ijwatson98 commited on Mar 13

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README.md CHANGED Viewed

@@ -1,201 +1,51 @@
 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
+license: mit
+base_model: gpt2-medium
+tags:
+- generated_from_trainer
+model-index:
+- name: sft-gpt2-xsum
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# sft-gpt2-xsum
+This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on an unknown dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 1
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 8
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 0.1
+- num_epochs: 4
+### Framework versions
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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+I0313 18:46:44.823169       1 log_spam.go:50] Built on Dec 31 1969 16:00:00 -0800 (0)
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+I0313 18:46:44.823169       1 log_spam.go:50] Built on Dec 31 1969 16:00:00 -0800 (0)
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+I0313 18:46:44.823199       1 log_spam.go:51] Command line arguments:
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+I0313 19:27:35.741089       1 log_spam.go:49] Current timezone is UTC (currently UTC +00:00)
+I0313 19:27:35.741108       1 log_spam.go:50] Built on Dec 31 1969 16:00:00 -0800 (0)
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+I0313 19:27:35.741112       1 log_spam.go:51] Command line arguments:
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+Binary: Built at redacted@redacted:.
+Binary: Built with gc go1.23-20240219-RC05 cl/608389183 +aaf8e844ed X:fieldtrack,boringcrypto for linux/amd64
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+I0313 19:04:13.294363       1 log_spam.go:47] Current working directory /datalab/web
+I0313 19:04:13.294375       1 log_spam.go:49] Current timezone is UTC (currently UTC +00:00)
+I0313 19:04:13.294435       1 log_spam.go:50] Built on Dec 31 1969 16:00:00 -0800 (0)
+ at redacted@redacted:.
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+ with gc go1.23-20240219-RC05 cl/608389183 +aaf8e844ed X:fieldtrack,boringcrypto for linux/amd64
+ from changelist 0 in a unknown client based on redacted
+Build tool: unknown
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+I0313 19:04:13.294441       1 log_spam.go:51] Command line arguments:
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+I0313 19:04:13.294449       1 log_spam.go:53]  argv[1]: '--lsp_search_dirs=/datalab/web/pyright/typeshed-fallback/stdlib,/usr/local/lib/python3.10/dist-packages'
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+I0313 19:04:13.294467       1 log_spam.go:53]  argv[6]: '/datalab/web/pyright/pyright-langserver.js'
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+Binary: Built at redacted@redacted:.
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+I0313 19:04:18.307809       1 log_spam.go:49] Current timezone is UTC (currently UTC +00:00)
+I0313 19:04:18.307865       1 log_spam.go:50] Built on Dec 31 1969 16:00:00 -0800 (0)
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+I0313 19:04:18.307881       1 log_spam.go:51] Command line arguments:
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+I0313 19:04:18.307917       1 log_spam.go:53]  argv[6]: '/datalab/web/pyright/pyright-langserver.js'
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+I0313 19:27:31.116533       1 log_spam.go:50] Built on Dec 31 1969 16:00:00 -0800 (0)
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+I0313 19:27:31.116539       1 log_spam.go:51] Command line arguments:
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+I0313 19:27:31.116573       1 log_spam.go:53]  argv[6]: '/datalab/web/pyright/pyright-langserver.js'
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language_service.eda5a8b22d76.root.log.INFO.20240313-192735.10431 ADDED Viewed

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+I0313 19:27:35.741089       1 log_spam.go:49] Current timezone is UTC (currently UTC +00:00)
+I0313 19:27:35.741108       1 log_spam.go:50] Built on Dec 31 1969 16:00:00 -0800 (0)
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+I0313 19:27:35.741112       1 log_spam.go:51] Command line arguments:
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tmp7juqs11u/__pycache__/_remote_module_non_scriptable.cpython-310.pyc ADDED Viewed

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tmp7juqs11u/_remote_module_non_scriptable.py ADDED Viewed

	@@ -0,0 +1,81 @@

+from typing import *
+import torch
+import torch.distributed.rpc as rpc
+from torch import Tensor
+from torch._jit_internal import Future
+from torch.distributed.rpc import RRef
+from typing import Tuple  # pyre-ignore: unused import
+module_interface_cls = None
+def forward_async(self, *args, **kwargs):
+    args = (self.module_rref, self.device, self.is_device_map_set, *args)
+    kwargs = {**kwargs}
+    return rpc.rpc_async(
+        self.module_rref.owner(),
+        _remote_forward,
+        args,
+        kwargs,
+    )
+def forward(self, *args, **kwargs):
+    args = (self.module_rref, self.device, self.is_device_map_set, *args)
+    kwargs = {**kwargs}
+    ret_fut = rpc.rpc_async(
+        self.module_rref.owner(),
+        _remote_forward,
+        args,
+        kwargs,
+    )
+    return ret_fut.wait()
+_generated_methods = [
+    forward_async,
+    forward,
+]
+def _remote_forward(
+    module_rref: RRef[module_interface_cls], device: str, is_device_map_set: bool, *args, **kwargs):
+    module = module_rref.local_value()
+    device = torch.device(device)
+    if device.type != "cuda":
+        return module.forward(*args, **kwargs)
+    # If the module is on a cuda device,
+    # move any CPU tensor in args or kwargs to the same cuda device.
+    # Since torch script does not support generator expression,
+    # have to use concatenation instead of
+    # ``tuple(i.to(device) if isinstance(i, Tensor) else i for i in *args)``.
+    args = (*args,)
+    out_args: Tuple[()] = ()
+    for arg in args:
+        arg = (arg.to(device),) if isinstance(arg, Tensor) else (arg,)
+        out_args = out_args + arg
+    kwargs = {**kwargs}
+    for k, v in kwargs.items():
+        if isinstance(v, Tensor):
+            kwargs[k] = kwargs[k].to(device)
+    if is_device_map_set:
+        return module.forward(*out_args, **kwargs)
+    # If the device map is empty, then only CPU tensors are allowed to send over wire,
+    # so have to move any GPU tensor to CPU in the output.
+    # Since torch script does not support generator expression,
+    # have to use concatenation instead of
+    # ``tuple(i.cpu() if isinstance(i, Tensor) else i for i in module.forward(*out_args, **kwargs))``.
+    ret: Tuple[()] = ()
+    for i in module.forward(*out_args, **kwargs):
+        i = (i.cpu(),) if isinstance(i, Tensor) else (i,)
+        ret = ret + i
+    return ret

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