# ValueError: You are trying to offload the whole model to the disk. Please use the \`disk\_offload\` function instead

**URL:** <https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687>\
**Category:** Beginners\
**Created:** [December 22, 2023, 4:37am UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687 "2023-12-22T04:37:31Z")\
**Posts on this page:** 10\
**Page:** 1

<div class="post-metadata">

**Author:** ![dhruvyadav8930](https://avatars.discourse-cdn.com/v4/letter/d/a698b9/32.png) [@dhruvyadav8930](https://discuss.huggingface.co/u/dhruvyadav8930)\
**Post date:** [December 22, 2023, 4:37am UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/1 "2023-12-22T04:37:31Z")

</div>

I am trying to run a GitHub project on my computer.  
  
[GitHub Repo that I am trying to run](https://github.com/suryanshgupta9933/Law-GPT)  
  
This is the code snippet that is causing errors.  
  
Steps I took for replicating the project are:

> 1. Cloned the repository.
> 2. Generated the Hugging Face access token
> 3. Added `offload_folder` and `offload_dict_state` after reading the Hugging Face guide to load huge models.

```auto
def load_llm():
    """
    Load the LLM
    """
    # Model ID
    repo_id = 'meta-llama/Llama-2-7b-chat-hf'
    login(token="hf_xxxxxxxx")
    # Load the model
    model = AutoModelForCausalLM.from_pretrained(
        repo_id,
        device_map='auto',
        load_in_4bit=False,
        token = True,
        offload_folder = r"C:\Users\DHRUV\Desktop\New folder\Law-GPT",
        offload_state_dict = True
    )

    # Load the tokenizer
    tokenizer = AutoTokenizer.from_pretrained(
        repo_id,
        use_fast=True
    )

    # Create pipeline
    pipe = pipeline(
        'text-generation',
        model=model,
        tokenizer=tokenizer,
        max_length=512
    )

    # Load the LLM
    llm = HuggingFacePipeline(pipeline=pipe)

    return llm

```

The Error I am facing, Please help:

```auto
Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.
Token is valid (permission: read).
Your token has been saved to C:\Users\DHRUV\.cache\huggingface\token
Login successful
Loading checkpoint shards: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "C:\Users\DHRUV\Desktop\New folder\Law-GPT\app.py", line 5, in <module>
    chain = qa_pipeline()
  File "C:\Users\DHRUV\Desktop\New folder\Law-GPT\utils.py", line 100, in qa_pipeline
    llm = load_llm()
  File "C:\Users\DHRUV\Desktop\New folder\Law-GPT\utils.py", line 44, in load_llm
    model = AutoModelForCausalLM.from_pretrained(
  File "C:\Users\DHRUV\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\transformers\models\auto\auto_factory.py", line 566, in from_pretrained
    return model_class.from_pretrained(
  File "C:\Users\DHRUV\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\transformers\modeling_utils.py", line 3773, in from_pretrained
    dispatch_model(model, **device_map_kwargs)
  File "C:\Users\DHRUV\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\accelerate\big_modeling.py", line 438, in dispatch_model
    raise ValueError(
ValueError: You are trying to offload the whole model to the disk. Please use the `disk_offload` function instead.

```

---

<div class="post-metadata">

**Author:** ![Tr3y](https://avatars.discourse-cdn.com/v4/letter/t/90ced4/32.png) [@Tr3y](https://discuss.huggingface.co/u/Tr3y)\
**Post date:** [December 29, 2023, 3:03pm UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/2 "2023-12-29T15:03:27Z")

</div>

> [@dhruvyadav8930](#):
>
> `File "C:\Users\DHRUV\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\accelerate\big_modeling.py", line 438, in dispatch_model`

I have the same issue trying to load. GPT-NeoXT-Chat-Base-20B  
Hopefully some one chimes in here. For now my best guess is to follow the instruction in the error message.

Find this file File “C:\Users\DHRUV\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9\_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\accelerate\big\_modeling.py”

on line 436 change : model.to(device)" to “model.disk\_offload(device)”

Let me know if that works. I’m using a colab notebook and struggling to get the file updated.

---

<div class="post-metadata">

**Author:** ![Tr3y](https://avatars.discourse-cdn.com/v4/letter/t/90ced4/32.png) [@Tr3y](https://discuss.huggingface.co/u/Tr3y)\
**Post date:** [December 31, 2023, 1:29am UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/4 "2023-12-31T01:29:48Z")

</div>

And that is not the answer. : (

---

<div class="post-metadata">

**Author:** ![dhruvyadav8930](https://avatars.discourse-cdn.com/v4/letter/d/a698b9/32.png) [@dhruvyadav8930](https://discuss.huggingface.co/u/dhruvyadav8930)\
**Post date:** [December 31, 2023, 9:47am UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/5 "2023-12-31T09:47:55Z")

</div>

Yeah, I tried it already. It was not working🥲

---

<div class="post-metadata">

**Author:** ![Tr3y](https://avatars.discourse-cdn.com/v4/letter/t/90ced4/32.png) [@Tr3y](https://discuss.huggingface.co/u/Tr3y)\
**Post date:** [December 31, 2023, 5:22pm UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/6 "2023-12-31T17:22:28Z")

</div>

I fixed it this morning. To resolve this, I made some modifications to the initialization method of my chat model. The modifications involve adding checks to determine if offloading to disk is necessary and using the `disk_offload` function accordingly, thereby resolving the `ValueError` .Here’s a comparison of the original and modified code with comments:

**Original Code - ChatModel init function** :

```auto
# Set device to GPU with specified id
device = torch.device('cuda', gpu_id)   
if max_memory is None:
    # Load model onto one device
    self._model = AutoModelForCausalLM.from_pretrained(
        model_name, torch_dtype=torch.float16, device_map="auto")
    self._model.to(device)
else:
    # Load model configuration
    config = AutoConfig.from_pretrained(model_name)
    with init_empty_weights():
        # Initialize model with empty weights
        model_from_conf = AutoModelForCausalLM.from_config(config)
    model_from_conf.tie_weights()
    # Create a device_map from max_memory
    device_map = infer_auto_device_map(
        model_from_conf, max_memory=max_memory, 
        no_split_module_classes=["GPTNeoXLayer"], dtype="float16"
    )
    # Load the model with the above device_map
    self._model = AutoModelForCausalLM.from_pretrained(
        model_name, device_map=device_map, offload_folder="offload",
        offload_state_dict=True, torch_dtype=torch.float16
    )
self._tokenizer = AutoTokenizer.from_pretrained(model_name)

```

**Modified Code - ChatModel init function** :

```auto
# Selects GPU if available, else CPU
device = torch.device('cuda', gpu_id) if torch.cuda.is_available() else torch.device('cpu')  
# Load model configuration
config = AutoConfig.from_pretrained(model_name)  
with init_empty_weights():
    # Initialize model with empty weights
    self._model = AutoModelForCausalLM.from_config(config)  
# Create device map based on memory constraints
device_map = infer_auto_device_map(
    self._model, max_memory=max_memory, no_split_module_classes=["GPTNeoXLayer"], dtype="float16"
)  
# Determine if offloading is needed
needs_offloading = any(device == 'disk' for device in device_map.values())  
if needs_offloading:
    # Load model for offloading
    self._model = AutoModelForCausalLM.from_pretrained(
        model_name, device_map=device_map, offload_folder="offload",
        offload_state_dict=True, torch_dtype=torch.float16
    )  
    offload_directory = "../offload/"
    # Offload model to disk
    disk_offload(model=self._model, offload_dir=offload_directory)  
else:
    # Load model normally to specified device
    self._model = AutoModelForCausalLM.from_pretrained(
        model_name, torch_dtype=torch.float16
    )
    self._model.to(device)  
# Initialize tokenizer
self._tokenizer = AutoTokenizer.from_pretrained(model_name)  

```

---

<div class="post-metadata">

**Author:** ![dhruvyadav8930](https://avatars.discourse-cdn.com/v4/letter/d/a698b9/32.png) [@dhruvyadav8930](https://discuss.huggingface.co/u/dhruvyadav8930)\
**Post date:** [January 1, 2024, 12:45am UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/7 "2024-01-01T00:45:38Z")

</div>

Thanks for the reply, will try it.  
Also, Happy New Year brother.

---

<div class="post-metadata">

**Author:** ![Tr3y](https://avatars.discourse-cdn.com/v4/letter/t/90ced4/32.png) [@Tr3y](https://discuss.huggingface.co/u/Tr3y)\
**Post date:** [January 1, 2024, 3:19am UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/8 "2024-01-01T03:19:39Z")

</div>

Same to you! 🎆

---

<div class="post-metadata">

**Author:** ![anivader](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/anivader/32/42843_2.png) [@anivader](https://discuss.huggingface.co/u/anivader)\
**Post date:** [March 9, 2025, 7:33pm UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/9 "2025-03-09T19:33:13Z")

</div>

Has this been resolved? Trying to download `Llama-3.1-70B-Instruct` (total mem: ~150GiB) locally and was facing the same issue. I was able to fix this by pushing the entire model into disk memory:

```auto
device_map = infer_auto_device_map(
            model,
            max_memory={"disk": "150GiB"}, # Adjust based on available RAM
            no_split_module_classes=["LlamaDecoderLayer"] # Ensures correct model partitioning
        )

```

However, when I run the script below -

```auto
def download_model(model_name: str, output_dir: str, use_auth: bool = False, force: bool = False):
    if model_name not in MODEL_CONFIGS:
        raise ValueError(f"Model {model_name} not found in configurations")
    
    config = MODEL_CONFIGS[model_name]
    model_id = config["huggingface_id"]
    local_path = config["path"]

    # Offload directory for large models
    offload_path = os.path.join(local_path, "offload")
    
    # Check if model already exists
    if os.path.exists(local_path):
        if not force:
            print(f"\n✓ {config['name']} already exists at {local_path}")
            return
        else:
            print(f"\nForce flag set - removing existing model at {local_path}")
            shutil.rmtree(local_path)
    
    os.makedirs(offload_path, exist_ok=True)

    # Create output directory
    model_output_dir = os.path.join(output_dir, model_name)
    os.makedirs(model_output_dir, exist_ok=True)

    try:
        # Download tokenizer
        print("Downloading tokenizer...")
        tokenizer = AutoTokenizer.from_pretrained(
            model_id,
            use_fast=True,
            trust_remote_code=True
        )
        tokenizer.save_pretrained(local_path)
        print("✓ Tokenizer downloaded successfully")
        
        # Download model with disk offloading
        print("\nDownloading model...")
        
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.float16, # Use float16 to reduce memory usage
            trust_remote_code=True
        )

        # Automatically offload large model parts to disk
        device_map = infer_auto_device_map(
            model,
            max_memory={"disk": "150GiB"},  
            no_split_module_classes=["LlamaDecoderLayer"] # Ensures correct model partitioning
        )

        # Dispatch model to offload components to disk
        model = dispatch_model(
            model,
            device_map=device_map,
            offload_dir=offload_path, # Offload excess parts to disk
            offload_state_dict=True # Ensures early offloading
        )

        # Save model after successful loading
        model.save_pretrained(
            local_path,
            safe_serialization=True,
            max_shard_size="2GB"
        )
        print("✓ Model downloaded successfully")

        print(f"\n✓ {config['name']} has been downloaded and saved to {local_path}")

    except Exception as e:
        print(f"\n❌ Error downloading {config['name']}: {str(e)}")
        if "401" in str(e) and not use_auth:
            print("\nTip: This model might require authentication. Try running with --use-auth")
        raise

```

I get this error msg -

```auto
Downloading shards: 100%|████████████████████████████████████████████████| 30/30 [26:55<00:00, 53.85s/it]
Loading checkpoint shards: 33%|█████████████▋ | 10/30 [02:00<04:31, 13.60s/it]zsh: killed python Codes/download_models.py --models llama-70b --output-dir Models
/opt/miniconda3/lib/python3.12/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '

```

Apparently, I’m still running into a bunch of OOM issues. No idea what’s going on.

Some info -

- Before running the script, I had \>300GiB free disk memory
- After the model is downloaded into cache, this reduced to ~160GiB
- NO other applications were running

---

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [March 10, 2025, 5:46am UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/10 "2025-03-10T05:46:21Z")

</div>

Like this?

```py
def download_model(model_name: str, output_dir: str, use_auth: bool = False, force: bool = False):
    if model_name not in MODEL_CONFIGS:
        raise ValueError(f"Model {model_name} not found in configurations")
    
    config = MODEL_CONFIGS[model_name]
    model_id = config["huggingface_id"]
    local_path = config["path"]

    # Offload directory for large models
    offload_path = os.path.join(local_path, "offload")
    
    # Check if model already exists
    if os.path.exists(local_path):
        if not force:
            print(f"\n✓ {config['name']} already exists at {local_path}")
            return
        else:
            print(f"\nForce flag set - removing existing model at {local_path}")
            shutil.rmtree(local_path)
    
    os.makedirs(offload_path, exist_ok=True)

    # Create output directory
    model_output_dir = os.path.join(output_dir, model_name)
    os.makedirs(model_output_dir, exist_ok=True)

    try:
        # Download tokenizer
        print("Downloading tokenizer...")
        tokenizer = AutoTokenizer.from_pretrained(
            model_id,
            use_fast=True,
            trust_remote_code=True
        )
        tokenizer.save_pretrained(local_path)
        print("✓ Tokenizer downloaded successfully")

        # Load model configuration
        config = AutoConfig.from_pretrained(model_id)  
        with init_empty_weights():
            # Initialize model with empty weights
            self._model = AutoModelForCausalLM.from_config(config)  
 
        # Automatically offload large model parts to disk
        device_map = infer_auto_device_map(
            model,
            max_memory={"disk": "150GiB"},  
            no_split_module_classes=["LlamaDecoderLayer"] # Ensures correct model partitioning
        )
        
        # Download model with disk offloading
        print("\nDownloading model...")
        
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.float16, # Use float16 to reduce memory usage
            device_map=device_map, # <= Without this, 150 GiB of RAM will be consumed during loading
            offload_folder=offload_path,
            offload_state_dict=True, 
            trust_remote_code=True
        )

        # Dispatch model to offload components to disk
        model = dispatch_model(
            model,
            device_map=device_map,
            offload_dir=offload_path, # Offload excess parts to disk
            offload_state_dict=True # Ensures early offloading
        )

        # Save model after successful loading
        model.save_pretrained(
            local_path,
            safe_serialization=True,
            max_shard_size="2GB"
        )
        print("✓ Model downloaded successfully")

        print(f"\n✓ {config['name']} has been downloaded and saved to {local_path}")

    except Exception as e:
        print(f"\n❌ Error downloading {config['name']}: {str(e)}")
        if "401" in str(e) and not use_auth:
            print("\nTip: This model might require authentication. Try running with --use-auth")
        raise

```

---

<div class="post-metadata">

**Author:** ![anivader](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/anivader/32/42843_2.png) [@anivader](https://discuss.huggingface.co/u/anivader)\
**Post date:** [March 11, 2025, 4:42pm UTC](https://discuss.huggingface.co/t/valueerror-you-are-trying-to-offload-the-whole-model-to-the-disk-please-use-the-disk-offload-function-instead/66687/11 "2025-03-11T16:42:19Z")

</div>

Turns out a lot of model files were getting pushed into my RAM from `.cache`, which caused the crash. I fixed it by downloading the model to cache and just storing it there. You can then `rsync` to wherever you want to run inference.

```auto
def download_model_to_cache(model_name: str):
    config = MODEL_CONFIGS[model_name]
    model_id = config["huggingface_id"]
    
    try:
        # Download full model snapshot to cache
        snapshot_download(repo_id=model_id, local_dir=None)
        print("\n✓ Model successfully downloaded to cache!")

    except Exception as e:
        print(f"\n❌ Error downloading {model_id}: {str(e)}")
        raise

```