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|---|---|---|---|---|---|---|---|---|---|
SADO1102/Towa_sama001 | SADO1102 | "2024-06-12T03:08:56Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T03:05:54Z" | ---
license: openrail
---
|
kavithr/gemma-7b-it-chatbot | kavithr | "2024-06-12T03:07:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T03:07:25Z" | Entry not found |
zwonakazwonakazwonaka/Zwonaka | zwonakazwonakazwonaka | "2024-06-12T03:12:24Z" | 0 | 0 | null | [
"license:unknown",
"region:us"
] | null | "2024-06-12T03:07:29Z" | ---
license: unknown
---
|
nlpresearcher/llama-mala-4bit | nlpresearcher | "2024-06-12T03:09:30Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-12T03:07:46Z" | Entry not found |
TTTXXX01/zephyr-7b-Cal-DPO0001-full | TTTXXX01 | "2024-06-12T03:09:20Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T03:09:20Z" | Entry not found |
smcleod/meta-llama-3-lora-smcleod-golang-ollama-charm | smcleod | "2024-06-12T03:09:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T03:09:34Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** smcleod
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
solidrust/Llama-3-8B-AWQ | solidrust | "2024-06-12T03:32:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"awq",
"region:us"
] | text-generation | "2024-06-12T03:10:38Z" | ---
library_name: transformers
tags:
- 4-bit
- AWQ
- text-generation
- autotrain_compatible
- endpoints_compatible
pipeline_tag: text-generation
inference: false
quantized_by: Suparious
---
# AI-Sweden-Models/Llama-3-8B AWQ
- Model creator: [AI-Sweden-Models](https://huggingface.co/AI-Sweden-Models)
- Original model: [Llama-3-8B](https://huggingface.co/AI-Sweden-Models/Llama-3-8B)
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/Llama-3-8B-AWQ"
system_message = "You are Llama-3-8B, incarnated as a powerful AI. You were created by AI-Sweden-Models."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
|
sunyuan/readme_1 | sunyuan | "2024-06-12T03:14:03Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T03:14:03Z" | Entry not found |
panckypenck/MikeATandT | panckypenck | "2024-06-12T03:18:48Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T03:15:35Z" | ---
license: openrail
---
|
Coolwowsocoolwow/Im_Gonna_Fly_Some_Planes | Coolwowsocoolwow | "2024-06-12T03:28:56Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T03:16:54Z" | ---
license: openrail
---
|
Augusto777/swin-tiny-patch4-window7-224-ve-U11-b-80 | Augusto777 | "2024-06-12T04:17:59Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T03:19:15Z" | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-ve-U11-b-80
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.782608695652174
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-ve-U11-b-80
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7088
- Accuracy: 0.7826
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 1.3860 | 0.1304 |
| 1.3859 | 2.0 | 13 | 1.3832 | 0.2609 |
| 1.3859 | 2.92 | 19 | 1.3773 | 0.2609 |
| 1.3791 | 4.0 | 26 | 1.3569 | 0.2174 |
| 1.3347 | 4.92 | 32 | 1.3177 | 0.2609 |
| 1.3347 | 6.0 | 39 | 1.2093 | 0.3913 |
| 1.2088 | 6.92 | 45 | 1.1083 | 0.4348 |
| 1.0456 | 8.0 | 52 | 1.0340 | 0.4565 |
| 1.0456 | 8.92 | 58 | 1.0120 | 0.5 |
| 0.9278 | 10.0 | 65 | 0.9282 | 0.5652 |
| 0.847 | 10.92 | 71 | 0.9934 | 0.5217 |
| 0.847 | 12.0 | 78 | 1.0171 | 0.4783 |
| 0.7142 | 12.92 | 84 | 0.8889 | 0.5870 |
| 0.5959 | 14.0 | 91 | 0.9392 | 0.5870 |
| 0.5959 | 14.92 | 97 | 0.9018 | 0.6304 |
| 0.5344 | 16.0 | 104 | 0.8327 | 0.6739 |
| 0.4438 | 16.92 | 110 | 0.7308 | 0.7391 |
| 0.4438 | 18.0 | 117 | 0.6834 | 0.7174 |
| 0.4419 | 18.92 | 123 | 0.7909 | 0.6304 |
| 0.3989 | 20.0 | 130 | 0.9103 | 0.6739 |
| 0.3989 | 20.92 | 136 | 0.7534 | 0.7391 |
| 0.3534 | 22.0 | 143 | 0.8043 | 0.7391 |
| 0.3534 | 22.92 | 149 | 0.7648 | 0.7174 |
| 0.3265 | 24.0 | 156 | 0.7088 | 0.7826 |
| 0.2808 | 24.92 | 162 | 0.8845 | 0.6957 |
| 0.2808 | 26.0 | 169 | 0.7756 | 0.7609 |
| 0.2753 | 26.92 | 175 | 0.9944 | 0.6087 |
| 0.2837 | 28.0 | 182 | 0.8091 | 0.7174 |
| 0.2837 | 28.92 | 188 | 0.9966 | 0.6739 |
| 0.2667 | 30.0 | 195 | 0.7711 | 0.7826 |
| 0.2325 | 30.92 | 201 | 0.8946 | 0.6957 |
| 0.2325 | 32.0 | 208 | 0.9079 | 0.6739 |
| 0.2096 | 32.92 | 214 | 1.0338 | 0.6522 |
| 0.1733 | 34.0 | 221 | 0.8191 | 0.7391 |
| 0.1733 | 34.92 | 227 | 1.0068 | 0.6957 |
| 0.1975 | 36.0 | 234 | 0.8644 | 0.7174 |
| 0.1844 | 36.92 | 240 | 0.8682 | 0.6739 |
| 0.1844 | 38.0 | 247 | 0.7915 | 0.7609 |
| 0.1701 | 38.92 | 253 | 0.7554 | 0.7609 |
| 0.1696 | 40.0 | 260 | 0.8762 | 0.7174 |
| 0.1696 | 40.92 | 266 | 1.0173 | 0.6739 |
| 0.1556 | 42.0 | 273 | 0.9080 | 0.7174 |
| 0.1556 | 42.92 | 279 | 1.2456 | 0.6739 |
| 0.153 | 44.0 | 286 | 0.9820 | 0.7391 |
| 0.1343 | 44.92 | 292 | 0.9908 | 0.7174 |
| 0.1343 | 46.0 | 299 | 0.9435 | 0.7391 |
| 0.1513 | 46.92 | 305 | 0.8842 | 0.7826 |
| 0.1402 | 48.0 | 312 | 1.0207 | 0.6739 |
| 0.1402 | 48.92 | 318 | 0.9915 | 0.7174 |
| 0.1648 | 50.0 | 325 | 1.1576 | 0.6739 |
| 0.1047 | 50.92 | 331 | 1.2283 | 0.6739 |
| 0.1047 | 52.0 | 338 | 1.0869 | 0.6957 |
| 0.1223 | 52.92 | 344 | 1.1203 | 0.7174 |
| 0.1223 | 54.0 | 351 | 0.9685 | 0.7174 |
| 0.1223 | 54.92 | 357 | 1.1926 | 0.7174 |
| 0.1236 | 56.0 | 364 | 1.0088 | 0.7174 |
| 0.1115 | 56.92 | 370 | 0.9149 | 0.7391 |
| 0.1115 | 58.0 | 377 | 0.8820 | 0.7391 |
| 0.1173 | 58.92 | 383 | 0.9653 | 0.7391 |
| 0.102 | 60.0 | 390 | 1.0046 | 0.7174 |
| 0.102 | 60.92 | 396 | 1.0585 | 0.6957 |
| 0.1206 | 62.0 | 403 | 1.0490 | 0.6957 |
| 0.1206 | 62.92 | 409 | 0.9683 | 0.7609 |
| 0.1124 | 64.0 | 416 | 0.9627 | 0.7609 |
| 0.0927 | 64.92 | 422 | 0.9771 | 0.7609 |
| 0.0927 | 66.0 | 429 | 1.0002 | 0.7174 |
| 0.0906 | 66.92 | 435 | 0.9607 | 0.7391 |
| 0.084 | 68.0 | 442 | 0.9414 | 0.7391 |
| 0.084 | 68.92 | 448 | 0.9863 | 0.7174 |
| 0.0866 | 70.0 | 455 | 0.9930 | 0.7174 |
| 0.0944 | 70.92 | 461 | 0.9981 | 0.7174 |
| 0.0944 | 72.0 | 468 | 1.0039 | 0.7174 |
| 0.1064 | 72.92 | 474 | 0.9987 | 0.7174 |
| 0.1074 | 73.85 | 480 | 0.9964 | 0.7174 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
richardkelly/Qwen-Qwen1.5-1.8B-1718162409 | richardkelly | "2024-06-12T05:03:32Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T03:20:09Z" | ---
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] |
nhatanhtran/CUSCTokenizer | nhatanhtran | "2024-06-12T03:22:12Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T03:22:12Z" | Entry not found |
hykim12/lives_Models | hykim12 | "2024-06-12T03:31:13Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2024-06-12T03:30:05Z" | ---
license: other
license_name: livesmodels
license_link: LICENSE
---
|
Augusto777/swin-tiny-patch4-window7-224-ve-U11-b-12 | Augusto777 | "2024-06-12T03:35:49Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T03:33:40Z" | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-ve-U11-b-12
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5434782608695652
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-ve-U11-b-12
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9473
- Accuracy: 0.5435
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 1.3839 | 0.1304 |
| 1.3821 | 2.0 | 13 | 1.3524 | 0.2391 |
| 1.3821 | 2.92 | 19 | 1.2898 | 0.3043 |
| 1.2875 | 4.0 | 26 | 1.1721 | 0.4348 |
| 1.1072 | 4.92 | 32 | 1.1018 | 0.4348 |
| 1.1072 | 6.0 | 39 | 1.0327 | 0.4783 |
| 0.9941 | 6.92 | 45 | 0.9920 | 0.4565 |
| 0.9132 | 8.0 | 52 | 0.9473 | 0.5435 |
| 0.9132 | 8.92 | 58 | 0.9522 | 0.5217 |
| 0.849 | 10.0 | 65 | 0.9478 | 0.5217 |
| 0.8124 | 10.92 | 71 | 0.9506 | 0.5217 |
| 0.8124 | 11.08 | 72 | 0.9505 | 0.5217 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
1earner1/my_mistral | 1earner1 | "2024-06-12T03:35:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T03:35:19Z" | ---
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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
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## 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. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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onizukal/Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold5 | onizukal | "2024-06-12T04:06:48Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T03:36:15Z" | ---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8418549346016647
---
<!-- 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. -->
# Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold5
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5966
- Accuracy: 0.8419
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3221 | 1.0 | 632 | 0.4174 | 0.8201 |
| 0.3949 | 2.0 | 1264 | 0.4102 | 0.8236 |
| 0.2149 | 3.0 | 1896 | 0.5617 | 0.8403 |
| 0.1037 | 4.0 | 2528 | 0.7488 | 0.8411 |
| 0.0902 | 5.0 | 3160 | 0.9759 | 0.8339 |
| 0.0457 | 6.0 | 3792 | 1.2673 | 0.8411 |
| 0.0051 | 7.0 | 4424 | 1.4733 | 0.8403 |
| 0.0063 | 8.0 | 5056 | 1.5705 | 0.8272 |
| 0.0 | 9.0 | 5688 | 1.5778 | 0.8446 |
| 0.0 | 10.0 | 6320 | 1.5966 | 0.8419 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
icefog72/IceCocoaRP-7b-6.5bpw-exl2 | icefog72 | "2024-06-12T03:58:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"alpaca",
"not-for-all-audiences",
"nsfw",
"conversational",
"arxiv:2306.01708",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T03:36:57Z" | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
- alpaca
- mistral
- not-for-all-audiences
- nsfw
license: cc-by-nc-4.0
---
# IceCocoaRP-7b-6.5bpw-exl2
6.5bpw-exl2 quant of [icefog72/IceCocoaRP-7b](https://huggingface.co/icefog72/IceCocoaRP-7b)
[SillyTavern Discord thread](https://discord.com/channels/1100685673633153084/1248929887570628688)
[Rules-lorebook and settings I'm using you can find here](https://huggingface.co/icefog72/GeneralInfoToStoreNotModel/tree/main)
[ko-fi](https://ko-fi.com/icefog72)
## Merge Details
The best one so far for me.
- [4.2bpw-exl2](https://huggingface.co/icefog72/IceCocoaRP-7b-4.2bpw-exl2)
- [6.5bpw-exl2](https://huggingface.co/icefog72/IceCocoaRP-7b-6.5bpw-exl2)
- [8bpw-exl2](https://huggingface.co/icefog72/IceCocoaRP-7b-8bpw-exl2)
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using NeuralBeagleJaskier as a base.
### Models Merged
The following models were included in the merge:
* NeuralBeagleJaskier
* IceBlendedCoffeeRP-7b (slerp bfloat16)
- IceCoffeeRP-7b
- IceBlendedLatteRP-7b base
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NeuralBeagleJaskier
parameters:
density: 0.9
weight: 0.5
- model: IceBlendedCoffeeRP-7b
parameters:
density: 0.5
weight: 0.3
merge_method: ties
base_model: NeuralBeagleJaskier
parameters:
normalize: true
int8_mask: true
dtype: float16
```
|
Danielrahmai1991/llama3_model_v2 | Danielrahmai1991 | "2024-06-12T03:38:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T03:37:56Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Danielrahmai1991
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Augusto777/swin-tiny-patch4-window7-224-ve-U11-b-40 | Augusto777 | "2024-06-12T03:44:31Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T03:38:54Z" | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-ve-U11-b-40
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8260869565217391
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-ve-U11-b-40
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6121
- Accuracy: 0.8261
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 1.5799 | 0.4783 |
| 2.1773 | 2.0 | 13 | 1.5648 | 0.3478 |
| 2.1773 | 2.92 | 19 | 1.5182 | 0.3261 |
| 2.1773 | 4.0 | 26 | 1.4232 | 0.3261 |
| 1.8993 | 4.92 | 32 | 1.3505 | 0.3913 |
| 1.8993 | 6.0 | 39 | 1.2747 | 0.3696 |
| 1.5045 | 6.92 | 45 | 1.2452 | 0.3696 |
| 1.2431 | 8.0 | 52 | 1.1982 | 0.2826 |
| 1.2431 | 8.92 | 58 | 1.2112 | 0.3043 |
| 1.1225 | 10.0 | 65 | 1.0160 | 0.5 |
| 0.9942 | 10.92 | 71 | 1.0138 | 0.4783 |
| 0.9942 | 12.0 | 78 | 0.9094 | 0.5652 |
| 0.9212 | 12.92 | 84 | 0.8860 | 0.5217 |
| 0.816 | 14.0 | 91 | 0.7693 | 0.6739 |
| 0.816 | 14.92 | 97 | 0.8290 | 0.6304 |
| 0.741 | 16.0 | 104 | 0.7810 | 0.6739 |
| 0.631 | 16.92 | 110 | 0.6342 | 0.7826 |
| 0.631 | 18.0 | 117 | 0.7677 | 0.6957 |
| 0.6402 | 18.92 | 123 | 0.6283 | 0.7391 |
| 0.5477 | 20.0 | 130 | 0.6687 | 0.7174 |
| 0.5477 | 20.92 | 136 | 0.6369 | 0.7826 |
| 0.5023 | 22.0 | 143 | 0.6334 | 0.7609 |
| 0.5023 | 22.92 | 149 | 0.6355 | 0.8043 |
| 0.4802 | 24.0 | 156 | 0.5976 | 0.8043 |
| 0.4336 | 24.92 | 162 | 0.6112 | 0.7609 |
| 0.4336 | 26.0 | 169 | 0.6148 | 0.8043 |
| 0.4203 | 26.92 | 175 | 0.6380 | 0.7391 |
| 0.429 | 28.0 | 182 | 0.6032 | 0.8043 |
| 0.429 | 28.92 | 188 | 0.6348 | 0.7391 |
| 0.4013 | 30.0 | 195 | 0.6121 | 0.8261 |
| 0.3747 | 30.92 | 201 | 0.6521 | 0.7391 |
| 0.3747 | 32.0 | 208 | 0.6424 | 0.7609 |
| 0.3668 | 32.92 | 214 | 0.6149 | 0.8261 |
| 0.3287 | 34.0 | 221 | 0.6426 | 0.7826 |
| 0.3287 | 34.92 | 227 | 0.6379 | 0.8043 |
| 0.372 | 36.0 | 234 | 0.6435 | 0.8043 |
| 0.3236 | 36.92 | 240 | 0.6450 | 0.8043 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
PhillipGuo/hp-lat-llama-PCA-epsilon0.0-pgd_layer8_16-def_layer0-wikitext-57 | PhillipGuo | "2024-06-12T03:42:24Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T03:42:15Z" | ---
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]
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## Uses
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### Direct Use
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### 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] |
wffcyrus/Chattts-Mix | wffcyrus | "2024-06-12T03:43:18Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T03:43:18Z" | Entry not found |
onizukal/Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold3 | onizukal | "2024-06-12T22:47:45Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T03:45:47Z" | ---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.83495670995671
---
<!-- 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. -->
# Boya1_3Class_Adamax_1e4_20Epoch_Beit-large-224_fold3
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2799
- Accuracy: 0.8350
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3841 | 1.0 | 923 | 0.4388 | 0.8203 |
| 0.235 | 2.0 | 1846 | 0.4317 | 0.8325 |
| 0.17 | 3.0 | 2769 | 0.5680 | 0.8374 |
| 0.0174 | 4.0 | 3692 | 1.0467 | 0.8287 |
| 0.0016 | 5.0 | 4615 | 1.2799 | 0.8350 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
PhillipGuo/hp-lat-llama-PCA-epsilon0.0-pgd_layer8_16-def_layer0-wikitext-58 | PhillipGuo | "2024-06-12T03:46:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T03:45:54Z" | ---
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:**
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[More Information Needed] |
BriannaHa/distilhubert-finetuned-gtzan | BriannaHa | "2024-06-12T05:27:09Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | "2024-06-12T03:46:59Z" | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6099
- Accuracy: 0.82
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9296 | 1.0 | 113 | 1.8410 | 0.49 |
| 1.2441 | 2.0 | 226 | 1.2808 | 0.64 |
| 1.0149 | 3.0 | 339 | 0.9687 | 0.71 |
| 0.6823 | 4.0 | 452 | 0.8456 | 0.73 |
| 0.5596 | 5.0 | 565 | 0.6905 | 0.83 |
| 0.4261 | 6.0 | 678 | 0.5846 | 0.84 |
| 0.2587 | 7.0 | 791 | 0.5405 | 0.83 |
| 0.1584 | 8.0 | 904 | 0.6447 | 0.81 |
| 0.1291 | 9.0 | 1017 | 0.6127 | 0.82 |
| 0.1009 | 10.0 | 1130 | 0.6099 | 0.82 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
hlzhang109/CoLoR-filter | hlzhang109 | "2024-06-15T19:21:30Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T03:47:49Z" | # CoLoR-filter
See accompanying code at: https://github.com/davidbrandfonbrener/color-filter-olmo
If you only want to download the filtered, untokenized data, see: https://huggingface.co/datasets/davidbrandfonbrener/color-filtered-c4
## Usage
To download the data, we recommend using the huggingface-cli.
To download all the data, run `huggingface-cli download hlzhang109/CoLoR-filter --local-dir YOUR_PATH`.
This will download the data to your huggingface cache and create a local-dir with symbolic links to the data. If you actually want the data at `YOUR_PATH`, set it as the `--cache-dir` in the command.
WARNING: the data is large since it contains a copy of tokenized C4 to ensure that the selected data indices match with the tokenized raw data. The C4 data is ~300GB and the rest of the repo is ~50GB of which ~45GB is the 1.2B model and optimizer checkpoints.
If you only want to download some files (e.g. just the models), use the cli. For example, `huggingface-cli download hlzhang109/CoLoR-filter --local-dir YOUR_PATH --include "models/*"`.
## Citation
If you use this code in your research, please cite the following paper:
```bibtex
@article{brandfonbrener2024color,
title={CoLoR-Filter: Conditional Loss Reduction Filtering for Targeted Language Model Pre-training},
author={Brandfonbrener, David and Zhang, Hanlin and Kirsch, Andreas and Schwarz, Jonathan Richard and Kakade, Sham M},
journal={arXiv preprint arXiv:XXXX.XXXXX},
year={2024}
}
``` |
VampeeHuntee/xlm-roberta-large_baseline_words | VampeeHuntee | "2024-06-12T04:13:13Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"vi",
"base_model:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2024-06-12T03:47:54Z" | ---
language:
- vi
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-large_baseline_words
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. -->
# xlm-roberta-large_baseline_words
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the covid19_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0850
- Patient Id: 0.9840
- Name: 0.7711
- Gender: 0.9767
- Age: 0.9821
- Job: 0.8062
- Location: 0.9570
- Organization: 0.8784
- Date: 0.9869
- Symptom And Disease: 0.8688
- Transportation: 1.0
- F1 Macro: 0.9211
- F1 Micro: 0.9459
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Patient Id | Name | Gender | Age | Job | Location | Organization | Date | Symptom And Disease | Transportation | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:------:|:------:|:------:|:--------:|:------------:|:------:|:-------------------:|:--------------:|:--------:|:--------:|
| 0.2002 | 1.0 | 629 | 0.1212 | 0.9160 | 0.8349 | 0.8346 | 0.8122 | 0.5385 | 0.8769 | 0.7201 | 0.9553 | 0.7769 | 0.8587 | 0.8124 | 0.8586 |
| 0.0557 | 2.0 | 1258 | 0.1025 | 0.9594 | 0.8836 | 0.9533 | 0.9731 | 0.2841 | 0.9228 | 0.8301 | 0.9842 | 0.8545 | 0.9444 | 0.8590 | 0.9191 |
| 0.038 | 3.0 | 1887 | 0.0804 | 0.9741 | 0.7154 | 0.9732 | 0.9821 | 0.7615 | 0.9372 | 0.8576 | 0.9869 | 0.8461 | 0.9943 | 0.9028 | 0.9309 |
| 0.0222 | 4.0 | 2516 | 0.0862 | 0.9871 | 0.5567 | 0.9691 | 0.9862 | 0.8207 | 0.9553 | 0.8707 | 0.9847 | 0.8740 | 1.0 | 0.9005 | 0.9398 |
| 0.0148 | 5.0 | 3145 | 0.0850 | 0.9840 | 0.7711 | 0.9767 | 0.9821 | 0.8062 | 0.9570 | 0.8784 | 0.9869 | 0.8688 | 1.0 | 0.9211 | 0.9459 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
PhillipGuo/hp-lat-llama-PCA-epsilon0.0-pgd_layer8_16-def_layer0-wikitext-59 | PhillipGuo | "2024-06-12T03:48:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T03:47:56Z" | ---
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. -->
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- **Paper [optional]:** [More Information Needed]
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## 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
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### 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
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[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]
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[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]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
hsan512/lora_finetune_ver3 | hsan512 | "2024-06-12T09:01:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T03:48:09Z" | ---
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
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#### 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] |
VampeeHuntee/xlm-roberta-base_baseline_words | VampeeHuntee | "2024-06-12T03:59:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"vi",
"base_model:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2024-06-12T03:48:49Z" | ---
language:
- vi
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_baseline_words
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. -->
# xlm-roberta-base_baseline_words
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the covid19_ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0873
- Patient Id: 0.9883
- Name: 0.9446
- Gender: 0.9785
- Age: 0.9765
- Job: 0.7063
- Location: 0.9538
- Organization: 0.8807
- Date: 0.9851
- Symptom And Disease: 0.8886
- Transportation: 1.0
- F1 Macro: 0.9302
- F1 Micro: 0.9499
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Patient Id | Name | Gender | Age | Job | Location | Organization | Date | Symptom And Disease | Transportation | F1 Macro | F1 Micro |
|:-------------:|:-----:|:----:|:---------------:|:----------:|:------:|:------:|:------:|:------:|:--------:|:------------:|:------:|:-------------------:|:--------------:|:--------:|:--------:|
| 0.2425 | 1.0 | 629 | 0.0989 | 0.9661 | 0.8889 | 0.8030 | 0.9068 | 0.3358 | 0.9152 | 0.8045 | 0.9843 | 0.8291 | 0.9222 | 0.8356 | 0.9001 |
| 0.0596 | 2.0 | 1258 | 0.0885 | 0.9807 | 0.9446 | 0.9283 | 0.9620 | 0.4786 | 0.9462 | 0.8665 | 0.9810 | 0.8690 | 0.9885 | 0.8945 | 0.9367 |
| 0.0376 | 3.0 | 1887 | 0.0899 | 0.9828 | 0.9284 | 0.9483 | 0.9765 | 0.6406 | 0.9458 | 0.8720 | 0.9865 | 0.8783 | 1.0 | 0.9159 | 0.9423 |
| 0.0257 | 4.0 | 2516 | 0.0919 | 0.9875 | 0.9362 | 0.9766 | 0.9805 | 0.6818 | 0.9505 | 0.8827 | 0.9869 | 0.8871 | 1.0 | 0.9270 | 0.9484 |
| 0.0172 | 5.0 | 3145 | 0.0873 | 0.9883 | 0.9446 | 0.9785 | 0.9765 | 0.7063 | 0.9538 | 0.8807 | 0.9851 | 0.8886 | 1.0 | 0.9302 | 0.9499 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
Augusto777/swin-tiny-patch4-window7-224-ve-U11-b-60 | Augusto777 | "2024-06-12T03:57:34Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T03:49:38Z" | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-ve-U11-b-60
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8043478260869565
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-ve-U11-b-60
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7230
- Accuracy: 0.8043
## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 1.3859 | 0.1304 |
| 1.3858 | 2.0 | 13 | 1.3818 | 0.2609 |
| 1.3858 | 2.92 | 19 | 1.3723 | 0.2609 |
| 1.3747 | 4.0 | 26 | 1.3355 | 0.2174 |
| 1.3001 | 4.92 | 32 | 1.2625 | 0.3696 |
| 1.3001 | 6.0 | 39 | 1.1306 | 0.4565 |
| 1.141 | 6.92 | 45 | 1.0510 | 0.4783 |
| 0.9784 | 8.0 | 52 | 0.9585 | 0.5435 |
| 0.9784 | 8.92 | 58 | 0.9895 | 0.4783 |
| 0.8533 | 10.0 | 65 | 0.9512 | 0.5 |
| 0.7564 | 10.92 | 71 | 0.9522 | 0.5217 |
| 0.7564 | 12.0 | 78 | 0.9144 | 0.5 |
| 0.6735 | 12.92 | 84 | 0.9070 | 0.6087 |
| 0.5919 | 14.0 | 91 | 0.7915 | 0.6522 |
| 0.5919 | 14.92 | 97 | 0.7989 | 0.6522 |
| 0.504 | 16.0 | 104 | 0.9510 | 0.6522 |
| 0.4422 | 16.92 | 110 | 0.8196 | 0.6739 |
| 0.4422 | 18.0 | 117 | 0.6629 | 0.7609 |
| 0.4031 | 18.92 | 123 | 0.8767 | 0.6522 |
| 0.3752 | 20.0 | 130 | 0.8253 | 0.6739 |
| 0.3752 | 20.92 | 136 | 0.7183 | 0.7391 |
| 0.3424 | 22.0 | 143 | 0.8852 | 0.6739 |
| 0.3424 | 22.92 | 149 | 0.7360 | 0.7391 |
| 0.3293 | 24.0 | 156 | 0.7230 | 0.8043 |
| 0.2822 | 24.92 | 162 | 0.8271 | 0.6957 |
| 0.2822 | 26.0 | 169 | 0.7443 | 0.8043 |
| 0.2623 | 26.92 | 175 | 0.9371 | 0.6739 |
| 0.2807 | 28.0 | 182 | 0.7392 | 0.7391 |
| 0.2807 | 28.92 | 188 | 0.8754 | 0.6739 |
| 0.223 | 30.0 | 195 | 0.7146 | 0.7826 |
| 0.2185 | 30.92 | 201 | 0.7702 | 0.7391 |
| 0.2185 | 32.0 | 208 | 0.7330 | 0.7174 |
| 0.2157 | 32.92 | 214 | 0.8817 | 0.6957 |
| 0.2011 | 34.0 | 221 | 0.7460 | 0.7174 |
| 0.2011 | 34.92 | 227 | 0.9663 | 0.6739 |
| 0.2204 | 36.0 | 234 | 0.8056 | 0.7174 |
| 0.1856 | 36.92 | 240 | 0.7799 | 0.7174 |
| 0.1856 | 38.0 | 247 | 0.8410 | 0.6957 |
| 0.1678 | 38.92 | 253 | 0.7334 | 0.7391 |
| 0.1682 | 40.0 | 260 | 0.8508 | 0.6957 |
| 0.1682 | 40.92 | 266 | 0.8106 | 0.6957 |
| 0.1638 | 42.0 | 273 | 0.8403 | 0.7174 |
| 0.1638 | 42.92 | 279 | 0.9157 | 0.6957 |
| 0.1573 | 44.0 | 286 | 0.9271 | 0.7391 |
| 0.1476 | 44.92 | 292 | 0.9167 | 0.7174 |
| 0.1476 | 46.0 | 299 | 0.9309 | 0.7174 |
| 0.1466 | 46.92 | 305 | 0.8236 | 0.7826 |
| 0.1457 | 48.0 | 312 | 0.8835 | 0.7826 |
| 0.1457 | 48.92 | 318 | 0.9162 | 0.7391 |
| 0.1625 | 50.0 | 325 | 0.8969 | 0.7391 |
| 0.1163 | 50.92 | 331 | 0.9183 | 0.7391 |
| 0.1163 | 52.0 | 338 | 0.9173 | 0.7391 |
| 0.1375 | 52.92 | 344 | 0.8886 | 0.7609 |
| 0.1379 | 54.0 | 351 | 0.8771 | 0.7391 |
| 0.1379 | 54.92 | 357 | 0.8857 | 0.7391 |
| 0.1321 | 55.38 | 360 | 0.8884 | 0.7391 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
horuragi1/llm | horuragi1 | "2024-06-14T02:06:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T03:55:07Z" | Entry not found |
chainup244/Qwen-Qwen1.5-7B-1718164606 | chainup244 | "2024-06-12T04:00:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T03:56:51Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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circulus/on-canvers-real-v3.9.1 | circulus | "2024-06-12T04:02:01Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T03:58:03Z" | ---
license: gpl-3.0
---
|
circulus/on-canvers-story-v3.9.1 | circulus | "2024-06-12T04:02:10Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T03:58:12Z" | ---
license: gpl-3.0
---
|
circulus/on-canvers-anime-v3.9.1 | circulus | "2024-06-12T04:02:12Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T03:58:20Z" | ---
license: gpl-3.0
---
|
circulus/on-canvers-disney-v3.9.1 | circulus | "2024-06-12T04:01:33Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T03:58:26Z" | ---
license: gpl-3.0
---
|
hanifabdlh/mistral-7b-v0.2-alpaca-lora | hanifabdlh | "2024-06-12T04:00:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:00:51Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** hanifabdlh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Razer112/baddy | Razer112 | "2024-06-12T18:35:25Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T04:01:41Z" | ---
license: openrail
---
|
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer8_16-def_layer0-wikitext-57 | PhillipGuo | "2024-06-12T04:02:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:02:04Z" | ---
library_name: transformers
tags: []
---
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PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer8_16-def_layer0-wikitext-57 | PhillipGuo | "2024-06-12T04:02:25Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:02:15Z" | ---
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tags: []
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PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer8_16-def_layer0-wikitext-57 | PhillipGuo | "2024-06-12T04:02:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
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MOLYHECI/llava-13b-986MViT-cc1m | MOLYHECI | "2024-06-12T04:10:13Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T04:10:13Z" | ---
license: apache-2.0
---
|
PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer8_16-def_layer0-wikitext-57 | PhillipGuo | "2024-06-12T04:10:46Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:10:35Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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hanifabdlh/mistral-7b-v0.2-alpaca-unsloth | hanifabdlh | "2024-06-12T04:11:26Z" | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:11:26Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** hanifabdlh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer8_16-def_layer0-wikitext-58 | PhillipGuo | "2024-06-12T04:12:20Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:12:04Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer8_16-def_layer0-wikitext-58 | PhillipGuo | "2024-06-12T04:12:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:12:32Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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datek/Qwen-Qwen1.5-7B-1718165561 | datek | "2024-06-12T04:16:09Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T04:13:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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Isawany/ppo-LunarLander-v2 | Isawany | "2024-06-12T04:14:54Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-12T04:14:34Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 266.60 +/- 21.66
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
cwei13/bert-base-japanese-ghost_rate-weighted-0612 | cwei13 | "2024-06-12T04:14:50Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T04:14:50Z" | Entry not found |
Arodrigo/temp0 | Arodrigo | "2024-06-12T04:15:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:15:28Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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volodya-leveryev/wav2vec2-large-mms-1b-sakha-colab | volodya-leveryev | "2024-06-12T04:16:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T04:16:17Z" | Entry not found |
chainup244/google-gemma-2b-1718165961 | chainup244 | "2024-06-12T04:21:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T04:19:23Z" | ---
library_name: transformers
tags: []
---
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1earner1/my_mistral2 | 1earner1 | "2024-06-12T04:20:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:19:43Z" | ---
library_name: transformers
tags: []
---
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kietha/vietnamese-correction-v2-MK | kietha | "2024-06-12T04:20:22Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T04:20:22Z" | Entry not found |
PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer8_16-def_layer0-wikitext-58 | PhillipGuo | "2024-06-12T04:21:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:21:41Z" | ---
library_name: transformers
tags: []
---
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milkuris/TraceDefender | milkuris | "2024-06-12T04:22:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T04:22:02Z" | Entry not found |
Frinkles/LLama3Adapter | Frinkles | "2024-06-12T04:25:23Z" | 0 | 0 | null | [
"safetensors",
"license:mit",
"region:us"
] | null | "2024-06-12T04:24:23Z" | ---
license: mit
---
|
vbv373/test_codegen_mono_0611-outputs | vbv373 | "2024-06-12T05:34:31Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-12T04:25:50Z" | Entry not found |
Augusto777/vit-base-patch16-224-ve-U12-b-12 | Augusto777 | "2024-06-12T04:28:45Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T04:27:07Z" | Entry not found |
PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer8_16-def_layer0-wikitext-58 | PhillipGuo | "2024-06-12T04:27:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:27:29Z" | ---
library_name: transformers
tags: []
---
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PhillipGuo/hp-lat-llama-PCA-epsilon6.0-pgd_layer8_16-def_layer0-wikitext-59 | PhillipGuo | "2024-06-12T04:29:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:29:34Z" | ---
library_name: transformers
tags: []
---
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Adebolajo/phi3-4k-FP16-instruction-TRT-LLM | Adebolajo | "2024-06-12T04:34:54Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-12T04:30:17Z" | ---
license: mit
---
|
PhillipGuo/hp-lat-llama-PCA-epsilon1.5-pgd_layer8_16-def_layer0-wikitext-59 | PhillipGuo | "2024-06-12T04:31:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:30:53Z" | ---
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.
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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[More Information Needed]
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- **Hardware Type:** [More Information Needed]
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Augusto777/vit-base-patch16-224-ve-U12-b-24 | Augusto777 | "2024-06-12T04:39:32Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T04:31:48Z" | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-ve-U12-b-24
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8478260869565217
---
<!-- 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. -->
# vit-base-patch16-224-ve-U12-b-24
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6456
- Accuracy: 0.8478
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.92 | 6 | 1.3806 | 0.4130 |
| 1.379 | 2.0 | 13 | 1.3103 | 0.5435 |
| 1.379 | 2.92 | 19 | 1.2269 | 0.4130 |
| 1.2758 | 4.0 | 26 | 1.1412 | 0.4565 |
| 1.121 | 4.92 | 32 | 1.0650 | 0.4783 |
| 1.121 | 6.0 | 39 | 1.0084 | 0.5217 |
| 0.9871 | 6.92 | 45 | 0.9395 | 0.6522 |
| 0.8612 | 8.0 | 52 | 0.8798 | 0.7174 |
| 0.8612 | 8.92 | 58 | 0.8219 | 0.7391 |
| 0.7653 | 10.0 | 65 | 0.7712 | 0.7826 |
| 0.6674 | 10.92 | 71 | 0.7328 | 0.7609 |
| 0.6674 | 12.0 | 78 | 0.6968 | 0.7391 |
| 0.568 | 12.92 | 84 | 0.6456 | 0.8478 |
| 0.4723 | 14.0 | 91 | 0.6528 | 0.8043 |
| 0.4723 | 14.92 | 97 | 0.7107 | 0.6739 |
| 0.4256 | 16.0 | 104 | 0.6335 | 0.7609 |
| 0.3524 | 16.92 | 110 | 0.5953 | 0.8261 |
| 0.3524 | 18.0 | 117 | 0.5824 | 0.8261 |
| 0.3282 | 18.92 | 123 | 0.6329 | 0.7174 |
| 0.3074 | 20.0 | 130 | 0.5775 | 0.8043 |
| 0.3074 | 20.92 | 136 | 0.5770 | 0.8043 |
| 0.3076 | 22.0 | 143 | 0.5749 | 0.8261 |
| 0.3076 | 22.15 | 144 | 0.5747 | 0.8261 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
PhillipGuo/hp-lat-llama-PCA-epsilon3.0-pgd_layer8_16-def_layer0-wikitext-59 | PhillipGuo | "2024-06-12T04:31:59Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:31:49Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
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<!-- 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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PhillipGuo/hp-lat-llama-PCA-epsilon0.5-pgd_layer8_16-def_layer0-wikitext-59 | PhillipGuo | "2024-06-12T04:31:59Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:31:49Z" | ---
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]
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<!-- Provide the basic links for the model. -->
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- 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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
Wasue/lora_model | Wasue | "2024-06-12T05:43:33Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2024-06-12T04:32:58Z" | ---
{}
---
|
JiaxinGe/llama3_transformed_data_anthropic_dataset_transformed_400 | JiaxinGe | "2024-06-12T22:38:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:33:07Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** JiaxinGe
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JiaxinGe/llama3_transformed_data_anthropic_dataset_transformed_1k | JiaxinGe | "2024-06-12T07:05:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:34:09Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** JiaxinGe
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
JiaxinGe/llama3_transformed_data_anthropic_dataset_transformed_600 | JiaxinGe | "2024-06-12T23:14:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:35:30Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** JiaxinGe
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
orby-yanan/llava-v1.6-mistral-7b-high-resolution | orby-yanan | "2024-06-12T06:37:12Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:36:47Z" | ---
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]
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[More Information Needed]
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[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
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[More Information Needed]
## Training Details
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## 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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
JiaxinGe/llama3_transformed_data_anthropic_dataset_transformed_800 | JiaxinGe | "2024-06-13T01:04:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:38:44Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** JiaxinGe
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tranthaihoa/sbert_llama2_5k_evidence | tranthaihoa | "2024-06-12T04:39:15Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-2-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:39:01Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-2-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** tranthaihoa
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-7b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
datek/google-gemma-2b-1718167171 | datek | "2024-06-12T04:41:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T04:39:34Z" | ---
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]
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## 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
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[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. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
lalok/NectarfinetunningWhisper_repo | lalok | "2024-06-12T04:43:50Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:43:47Z" | ---
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] |
l3utterfly/llama3-8b-executorch-tokenizer-model | l3utterfly | "2024-06-12T04:44:42Z" | 0 | 0 | null | [
"license:llama3",
"region:us"
] | null | "2024-06-12T04:44:17Z" | ---
license: llama3
---
|
anvuong/ddpm-butterflies-128 | anvuong | "2024-06-12T05:13:27Z" | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | "2024-06-12T04:46:04Z" | Entry not found |
Coolwowsocoolwow/Mr_Worthless | Coolwowsocoolwow | "2024-06-12T04:54:05Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-12T04:48:49Z" | ---
license: openrail
---
|
onizukal/Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold5 | onizukal | "2024-06-12T05:30:23Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-12T04:49:01Z" | ---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8631752125034274
---
<!-- 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. -->
# Boya2_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold5
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6377
- Accuracy: 0.8632
## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4668 | 1.0 | 914 | 0.3852 | 0.8385 |
| 0.2261 | 2.0 | 1828 | 0.3623 | 0.8645 |
| 0.1143 | 3.0 | 2742 | 0.5165 | 0.8580 |
| 0.2149 | 4.0 | 3656 | 0.7493 | 0.8626 |
| 0.1035 | 5.0 | 4570 | 1.0893 | 0.8607 |
| 0.0224 | 6.0 | 5484 | 1.3211 | 0.8582 |
| 0.0055 | 7.0 | 6398 | 1.5211 | 0.8604 |
| 0.0001 | 8.0 | 7312 | 1.6383 | 0.8563 |
| 0.0001 | 9.0 | 8226 | 1.6304 | 0.8678 |
| 0.0 | 10.0 | 9140 | 1.6377 | 0.8632 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
Arbi-Houssem/TunLangModel_test1.9 | Arbi-Houssem | "2024-06-12T04:50:23Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"dataset:Arbi-Houssem/Tunisian_dataset_STT-TTS15s_filtred1.0",
"base_model:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-12T04:49:50Z" | ---
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- Arbi-Houssem/Tunisian_dataset_STT-TTS15s_filtred1.0
metrics:
- wer
model-index:
- name: Whisper Tunisien
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Tunisian_dataset_STT-TTS15s_filtred1.0
type: Arbi-Houssem/Tunisian_dataset_STT-TTS15s_filtred1.0
args: 'config: ar, split: test'
metrics:
- name: Wer
type: wer
value: 117.69074949358543
---
<!-- 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. -->
# Whisper Tunisien
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Tunisian_dataset_STT-TTS15s_filtred1.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7577
- Wer: 117.6907
## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.5147 | 7.7519 | 500 | 3.1417 | 128.5618 |
| 0.0559 | 15.5039 | 1000 | 3.8111 | 132.5456 |
| 0.01 | 23.2558 | 1500 | 4.2115 | 120.1891 |
| 0.0029 | 31.0078 | 2000 | 4.4628 | 120.3916 |
| 0.0017 | 38.7597 | 2500 | 4.6127 | 111.2086 |
| 0.0011 | 46.5116 | 3000 | 4.6945 | 124.6455 |
| 0.0009 | 54.2636 | 3500 | 4.7426 | 113.3018 |
| 0.0009 | 62.0155 | 4000 | 4.7577 | 117.6907 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
monkeymon/singapore-ghost-story | monkeymon | "2024-06-20T14:59:31Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"frictionional",
"story telling",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T04:51:01Z" | ---
library_name: transformers
tags:
- unsloth
- frictionional
- story telling
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Write a simple ghost story.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Generate Singapore ghost story based on the prompt.
I hold no responsibility if you use this model and scare yourself.
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:** Chris Sim
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** llama-8b-bnb
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://huggingface.co/monkeymon/singapore-ghost-story
## 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. -->
This model is intened as a personal mini-POC to my other Billon Dollar Idea to the Education & Editorial field.
## 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. -->
The training data is available in https://huggingface.co/datasets/monkeymon/singapore-ghost-story
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
## Technical Specifications [optional]
#### Hardware
T-4 Nvidia for training
#### Software
- Pandas
- unsolth
- huggingface |
circulus/on-vits2-korean-v1 | circulus | "2024-07-01T08:08:55Z" | 0 | 0 | null | [
"onnx",
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T04:56:46Z" | ---
license: gpl-3.0
---
|
tvpian/tvpian_test_model | tvpian | "2024-06-12T04:59:22Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-12T04:59:22Z" | ---
license: mit
---
|
chainup244/google-gemma-7b-1718168397 | chainup244 | "2024-06-12T05:03:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T05:00:03Z" | ---
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] |
slelab/AES1 | slelab | "2024-06-12T05:50:24Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T05:00:46Z" | Entry not found |
circulus/on-canvers-real-v3.9.1-int8 | circulus | "2024-06-12T05:04:25Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T05:01:18Z" | ---
license: gpl-3.0
---
|
circulus/on-canvers-anime-v3.9.1-int8 | circulus | "2024-06-12T05:04:20Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T05:01:25Z" | ---
license: gpl-3.0
---
|
circulus/on-canvers-story-v3.9.1-int8 | circulus | "2024-06-12T05:04:10Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T05:01:33Z" | ---
license: gpl-3.0
---
|
circulus/on-canvers-disney-v3.9.1-int8 | circulus | "2024-06-12T05:03:34Z" | 0 | 0 | null | [
"license:gpl-3.0",
"region:us"
] | null | "2024-06-12T05:01:39Z" | ---
license: gpl-3.0
---
|
klandtech/llama2-hkcode-8b-ko-fintech | klandtech | "2024-06-12T05:08:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-12T05:01:59Z" | ---
license: mit
---
|
rhksals2001/Moedla | rhksals2001 | "2024-06-12T05:04:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T05:04:17Z" | Entry not found |
mikec003/yugi_muto | mikec003 | "2024-06-12T05:13:45Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T05:11:51Z" | Entry not found |
Youssef85/conte | Youssef85 | "2024-06-12T05:11:52Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T05:11:52Z" | Entry not found |
terry69/llama320pGrad_1 | terry69 | "2024-06-12T07:25:30Z" | 0 | 0 | null | [
"tensorboard",
"region:us"
] | null | "2024-06-12T05:17:23Z" | Entry not found |
heetha/phi-1_5-finetuned-gsm8k | heetha | "2024-06-12T05:30:47Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-1_5",
"license:mit",
"region:us"
] | null | "2024-06-12T05:17:36Z" | ---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-1_5
model-index:
- name: phi-1_5-finetuned-gsm8k
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. -->
# phi-1_5-finetuned-gsm8k
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |
allen0909/ROE_Patent_Breeze7B_RLHF_13 | allen0909 | "2024-06-12T06:22:48Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"reward-trainer",
"generated_from_trainer",
"base_model:MediaTek-Research/Breeze-7B-Instruct-v1_0",
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T05:25:00Z" | ---
license: apache-2.0
library_name: peft
tags:
- trl
- reward-trainer
- generated_from_trainer
base_model: MediaTek-Research/Breeze-7B-Instruct-v1_0
model-index:
- name: ROE_Patent_Breeze7B_RLHF_13
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. -->
# ROE_Patent_Breeze7B_RLHF_13
This model is a fine-tuned version of [MediaTek-Research/Breeze-7B-Instruct-v1_0](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0) on the None 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.11.2.dev0
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |
munbongshin/munbong | munbongshin | "2024-06-12T05:27:46Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-12T05:27:46Z" | ---
license: apache-2.0
---
|
ka05ar/Mistral-7B-Ins-Bn_Math_v1 | ka05ar | "2024-06-12T06:49:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T05:28:09Z" | ---
library_name: transformers
tags:
- unsloth
---
# 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]
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### Direct Use
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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. -->
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laitrongduc/my_awesome_qa_model | laitrongduc | "2024-06-12T05:28:15Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-12T05:28:15Z" | Entry not found |