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its normal. you need to duplicate the space to your own user to make it work. check out the docs: hf.co/docs/autotrain

Thank you very much for the reply.

That's exactly what I've already done (duplicating the space).

Here is a screenshot of my space with the proper settings to train the model and the loaded dataset :

And here are the logs I receive once I create the project:

INFO task_type: LLM Finetuning
INFO model_choice: HuggingFace Hub
INFO Updating hub model choices for task: lm_training, model_choice: HuggingFace Hub
Traceback (most recent call last):
File "/app/env/lib/python3.9/site-packages/gradio/routes.py", line 442, in run_predict
output = await app.get_blocks().process_api(
File "/app/env/lib/python3.9/site-packages/gradio/blocks.py", line 1392, in process_api
result = await self.call_function(
File "/app/env/lib/python3.9/site-packages/gradio/blocks.py", line 1097, in call_function
prediction = await anyio.to_thread.run_sync(
File "/app/env/lib/python3.9/site-packages/anyio/to_thread.py", line 33, in run_sync
return await get_asynclib().run_sync_in_worker_thread(
File "/app/env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
return await future
File "/app/env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 807, in run
result = context.run(func, *args)
File "/app/env/lib/python3.9/site-packages/gradio/utils.py", line 703, in wrapper
response = f(*args, **kwargs)
File "/app/src/autotrain/app.py", line 170, in _update_col_map
data_cols = pd.read_csv(training_data[0].name, nrows=2).columns.tolist()
TypeError: 'NoneType' object is not subscriptable
INFO Estimating costs....
INFO model_choice: HuggingFace Hub
INFO Updating hub model choices for task: lm_training, model_choice: HuggingFace Hub
INFO Estimating costs....
INFO Estimating costs....
INFO Estimating costs....
INFO Estimating number of samples
INFO Estimating costs for: num_models: 3, task: lm_training, num_samples: 3996
INFO Getting project cost...
INFO Sending GET request to https://api.autotrain.huggingface.co/pricing/compute?username=JordanLaforet&task_id=9&num_samples=3996&num_models=3
INFO Estimated_cost: 0
INFO 🚨🚨🚨Creating project: h1fq-m1lc-mxul
INFO 🚨Task: lm_training
INFO 🚨Training data: [<tempfile._TemporaryFileWrapper object at 0x7f6e8e13c850>]
INFO 🚨Validation data: None
INFO 🚨Training params: [{"hub_model": "meta-llama/Llama-2-7b-hf", "num_models": 3}]
INFO 🚨Hub model: meta-llama/Llama-2-7b-hf
INFO 🚨Estimated cost: 0.0
INFO 🚨:Can pay: False
INFO Dataset: h1fq-m1lc-mxul (lm_training)
Train data: ['/tmp/gradio/552a0fe9ac975ff47df0e51f77a96d2ef40f8e6a/Dataset.csv']
Valid data: []
Column mapping: {'text': 'training_data'}

Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00<?, ?it/s]

Creating parquet from Arrow format: 0%| | 0/4 [00:00<?, ?ba/s]
Creating parquet from Arrow format: 100%|██████████| 4/4 [00:00<00:00, 656.93ba/s]

Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:00<00:00, 2.97it/s]
Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:00<00:00, 2.97it/s]

Pushing dataset shards to the dataset hub: 0%| | 0/1 [00:00<?, ?it/s]

Creating parquet from Arrow format: 0%| | 0/1 [00:00<?, ?ba/s]
Creating parquet from Arrow format: 100%|██████████| 1/1 [00:00<00:00, 712.95ba/s]

Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:00<00:00, 5.17it/s]
Pushing dataset shards to the dataset hub: 100%|██████████| 1/1 [00:00<00:00, 5.17it/s]

Downloading metadata: 0%| | 0.00/466 [00:00<?, ?B/s]
Downloading metadata: 100%|██████████| 466/466 [00:00<00:00, 7.73MB/s]

INFO 🚀🚀🚀 Creating project h1fq-m1lc-mxul, task: lm_training
INFO 🚀 Using username: JordanLaforet
INFO 🚀 Using param_choice: autotrain
INFO 🚀 Using hub_model: meta-llama/Llama-2-7b-hf
INFO 🚀 Using job_params: [{'hub_model': 'meta-llama/Llama-2-7b-hf', 'num_models': 3, 'task': 'lm_training'}]
INFO 🚀 Creating project h1fq-m1lc-mxul, task: lm_training
INFO 🚀 Creating project with payload: {'username': 'JordanLaforet', 'proj_name': 'h1fq-m1lc-mxul', 'task': 9, 'config': {'advanced': True, 'autotrain': True, 'language': 'unk', 'max_models': 3, 'hub_model': 'meta-llama/Llama-2-7b-hf', 'params': [{'hub_model': 'meta-llama/Llama-2-7b-hf', 'task': 'lm_training'}]}}
INFO 🚀 Creating project with payload: {'username': 'JordanLaforet', 'proj_name': 'h1fq-m1lc-mxul', 'task': 9, 'config': {'advanced': True, 'autotrain': True, 'language': 'unk', 'max_models': 3, 'hub_model': 'meta-llama/Llama-2-7b-hf', 'params': [{'hub_model': 'meta-llama/Llama-2-7b-hf', 'task': 'lm_training'}]}}
INFO Sending POST request to https://api.autotrain.huggingface.co/projects/create
INFO Sending POST request to https://api.autotrain.huggingface.co/projects/81027/data/start_processing
INFO ⏳ Waiting for data processing to complete ...
INFO Sending GET request to https://api.autotrain.huggingface.co/projects/81027
INFO Sending GET request to https://api.autotrain.huggingface.co/projects/81027
INFO ✅ Data processing complete!
INFO 🚀 Approving project # 81027
INFO Sending POST request to https://api.autotrain.huggingface.co/projects/81027/start_training
Traceback (most recent call last):
File "/app/env/lib/python3.9/site-packages/gradio/routes.py", line 442, in run_predict
output = await app.get_blocks().process_api(
File "/app/env/lib/python3.9/site-packages/gradio/blocks.py", line 1392, in process_api
result = await self.call_function(
File "/app/env/lib/python3.9/site-packages/gradio/blocks.py", line 1097, in call_function
prediction = await anyio.to_thread.run_sync(
File "/app/env/lib/python3.9/site-packages/anyio/to_thread.py", line 33, in run_sync
return await get_asynclib().run_sync_in_worker_thread(
File "/app/env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 877, in run_sync_in_worker_thread
return await future
File "/app/env/lib/python3.9/site-packages/anyio/_backends/_asyncio.py", line 807, in run
result = context.run(func, *args)
File "/app/env/lib/python3.9/site-packages/gradio/utils.py", line 703, in wrapper
response = f(*args, **kwargs)
File "/app/src/autotrain/app.py", line 503, in _create_project
project.approve(project_id)
File "/app/src/autotrain/project.py", line 201, in approve
_ = http_post(
File "/app/src/autotrain/utils.py", line 94, in http_post
response.raise_for_status()
File "/app/env/lib/python3.9/site-packages/requests/models.py", line 1021, in raise_for_status
raise HTTPError(http_error_msg, response=self)
requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://api.autotrain.huggingface.co/projects/81027/start_training

It looks like the '/start_processing' step runs fine, but the '/start_training' step encounters an issue when making a request to the API.
Being fairly new to Hugging Face, I actually have no idea what the problem is so any assistance you could provide would be greatly appreciated !

Thank you all and have a great day !

@abhishek showing error

ImportError: Using bitsandbytes 8-bit quantization requires Accelerate: pip install accelerate and the latest version of bitsandbytes: pip install -i https://pypi.org/simple/ bitsandbytes

ERROR | 2024-04-20 12:05:11 | autotrain.trainers.common:wrapper:117 - Using bitsandbytes 8-bit quantization requires Accelerate: pip install accelerate and the latest version of bitsandbytes: pip install -i https://pypi.org/simple/ bitsandbytes
INFO | 2024-04-20 12:05:14 | autotrain.app_utils:get_running_jobs:28 - Killing PID: 103