model_type: 'marian',
is_encoder_decoder: true,
_name_or_path: 'Helsinki-NLP/opus-mt-en-fr',
_num_labels: 3,
activation_dropout: 0,
activation_function: 'swish',
add_bias_logits: false,
add_final_layer_norm: false,
architectures: ['MarianMTModel'],
attention_dropout: 0,
bad_words_ids: [[Array]],
bos_token_id: 0,
classif_dropout: 0,
classifier_dropout: 0,
d_model: 512,
decoder_attention_heads: 8,
decoder_ffn_dim: 2048,
decoder_layerdrop: 0,
decoder_layers: 6,
decoder_start_token_id: 59513,
decoder_vocab_size: 59514,
dropout: 0.1,
encoder_attention_heads: 8,
encoder_ffn_dim: 2048,
encoder_layerdrop: 0,
encoder_layers: 6,
eos_token_id: 0,
forced_eos_token_id: 0,
gradient_checkpointing: false,
id2label: { '0': 'LABEL_0', '1': 'LABEL_1', '2': 'LABEL_2' },
init_std: 0.02,
label2id: { LABEL_0: 0, LABEL_1: 1, LABEL_2: 2 },
max_length: 512,
max_position_embeddings: 512,
normalize_before: false,
normalize_embedding: false,
num_beams: 4,
num_hidden_layers: 6,
pad_token_id: 59513,
scale_embedding: true,
share_encoder_decoder_embeddings: true,
static_position_embeddings: true,
transformers_version: '4.34.0.dev0',
use_cache: true,
vocab_size: 59514,
output_hidden_states: true,
output_cross_attentions: true,
output_attentions: true
}
})
const translated = await model.generate(tokens)
const result = tokenizer.decode(translated[0], { skip_special_tokens: true })
console.log((await model.getAttentions(translated)))
```
I'm then getting the following error when I run the code:
`
Error: `output_attentions` is true, but the model did not produce cross-attentions. This is most likely because the model was not exported with `output_attentions=True`.
`
I've looked around but haven't been able to find out what is meant by the reference to exporting the model. How would I go about fixing this? | https://github.com/huggingface/transformers.js/issues/516 | open | [
"question"
] | 2024-01-11T20:16:42Z | 2024-01-15T08:21:17Z | null | DaveTJones |
huggingface/text-generation-inference | 1,437 | How to run text-generation-benchmark without the graph and get the output data into a csv file or a json file? | ### Feature request
text-generation-benchmark has been an amazing tool for understanding the model deployments better. Is there a way where we can run this without generating the graph and get the results in a csv format?
### Motivation
Motivation is that we want to use this tool with another program which gets the results from the binary.
### Your contribution
I'm not sure. Looks like an addition to the TGI-benchmark parameter and it can be a potential PR | https://github.com/huggingface/text-generation-inference/issues/1437 | closed | [
"Stale"
] | 2024-01-11T15:33:37Z | 2024-02-17T01:44:18Z | null | pranavthombare |
huggingface/transformers.js | 515 | ONNX optimisations for edge deployment | ### Question
Hello, I'm exploring if I can extract any more performance from my deployment of transformers.js. Appreciate the answer to this is nuanced and best answered by profiling, but would value opinions of experts that have walked this path before using this lib.
In my specific use case I know that I will always be deploying to the latest chrome running on windows systems that exist in VM and do not have a dedicated GPU (i.e. vanilla corprate desktop)
In the current util, during the export no optimization flag is passed so by default the models aren't optimized. https://github.com/xenova/transformers.js/blob/main/scripts/convert.py#L426
The main export takes a AutoOptimization level as a string and given no GPU's I would be restricted to 03.
https://github.com/huggingface/optimum/blob/main/optimum/exporters/onnx/__main__.py#L567
##Questions:
1. Is there any reasons I wouldn't want to optimize a model using transformers.js?
2. Auto optimize seems to detect BERT automatically.
https://github.com/microsoft/onnxruntime/blob/main/onnxruntime/python/tools/transformers/fusion_options.py#L56
Is there any reason that I should modify transformers.js convert.py to to manually call ORTOptimizer with a OptimizationConfig inbetween steps 1&2 instead of passing a level string in step 1?
https://github.com/xenova/transformers.js/blob/main/scripts/convert.py#L429
| https://github.com/huggingface/transformers.js/issues/515 | closed | [
"question"
] | 2024-01-11T13:49:59Z | 2025-10-13T04:59:32Z | null | georgedavies019 |
huggingface/alignment-handbook | 98 | Is QLoRA better than finetuning? | The results reported in https://github.com/huggingface/alignment-handbook/pull/88 suggest that QLoRA is better for both SFT and DPO. Is this accurate, and have people seen this happen in any other settings? | https://github.com/huggingface/alignment-handbook/issues/98 | open | [] | 2024-01-10T21:04:11Z | 2024-01-10T21:04:11Z | 0 | normster |
huggingface/transformers.js | 514 | Is it possible to use adapters from the hub? | ### Question
Hi, would it be possible to use adapters on top of a model using the js library? | https://github.com/huggingface/transformers.js/issues/514 | open | [
"question"
] | 2024-01-10T20:57:03Z | 2024-01-11T16:01:11Z | null | vabatta |
huggingface/setfit | 468 | How effective is to use your own pre-trained ST model based on NLI dataset ? | Hi !
I'm interested to use SetFit for classify text extracted from hotel reviews (booking, tripadvisor, etc) but I would to add domain knowledge to my Sentence Transfomers body.
For example, this [paper](https://arxiv.org/abs/2202.01924) use a Sentence Transformers model trained on a custom NLI dataset (RNLI for Review Natural Langage Inference) for extract product features without training on labeled data. The results show that a train on domain based NLI dataset is better that the MNLI for Zero-Shot aspect extraction.
So, is it a good approach to train my own Sentence Transformers model (or fine-tune a pre-trained) on NLI domain based dataset for improve performance of SetFit ?
Thank you in advance | https://github.com/huggingface/setfit/issues/468 | closed | [] | 2024-01-10T19:25:09Z | 2024-02-09T14:55:46Z | null | azaismarc |
huggingface/transformers.js | 512 | What do you all think about having a "Transformers.js Community" in Hugging Face? | ### Question
After checking how [MLX Community on Hugging Face](https://huggingface.co/mlx-community) is working, I thought it could be a good idea to have one for Transformers.js.
One of the key benefits of a community is "multiple curators": anyone in the community would have the ability to edit the repositories, which makes it easier to maintain the converted models and ensure that they have more detailed Readmes.
Also, having multiple curators allows for quicker resolution of issues with the model configuration. Members of the community won't need to create a pull request to request changes or wait for someone to approve the PR, which is especially important for urgent fixes.
Another good move the MLX community made was releasing a script that automatically uploads models to the organization in Hugging Face, which makes it easy for anyone to convert and share their favorite models.
I would love to hear the opinions of others. | https://github.com/huggingface/transformers.js/issues/512 | closed | [
"question"
] | 2024-01-10T16:03:51Z | 2025-05-10T21:06:54Z | null | felladrin |
huggingface/candle | 1,552 | How to pass the attention_mask to Bert model in examples? | I am trying to run `shibing624/text2vec-base-chinese` with candle, and the encoder returns `input_ids`, `attention_mask`, `token_id_types`, but there are only two params of BertModel in candle.
https://github.com/huggingface/candle/blob/main/candle-examples/examples/bert/main.rs#L170
```python
from transformers import BertTokenizer, BertModel
import torch
# Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] # First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Load model from HuggingFace Hub
tokenizer = BertTokenizer.from_pretrained('shibing624/text2vec-base-chinese')
model = BertModel.from_pretrained('shibing624/text2vec-base-chinese')
sentences = ['如何更换花呗绑定银行卡', '花呗更改绑定银行卡']
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
``` | https://github.com/huggingface/candle/issues/1552 | closed | [] | 2024-01-10T11:57:55Z | 2024-01-10T12:38:54Z | null | lz1998 |
huggingface/sentence-transformers | 2,400 | New release of library? | I was wondering when you will be releasing a new version of the library that includes the latest changes in the main branch? We are eagerly awaiting one inorder to consume the fix for this issue https://github.com/UKPLab/sentence-transformers/issues/1800 | https://github.com/huggingface/sentence-transformers/issues/2400 | closed | [
"question"
] | 2024-01-09T20:42:53Z | 2024-01-29T10:00:33Z | null | vineetsajuTR |
huggingface/peft | 1,334 | when we use inject_adapter_in_model method to inject the adapters directly into a PyTorch model, how to merge the Lora weight with the base model in the inference stage? | https://github.com/huggingface/peft/issues/1334 | closed | [] | 2024-01-09T12:30:52Z | 2024-02-17T15:03:59Z | null | mikiyukio | |
huggingface/datasets | 6,570 | No online docs for 2.16 release | We do not have the online docs for the latest minor release 2.16 (2.16.0 nor 2.16.1).
In the online docs, the latest version appearing is 2.15.0: https://huggingface.co/docs/datasets/index
| https://github.com/huggingface/datasets/issues/6570 | closed | [
"bug",
"documentation"
] | 2024-01-09T07:43:30Z | 2024-01-09T16:45:50Z | 7 | albertvillanova |
huggingface/text-generation-inference | 1,415 | How to use local Medusa head? | It is said that Medusa can significantly accelerate inference speed. During my attempts to utilize it, I have observed that it does not support the use of local Medusa config and head. The code fragment I discovered that pertains to this functionality is as follows, which I have modified. However, I do not comprehend the meaning of 'medusa_sf'. The training process of Medusa does not generate new safetensors. What is this?
```python
medusa_config = f"{model_id}/config_medusa.json"
# medusa_config = hf_hub_download(
# use_medusa, revision=revision, filename="config.json"
# )
with open(medusa_config, "r") as f:
config = json.load(f)
medusa_head = f"{model_id}/medusa_lm_head.pt"
# medusa_head = hf_hub_download(
# use_medusa, revision=revision, filename="medusa_lm_head.pt"
# )
medusa_sf = medusa_head[: -len(".pt")] + ".safetensors"
weights = Weights(
[medusa_sf], device, dtype, process_group=self.process_group
)
lm_head = model.lm_head
model.lm_head = MedusaModel(config, weights, lm_head)
```
How should I employ TGI to access the local Medusa? A huge thank for your work! | https://github.com/huggingface/text-generation-inference/issues/1415 | closed | [] | 2024-01-09T03:22:47Z | 2024-01-10T17:36:23Z | null | eurus-ch |
huggingface/transformers | 28,388 | How to use an efficient encoder as shared EncoderDecoderModel? | ### Feature request
Efficient encoder like destilBERT, ALBERT or ELECTRA aren't supported as decoder of the EncoderDecoderModel and so they can't be shared as encoder and decoder.
### Motivation
Warm-starting shared models is a powerful way to build transformer models. Yet the efficient models can't be used.
### Your contribution
We could implement the support for destilBERT, ALBERT or ELECTRA. They shouldn't be that different from other encoders. | https://github.com/huggingface/transformers/issues/28388 | open | [
"Feature request"
] | 2024-01-08T11:43:05Z | 2024-01-08T12:35:24Z | null | Bachstelze |
huggingface/alignment-handbook | 92 | Is there anyway that I can use learning rate warm-up during the training ? | I am using this repo to:
1. Continual Pre-training
2. SFT
3. DPR
For stage 1, I want to use a learning rate warm-up. | https://github.com/huggingface/alignment-handbook/issues/92 | closed | [] | 2024-01-07T21:07:25Z | 2024-01-10T06:48:52Z | 1 | shamanez |
huggingface/alignment-handbook | 91 | how to use dpo without flash-attention | Is there any flash-attention free version? | https://github.com/huggingface/alignment-handbook/issues/91 | open | [] | 2024-01-07T16:27:08Z | 2024-02-06T19:51:38Z | null | Fu-Dayuan |
huggingface/accelerate | 2,312 | Seeking for Help: how to work deepspeed zero stage 3 with quantized model? | Hi, I would like to conduct dpo training on my 2 a6000 (48GB) gpus based on this project (https://github.com/allenai/open-instruct). Specifically, the model was based on qlora and reference model was based on quantized one. I would like to utilize the deepspeed zero stage 3 to accelerate training time.
During the training process, I encountered errors related to the model and reference model integration with Deepspeed. Below is the relevant code snippet and the encountered error:
The model and reference model both were loaded with
```python
bnb_config = BitsAndBytesConfig(
load_in_8bit=True,
)
device_index = accelerator.local_process_index
device_map = {"": device_index} # force data-parallel training.
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
from_tf=bool(".ckpt" in model_name_or_path),
config=config,
load_in_8bit=True,
quantization_config=bnb_config,
torch_dtype=torch.bfloat16,
use_flash_attention_2=True if args.use_flash_attn else False,
)
reference_model = model
# some codes about coverting model to lora model...
def prepare_deepspeed(accelerator, model):
deepspeed_plugin = accelerator.state.deepspeed_plugin
config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
if model is not None:
if hasattr(model, "config"):
hidden_size = (
max(model.config.hidden_sizes)
if getattr(model.config, "hidden_sizes", None)
else getattr(model.config, "hidden_size", None)
)
if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
# Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
# This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
config_kwargs.update(
{
"zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
"zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
"zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
}
)
# If ZeRO-3 is used, we shard both the active and reference model.
# Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
if config_kwargs["zero_optimization"]["stage"] != 3:
config_kwargs["zero_optimization"]["stage"] = 0
model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
model.eval()
return model
reference_model = prepare_deepspeed(accelerator, reference_model)
```
```
File "/root/data1/tulu2/open-instruct/open-instruct-main/open_instruct/dpo_tune.py", line 692, in main
reference_model = prepare_deepspeed(accelerator, reference_model)
File "/root/data1/tulu2/open-instruct/open-instruct-main/open_instruct/dpo_tune.py", line 396, in prepare_deepspeed
model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
File "/conda/envs/tulu_dpo_env/lib/python3.10/site-packages/deepspeed/__init__.py", line 171, in initialize
engine = DeepSpeedEngine(args=args,
File "/conda/envs/tulu_dpo_env/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 259, in __init__
self._configure_distributed_model(model)
File "/conda/envs/tulu_dpo_env/lib/python3.10/site- | https://github.com/huggingface/accelerate/issues/2312 | closed | [] | 2024-01-07T09:44:28Z | 2024-01-11T11:01:31Z | null | grayground |
huggingface/datasets | 6,565 | `drop_last_batch=True` for IterableDataset map function is ignored with multiprocessing DataLoader | ### Describe the bug
Scenario:
- Interleaving two iterable datasets of unequal lengths (`all_exhausted`), followed by a batch mapping with batch size 2 to effectively merge the two datasets and get a sample from each dataset in a single batch, with `drop_last_batch=True` to skip the last batch in case it doesn't have two samples.
What works:
- Using DataLoader with `num_workers=0`
What does not work:
- Using DataLoader with `num_workers=1`, errors in the last batch.
Basically, `drop_last_batch=True` is ignored when using multiple dataloading workers.
Please take a look at the minimal repro script below.
### Steps to reproduce the bug
```python
from datasets import Dataset, interleave_datasets
from torch.utils.data import DataLoader
def merge_samples(batch):
assert len(batch['a']) == 2, "Batch size must be 2"
batch['c'] = [batch['a'][0]]
batch['d'] = [batch['a'][1]]
return batch
def gen1():
for ii in range(1, 8385):
yield {"a": ii}
def gen2():
for ii in range(1, 5302):
yield {"a": ii}
if __name__ == '__main__':
dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=1024)
dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=1024)
interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted")
mapped = interleaved.map(merge_samples, batched=True, batch_size=2, remove_columns=interleaved.column_names,
drop_last_batch=True)
# Works
loader = DataLoader(mapped, batch_size=32, num_workers=0)
i = 0
for b in loader:
print(i, b['c'].shape, b['d'].shape)
i += 1
print("DataLoader with num_workers=0 works")
# Doesn't work
loader = DataLoader(mapped, batch_size=32, num_workers=1)
i = 0
for b in loader:
print(i, b['c'].shape, b['d'].shape)
i += 1
```
### Expected behavior
`drop_last_batch=True` should have same behaviour for `num_workers=0` and `num_workers>=1`
### Environment info
- `datasets` version: 2.16.1
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.10.12
- `huggingface_hub` version: 0.20.2
- PyArrow version: 12.0.1
- Pandas version: 2.0.3
- `fsspec` version: 2023.6.0
I have also tested on Linux and got the same behavior. | https://github.com/huggingface/datasets/issues/6565 | closed | [] | 2024-01-07T02:46:50Z | 2025-03-08T09:46:05Z | 2 | naba89 |
huggingface/transformers.js | 505 | How do I use WebGL as executionProvider? | ### Question
```js
export const executionProviders = [
// 'webgpu',
'wasm'
];
```
I looked at src/backends/onnx.js and noticed that there was no webgl in the executionProviders.
Is there a way to use WebGL as executionProvider? | https://github.com/huggingface/transformers.js/issues/505 | closed | [
"question"
] | 2024-01-06T19:16:36Z | 2024-10-18T13:30:09Z | null | kwaroran |
huggingface/diffusers | 6,474 | how to use xformers | Maybe this is a relatively low-level question, but what always bothers me is how does Xformer run when running SD? Or can it be accelerated by default after installing this library? Thank you all for answering your questions | https://github.com/huggingface/diffusers/issues/6474 | closed | [] | 2024-01-06T03:34:16Z | 2024-01-11T03:38:19Z | null | babyta |
huggingface/datasets | 6,561 | Document YAML configuration with "data_dir" | See https://huggingface.co/datasets/uonlp/CulturaX/discussions/15#6597e83f185db94370d6bf50 for reference | https://github.com/huggingface/datasets/issues/6561 | open | [
"documentation"
] | 2024-01-05T14:03:33Z | 2025-08-07T14:57:58Z | 6 | severo |
huggingface/sentence-transformers | 2,397 | Does finetuning a cross-encoder yield prediction labels and not similarity scores? | Hi,
This is less of a coding issue and more of a conceptual question. I have binary labels for similarity and dissimilarity while training a cross-encoder; so its a binary classification task. The pretrained cross-encoder has a float score, most of the time around .5. After finetuning, the models only predict a decimal really close 0 or 1, which makes sense since the model is being trained for a binary classification task. But is is supposed to be a label prediction or a similarity score? Or is it limited to the type of data you have for training? | https://github.com/huggingface/sentence-transformers/issues/2397 | closed | [
"question"
] | 2024-01-04T21:01:44Z | 2024-01-09T17:53:17Z | null | FDSRashid |
huggingface/text-generation-inference | 1,403 | How to load llama-2 thru Client | ### System Info
Hi there, text_generation.__version__ = 0.6.0
### Information
- [ ] Docker
- [X] The CLI directly
### Tasks
- [ ] An officially supported command
- [ ] My own modifications
### Reproduction
I am trying to load llama-2 model thru Client
```
from text_generation import Client
model_endpoint = "https://api-inference.huggingface.co/models/meta-llama/Llama-2-7b-hf"
# model_endpoint = "https://api-inference.huggingface.co/models/tiiuae/falcon-7b-instruct"
# model_endpoint = "https://api-inference.huggingface.co/models/lmsys/vicuna-7b-v1.5"
client = Client(model_endpoint, timeout=60, headers={"Authorization": f"Bearer {token_auth}"})
generation: str = client.generate(
prompt="What is the capital city of British Columbia, Canada",
temperature=1,
top_p=0.9,
max_new_tokens=384,
stop_sequences=None,
).generated_text
```
### Expected behavior
However, this is an error:
> BadRequestError: Model requires a Pro subscription; check out hf.co/pricing to learn more. Make sure to include your HF token in your query.
Kindly ask any solutions ?
thanks. | https://github.com/huggingface/text-generation-inference/issues/1403 | closed | [] | 2024-01-04T17:25:59Z | 2024-01-05T16:01:56Z | null | yanan1116 |
huggingface/transformers | 28,343 | How to log custom value? | I want to log some info to `{'loss': 2.5234, 'learning_rate': 1.0344827586206896e-06, 'epoch': 0.0}`
how can i do that?
like: {'loss': 2.5234, 'learning_rate': 1.0344827586206896e-06, 'epoch': 0.0, 'version': 'v1'} | https://github.com/huggingface/transformers/issues/28343 | closed | [] | 2024-01-04T12:28:43Z | 2024-01-07T13:07:22Z | null | xmy0916 |
huggingface/transformers.js | 499 | An error occurred during model execution: "RangeError: offset is out of bounds". | ### Question
Hello - having an issue getting this code to run in the browser. Using `Xenova/TinyLlama-1.1B-Chat-v1.0` on `"@xenova/transformers": "^2.13.2"`
It runs perfectly in node.
```ts
import { pipeline } from '@xenova/transformers';
console.log('Loading model...');
const generator = await pipeline('text-generation', 'Xenova/TinyLlama-1.1B-Chat-v1.0');
console.log('Model loaded!');
const messages = [
{ role: 'system', content: 'You are a friendly Assistant' },
{ role: 'user', content: 'Explain JavaScript Scopes in simple terms' },
];
const prompt = generator.tokenizer.apply_chat_template(messages, {
tokenize: false,
add_generation_prompt: true,
});
console.log('Generating...');
const result = await generator(prompt, {
max_new_tokens: 256,
temperature: 0.5,
do_sample: true,
top_k: 50,
});
console.dir(result);
```
In Node it runs:
<img width="951" alt="Screenshot 2024-01-03 at 2 53 39 PM" src="https://github.com/xenova/transformers.js/assets/176013/4dfb556c-4605-4a19-b560-a52c07a28e5f">
But in the browser I see this:
<img width="1264" alt="Screenshot 2024-01-03 at 2 54 28 PM" src="https://github.com/xenova/transformers.js/assets/176013/899c803f-d311-4661-b3f9-ccd3e9c714d0">
Same issue in Firefox.
This issue seems to say it's memory: https://github.com/xenova/transformers.js/issues/8
Is this one too large to run in the browser? | https://github.com/huggingface/transformers.js/issues/499 | closed | [
"question"
] | 2024-01-03T19:55:45Z | 2024-10-18T13:30:09Z | null | wesbos |
huggingface/transformers.js | 497 | Cross Encoder | ### Question
I'm trying to run this pre-trained Cross Encoder model ([MS Marco TinyBERT](https://huggingface.co/cross-encoder/ms-marco-TinyBERT-L-2-v2)) not available in Transformers.js.
I've managed to convert it using the handy script, and I'm successfully running it with the "feature-extraction" task:
```js
const pairs = [
["How many people live in Berlin?", "Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers."],
[ "How many people live in Berlin?", "Berlin is well known for its museums."]
];
const model = await pipeline("feature-extraction", modelName);
const out = await model(pairs[0]);
console.log(Array.from(out.data)) // [-8.387903213500977, -9.811422348022461]
```
But I'm trying to run it as a Cross Encoder model as it's intended to, like the Python [example code](https://www.sbert.net/docs/pretrained-models/ce-msmarco.html?highlight=cross%20encoder):
```python
from sentence_transformers import CrossEncoder
model_name = 'cross-encoder/ms-marco-TinyBERT-L-2-v2'
model = CrossEncoder(model_name, max_length=512)
scores = model.predict([
('How many people live in Berlin?', 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'),
('How many people live in Berlin?', 'Berlin is well known for its museums.')
])
print(scores) // [ 7.1523685 -6.2870455]
```
How can I infer a similarity score from two sentences?
PS: if there are existing models/techniques for sentence similarity I'll take it! | https://github.com/huggingface/transformers.js/issues/497 | closed | [
"question"
] | 2024-01-03T16:24:37Z | 2024-03-01T00:11:31Z | null | achrafash |
huggingface/autotrain-advanced | 448 | What is the difference between autotrain and kohya_ss? | What is the difference between autotrain and kohya_ss?
| https://github.com/huggingface/autotrain-advanced/issues/448 | closed | [
"stale"
] | 2024-01-03T16:18:58Z | 2024-01-22T15:01:45Z | null | loboere |
huggingface/optimum | 1,622 | device set bug | ### System Info
```shell
optimum 1.16.1
```
### Who can help?
@philschmid
### Information
- [X] The official example scripts
- [ ] 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 (minimal, reproducible, runnable)
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_id = "facebook/opt-125m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
quantization_config = GPTQConfig(bits=4, dataset=["c4", "c4", "c4"], tokenizer=tokenizer)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda:5", quantization_config=quantization_config)
print()
### Expected behavior
optimum/gptq/quantizer.py line 429
data[k] = v.to(0)
Why is it fixed at 0? When setting device_map for the model, an error occurs that the input and model are not on the same device.
Is this a bug? | https://github.com/huggingface/optimum/issues/1622 | open | [
"bug"
] | 2024-01-03T09:01:16Z | 2024-01-09T10:17:45Z | 1 | Yuang-Deng |
huggingface/transformers.js | 494 | in-browser inference slower than node inference to be expected? | ### Question
i noticed that i get much higher performance when i run inference in node vs in the browser (latest chrome, m2 mac, ). is that generally to be expected? for context - i'm creating embeddings for chunks of text using the gte-small model.
thank you! | https://github.com/huggingface/transformers.js/issues/494 | closed | [
"question"
] | 2024-01-03T04:26:47Z | 2024-08-27T23:53:36Z | null | carlojoerges |
huggingface/optimum | 1,621 | Cannot convert sentence transformer model properly | ### System Info
```shell
Optimum Version = 1.16.1
```
### Who can help?
@michaelbenayoun
@fxmarty
### 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 (minimal, reproducible, runnable)
When running:
`optimum-cli export onnx -m sentence-transformers/distiluse-base-multilingual-cased-v2 --task feature-extraction ./models/distiluse-base-multilingual-cased-v2`
I get:
```
...
The ONNX export succeeded with the warning: The exported ONNX model does not have the exact same outputs as what is provided in SentenceTransformersTransformerOnnxConfig. Difference: onnx::Shape_530, onnx::Shape_233, onnx::Shape_332, onnx::Shape_431, onnx::Shape_629, 764.
...
```
And afterwards when i try running the inference session with the generated .onnx model i get:
```
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Non-zero status code returned while running Expand node. Name:'/1/Expand' Status Message: invalid expand shape
```
It seems like the model is not being properly converted. I'm currently trying to figure out why exactly.
### Extra context:
- This pr seems to have added support to sentence-transformers models, maybe something is missing: https://github.com/huggingface/optimum/pull/1589
- To generate the runtime session error I used this script and changed the model names and exported model path: https://github.com/huggingface/optimum/issues/1519#issuecomment-1854780869
- The same error occurs using node.js onnx runtime, so I assume the model is not exported properly.
### Expected behavior
The model is exported properly and generates the same results as using Sentence transformers directly. | https://github.com/huggingface/optimum/issues/1621 | closed | [
"bug"
] | 2024-01-02T12:08:07Z | 2024-01-12T15:26:21Z | 4 | leodalcin |
huggingface/alignment-handbook | 87 | How can I config `loss_type`? | I want to change the **loss_type** into KTO or something else to test but I can't. Please show me the way. Thank you. | https://github.com/huggingface/alignment-handbook/issues/87 | closed | [] | 2024-01-02T11:54:34Z | 2024-01-10T13:41:19Z | 2 | hahuyhoang411 |
huggingface/datasets | 6,548 | Skip if a dataset has issues | ### Describe the bug
Hello everyone,
I'm using **load_datasets** from **huggingface** to download the datasets and I'm facing an issue, the download starts but it reaches some state and then fails with the following error:
Couldn't reach https://huggingface.co/datasets/wikimedia/wikipedia/resolve/4cb9b0d719291f1a10f96f67d609c5d442980dc9/20231101.ext/train-00000-of-00001.parquet
Failed to resolve \'huggingface.co\' ([Errno -3] Temporary failure in name resolution)"))')))
so I was wondering is there a parameter to be passed to load_dataset() to skip files that can't be downloaded??
### Steps to reproduce the bug
Parameter to be passed to load_dataset() of huggingface to skip files that can't be downloaded??
### Expected behavior
load_dataset() finishes without error
### Environment info
None | https://github.com/huggingface/datasets/issues/6548 | open | [] | 2023-12-31T12:41:26Z | 2024-01-02T10:33:17Z | 1 | hadianasliwa |
huggingface/transformers.js | 491 | Running tests locally fail | ### Question
When I git clone to my Mac, and run tests, I get a lot of errors:
```
● Models › Loading different architecture types › gpt2 (GPT2Model)
Could not locate file: "https://huggingface.co/gpt2/resolve/main/tokenizer_config.json".
239 |
240 | const message = ERROR_MAPPING[status] ?? `Error (${status}) occurred while trying to load file`;
> 241 | throw Error(`${message}: "${remoteURL}".`);
| ^
242 | }
243 |
244 | class FileCache {
at handleError (src/utils/hub.js:241:11)
at getModelFile (src/utils/hub.js:474:24)
at getModelJSON (src/utils/hub.js:575:18)
at async Promise.all (index 1)
at loadTokenizer (src/tokenizers.js:61:16)
at Function.from_pretrained (src/tokenizers.js:2465:20)
at Object.<anonymous> (tests/models.test.js:61:37)
```
And indeed, a lot of files don't actually exist, like in this case:
https://huggingface.co/gpt2/resolve/main/tokenizer_config.json
But I don't see this in the logs for your github actions, so i am confused. | https://github.com/huggingface/transformers.js/issues/491 | closed | [
"question"
] | 2023-12-30T02:12:35Z | 2024-10-18T13:30:11Z | null | sroussey |
huggingface/transformers.js | 490 | Is it possible to implement sentence splitting? | ### Question
Can this library be used to implement sentence splitting, possibly with tokenizers? | https://github.com/huggingface/transformers.js/issues/490 | closed | [
"question"
] | 2023-12-30T01:17:55Z | 2024-02-01T01:51:52Z | null | devfacet |
huggingface/transformers.js | 486 | Output different from sentence transformers | ### Question
Hello, i'm not sure if i'm doing something wrong, but the pooled outputs from sentence transformers and this library seem to be different.
The results are the same if I use `pooling: 'none'` in js and `output_value='token_embedding` in python.
I've seen some other similar issues, but this seems to be a different problem.
```js
const fs = require('fs');
class MyClassificationPipeline {
static task = 'feature-extraction';
static model = 'Xenova/distiluse-base-multilingual-cased-v2';
static instance = null;
static async getInstance(progress_callback = null) {
if (this.instance === null) {
// Dynamically import the Transformers.js library
let { pipeline, env } = await import('@xenova/transformers');
// NOTE: Uncomment this to change the cache directory
// env.cacheDir = './.cache';
this.instance = pipeline(this.task, this.model, { progress_callback, quantized: false });
}
return this.instance;
}
}
// Comment out this line if you don't want to start loading the model as soon as the server starts.
// If commented out, the model will be loaded when the first request is received (i.e,. lazily).
MyClassificationPipeline.getInstance();
async function main() {
const classifier = await MyClassificationPipeline.getInstance();
const res = await classifier('This is an example sentence', { pooling: 'mean', normalize:false });
fs.writeFileSync('./xenova-embedding.json', JSON.stringify(res.data, null, 2), 'utf-8');
}
main();
```
```python
import json
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v2')
embedding = model.encode("This is an example sentence")
with open('embeddings.json', 'w') as f:
json.dump(embedding.tolist(), f)
```
Am i missing something? | https://github.com/huggingface/transformers.js/issues/486 | closed | [
"question"
] | 2023-12-29T10:15:07Z | 2024-01-02T12:20:17Z | null | leodalcin |
huggingface/trl | 1,155 | What is the best way for the inference process in LORA in PEFT approach | Here is the SFTtrainer method i used for finetuning mistral
```
trainer = SFTTrainer(
model=peft_model,
train_dataset=data,
peft_config=peft_config,
dataset_text_field=" column name",
max_seq_length=3000,
tokenizer=tokenizer,
args=training_arguments,
packing=packing,
)
trainer.train()
```
I found different mechanisms for the finetuned model inference after PEFT based LORA finetuning
Method - 1
save adapter after completing training and then merge with base model then use for inference
```
trainer.model.save_pretrained("new_adapter_path")
from peft import PeftModel
finetuned_model = PeftModel.from_pretrained(base_model,
new_adapter_path,
torch_dtype=torch.float16,
is_trainable=False,
device_map="auto"
)
finetuned_model = finetuned_model.merge_and_unload()
```
Method - 2
save checkpoints during training and then use the checkpoint with the least loss
```
from peft import PeftModel
finetuned_model = PeftModel.from_pretrained(base_model,
"least loss checkpoint path",
torch_dtype=torch.float16,
is_trainable=False,
device_map="auto"
)
finetuned_model = finetuned_model.merge_and_unload()
```
Method - 3
same method with AutoPeftModelForCausalLM class
```
model = AutoPeftModelForCausalLM.from_pretrained(
"output directory checkpoint path",
low_cpu_mem_usage=True,
return_dict=True,
torch_dtype=torch.float16,
device_map="cuda")
finetuned_model = finetuned_model.merge_and_unload()
```
Method-4
AutoPeftModelForCausalLM class specifies the output folder without specifying a specific checkpoint
```
instruction_tuned_model = AutoPeftModelForCausalLM.from_pretrained(
training_args.output_dir,
torch_dtype=torch.bfloat16,
device_map = 'auto',
trust_remote_code=True,
)
finetuned_model = finetuned_model.merge_and_unload()
```
Method-5
All the above methods without merging
```
#finetuned_model = finetuned_model.merge_and_unload()
```
Which is the actual method I should follow for inference?
and when to use which method over another? | https://github.com/huggingface/trl/issues/1155 | closed | [] | 2023-12-29T09:51:23Z | 2024-02-10T15:05:12Z | null | pradeepdev-1995 |
huggingface/peft | 1,310 | What is the best way for the inference process in LORA in PEFT approach | ### Feature request
What is the best way for the inference process in LORA in PEFT approach
### Motivation
What is the best way for the inference process in LORA in PEFT approach
### Your contribution
Here is the SFTtrainer method i used for finetuning mistral
```
trainer = SFTTrainer(
model=peft_model,
train_dataset=data,
peft_config=peft_config,
dataset_text_field=" column name",
max_seq_length=3000,
tokenizer=tokenizer,
args=training_arguments,
packing=packing,
)
trainer.train()
```
I found different mechanisms for the finetuned model inference after PEFT based LORA finetuning
Method - 1
save adapter after completing training and then merge with base model then use for inference
```
trainer.model.save_pretrained("new_adapter_path")
from peft import PeftModel
finetuned_model = PeftModel.from_pretrained(base_model,
new_adapter_path,
torch_dtype=torch.float16,
is_trainable=False,
device_map="auto"
)
finetuned_model = finetuned_model.merge_and_unload()
```
Method - 2
save checkpoints during training and then use the checkpoint with the least loss
```
from peft import PeftModel
finetuned_model = PeftModel.from_pretrained(base_model,
"least loss checkpoint path",
torch_dtype=torch.float16,
is_trainable=False,
device_map="auto"
)
finetuned_model = finetuned_model.merge_and_unload()
```
Method - 3
same method with AutoPeftModelForCausalLM class
```
model = AutoPeftModelForCausalLM.from_pretrained(
"output directory checkpoint path",
low_cpu_mem_usage=True,
return_dict=True,
torch_dtype=torch.float16,
device_map="cuda")
finetuned_model = finetuned_model.merge_and_unload()
```
Method-4
AutoPeftModelForCausalLM class specifies the output folder without specifying a specific checkpoint
```
instruction_tuned_model = AutoPeftModelForCausalLM.from_pretrained(
training_args.output_dir,
torch_dtype=torch.bfloat16,
device_map = 'auto',
trust_remote_code=True,
)
finetuned_model = finetuned_model.merge_and_unload()
```
Method-5
All the above methods without merging
```
#finetuned_model = finetuned_model.merge_and_unload()
```
Which is the actual method I should follow for inference?
and when to use which method over another? | https://github.com/huggingface/peft/issues/1310 | closed | [] | 2023-12-29T09:49:55Z | 2024-01-02T15:31:23Z | null | pradeepdev-1995 |
huggingface/datasets | 6,542 | Datasets : wikipedia 20220301.en error | ### Describe the bug
When I used load_dataset to download this data set, the following error occurred. The main problem was that the target data did not exist.
### Steps to reproduce the bug
1.I tried downloading directly.
```python
wiki_dataset = load_dataset("wikipedia", "20220301.en")
```
An exception occurred
```
MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/
If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory).
Example of usage:
`load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')`
```
2.I modified the code as prompted.
```python
wiki_dataset = load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')
```
An exception occurred:
```
FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json
```
### Expected behavior
I searched in the parent directory of the corresponding URL, but there was no corresponding "20220301" directory.
I really need this data set and hope to provide a download method.
### Environment info
python 3.8
datasets 2.16.0
apache-beam 2.52.0
dill 0.3.7
| https://github.com/huggingface/datasets/issues/6542 | closed | [] | 2023-12-29T08:34:51Z | 2024-01-02T13:21:06Z | 2 | ppx666 |
huggingface/diffusers | 6,384 | How to map A1111 reference_only parameters into diffusers? | Thanks for the community to implement the reference_only functionality in A1111, but how can the parameters correspond to each other? I have tried to reproduce the effect of webui in the diffusers library, but I can't seem to do it. I'm using the StableDiffusionReferencePipeline community pipeline.
My questions are:
1. Is reference_only in A1111 equivalent to reference_attn=True, reference_adain=False?
2. Some parameters in A1111, such as starting control step, seem to have no corresponding parameters in the pipeline.
3. The style_fidelity in A111 seems to have significant differences compared to style_fidelity in A1111. | https://github.com/huggingface/diffusers/issues/6384 | closed | [
"stale"
] | 2023-12-29T08:16:15Z | 2024-01-28T15:29:43Z | null | Logos23333 |
huggingface/peft | 1,308 | How to check the gradients of lora layers when training a peft model | ### Feature request
when I trained a lora model like this
```python
model = get_peft_model(model, lora_config)
training(model,data)
```
How can I check the gradients of lora layers from a `peft` model ?
### Motivation
check gradients of lora layers from peft model during training
### Your contribution
ni | https://github.com/huggingface/peft/issues/1308 | closed | [] | 2023-12-29T04:26:10Z | 2024-01-05T04:55:41Z | null | stardusts-hj |
huggingface/transformers.js | 484 | TypeScript Pipline Types for different models? | ### Question
Is there a suggested way to get types for the different models? Right now after I create a pipline, like one of the following:
```
const segmenter = await pipeline('image-segmentation', 'Xenova/face-parsing');
// or
const extractor = await pipeline(`feature-extraction`, `Xenova/UAE-Large-V1`, {
quantized: true, // Set this to false to use the full (unquantized) model
});
```
All the methods and returned values are `(...args: any[]) => any` - finding it hard to work with the methods and returned values.
I realize each model returns different outputs, and I'm fairly new to the whole convertion process, but are these types kept somewhere in the Python or the json files with the model that could be used as typescript types?
Ideally `pipeline` would infer the types, but I'm also ok with importing (or generating the types myself) and using it as a generic:
```
const whateve = pipeline<ReturnType>(`task`, `model`)
``` | https://github.com/huggingface/transformers.js/issues/484 | closed | [
"question"
] | 2023-12-28T21:16:05Z | 2024-01-02T15:08:47Z | null | wesbos |
huggingface/optimum-neuron | 395 | How to use generate() with inputs_embeds | I hope this is the right place to ask this question. Let me know if I need to move to another repo.
Currently I'm using `NeuronModelForCausalLM`.
I have a use case where I need to be able to do the following:
1. Generate embedding tokens
2. Modify embedding tokens
3. Run inference from modified embedding tokens
I am able to do steps 1 & 2 currently using the following:
```
from optimum.neuron import NeuronModelForCausalLM
llama_model = NeuronModelForCausalLM.from_pretrained('aws-neuron/Llama-2-7b-chat-hf-seqlen-2048-bs-1')
embedded_tokens = llama_model.model.chkpt_model.model.embed_tokens(token_ids)
### Code to modify embedded_tokens
```
However, as far as I can tell, generation with these modified tokens is not possible with `llama_model.generate()`
When I use the 'input_embeds' keyword argument, and set `input_ids=None`, I get the following:
```
ValueError: The following `model_kwargs` are not used by the model: ['inputs_embeds']
```
If this is not possible with the NeuronModelForCausalLM.generate() currently, is there a way to work around this manually? If so, could you provide an example?
Thanks very much for your help! | https://github.com/huggingface/optimum-neuron/issues/395 | closed | [
"Stale"
] | 2023-12-28T18:28:28Z | 2024-10-31T08:04:57Z | null | liechtym |
huggingface/transformers.js | 483 | Unrecognized token '<' when running | ### Question
I downloaded the react translation example. When I start the app everything seems to render fine, but as soon as I press translate, nothing happens and I get this error in the console on the browser:
`Unhandled Promise Rejection: SyntaxError: JSON Parse error: Unrecognized token '<'`
I've gotten this same issue trying to run other models keeping things very basic as found here: https://huggingface.co/docs/transformers.js/pipelines
UPDATE: This error only happens in Safari, but it works fine in Chrome.
If I try to make the simplest example with react like in the tutorial link it fails in both chrome and safari | https://github.com/huggingface/transformers.js/issues/483 | closed | [
"question"
] | 2023-12-28T14:44:50Z | 2023-12-28T20:35:02Z | null | philg-204 |
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