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null | peft |
<!-- 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. -->
# GUE_mouse_0-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_0-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_0-seqsight_16384_512_56M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T02:53:50+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_0-seqsight\_16384\_512\_56M-L32\_f
==============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.4551
* F1 Score: 0.7309
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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text-generation | transformers | <a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a>
# Llama-3 8B Gradient Instruct 1048k
Gradient incorporates your data to deploy autonomous assistants that power critical operations acr... | {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3"], "pipeline_tag": "text-generation"} | blockblockblock/Llama-3-8B-Instruct-Gradient-1048k-bpw4.8-exl2 | null | [
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#transformers #safetensors #llama #text-generation #meta #llama-3 #conversational #en #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| [<img src="URL width="200"/>](URL)
Llama-3 8B Gradient Instruct 1048k
==================================
Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message contact@URL.
For m... | [
"### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.",
"#### Transformers pipeline",
"#### Transformers AutoModelForCausalLM",
"### Use with 'llama3'\n\n\n... | [
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null | peft |
<!-- 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. -->
# GUE_mouse_1-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:/... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_1-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_16384_512_56M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T02:55:46+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_1-seqsight\_16384\_512\_56M-L1\_f
=============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2418
* F1 Score: 0.8934
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | cilantro9246/irspo6v | null | [
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"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T02:56:23+00:00 | [
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#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License... | [
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null | peft |
<!-- 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. -->
# GUE_mouse_1-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:/... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_1-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_16384_512_56M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T02:56:31+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_1-seqsight\_16384\_512\_56M-L8\_f
=============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2334
* F1 Score: 0.8986
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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null | peft |
<!-- 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. -->
# GUE_mouse_1-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_1-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_16384_512_56M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T02:57:16+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_1-seqsight\_16384\_512\_56M-L32\_f
==============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2366
* F1 Score: 0.9033
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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null | peft |
<!-- 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. -->
# GUE_mouse_4-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:/... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_4-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_16384_512_56M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T02:57:29+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_4-seqsight\_16384\_512\_56M-L1\_f
=============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_4 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5884
* F1 Score: 0.6940
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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text-generation | transformers | # Llama3-ElonMusk-v1
This was finetuned on a small dataset with conversations of Elon Musk (and simulated conversations). This will be updated every day with better data, so dont lose any hope.
<sup>Test it out here: [Click me!](https://huggingface.co/spaces/Walmart-the-bag/Llama3-ElonMusk-v1)</sup>
# Communication
- ... | {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["elon", "musk", "humor"]} | Walmart-the-bag/Llama3-ElonMusk-v1 | null | [
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] | null | 2024-04-30T02:57:41+00:00 | [] | [
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#transformers #safetensors #llama #text-generation #elon #musk #humor #conversational #en #license-llama3 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
| # Llama3-ElonMusk-v1
This was finetuned on a small dataset with conversations of Elon Musk (and simulated conversations). This will be updated every day with better data, so dont lose any hope.
<sup>Test it out here: Click me!</sup>
# Communication
- Humor: You will experience humor of Elon Musk, and other interesting... | [
"# Llama3-ElonMusk-v1\nThis was finetuned on a small dataset with conversations of Elon Musk (and simulated conversations). This will be updated every day with better data, so dont lose any hope.\n\n<sup>Test it out here: Click me!</sup>",
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null | peft |
<!-- 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. -->
# GUE_mouse_4-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:/... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_4-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_16384_512_56M-L8_f | null | [
"peft",
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"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
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] | null | 2024-04-30T02:58:18+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_4-seqsight\_16384\_512\_56M-L8\_f
=============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_4 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6222
* F1 Score: 0.7026
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | shallow6414/hk7leqz | null | [
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"safetensors",
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"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
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"text-generation-inference",
"region:us"
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"1910.09700"
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#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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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text-classification | transformers |
<!-- 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. -->
# model_results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on... | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "model_results", "results": []}]} | DRAGOO/VGG16_MODEL | null | [
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| model\_results
==============
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.1240
* Accuracy: 0.9780
Model description
-----------------
More information needed
Intended uses & limitations
------------------... | [
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null | peft |
# 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. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** ... | {"library_name": "peft", "base_model": "westlake-repl/SaProt_35M_AF2"} | CluelessNovice/demo_cls2 | null | [
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automatic-speech-recognition | transformers |
<!-- 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 Tiny chinese - VingeNie
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whispe... | {"language": ["zh"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "Whisper Tiny chinese - VingeNie", "results": []}]} | VingeNie/whisper-tiny-zh_CN_cosine | null | [
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| Whisper Tiny chinese - VingeNie
===============================
This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0204
* Cer Ortho: 48.2903
* Cer: 37.8890
Model description
-----------------
More informa... | [
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | Fighoture/Llama-2-7b-chat-shortgpt-25-percent-tuluv2-lora | null | [
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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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null | peft |
<!-- 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. -->
# GUE_mouse_4-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_4-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_16384_512_56M-L32_f | null | [
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| GUE\_mouse\_4-seqsight\_16384\_512\_56M-L32\_f
==============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_4 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6372
* F1 Score: 0.7006
*... | [
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text-generation | transformers | Quantizations of https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct
# From original readme
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Model Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepsee... | {"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "deepseek-coder-6.7b-instruct"], "pipeline_tag": "text-generation", "inference": false} | duyntnet/deepseek-coder-6.7b-instruct-imatrix-GGUF | null | [
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text-generation | transformers |
# Uploaded model
- **Developed by:** 1024m
- **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/mai... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | 1024m/LLAMA3-SMM4H-Task6-16bit | null | [
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null | transformers |
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from d... | {"language": "en"} | Alexleetw/db_resnet50_20240430-025744 | null | [
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<p align="center">
<img src="URL width="60%">
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Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
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URL
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null | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | IN4/fast-whisper-v3-LoRA-8bit-epochs-3_num1_ru_kz | null | [
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null | peft |
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# Model Card for Model ID
## Model Details
### Model Description
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### Model Sources [optional]
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | lunarsylph/mooncell_v35 | null | [
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|
# Model Card for Model ID
## Model Details
### Model Description
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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- Funded by [optional]:
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null | peft |
<!-- 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. -->
# GUE_mouse_3-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:/... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_3-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_16384_512_56M-L1_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_3-seqsight\_16384\_512\_56M-L1\_f
=============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9372
* F1 Score: 0.8326
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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text-generation | Transformers |
This is a Llama 2 architecture model series trained on the FineWeb dataset upto 1 Billion parameters and uses tiktoken cl100k_base model as tokenizer | {"license": "mit", "library_name": "Transformers", "datasets": ["HuggingFaceFW/fineweb"], "pipeline_tag": "text-generation"} | sabareesh88/fw14k | null | [
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#Transformers #text-generation #dataset-HuggingFaceFW/fineweb #license-mit #region-us
|
This is a Llama 2 architecture model series trained on the FineWeb dataset upto 1 Billion parameters and uses tiktoken cl100k_base model as tokenizer | [] | [
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null | transformers |
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from d... | {"language": "en"} | Alexleetw/db_resnet50_20240430-030513 | null | [
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|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
### Run Configuration
{
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null | peft |
<!-- 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. -->
# GUE_mouse_3-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:/... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_3-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_16384_512_56M-L8_f | null | [
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"generated_from_trainer",
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_3-seqsight\_16384\_512\_56M-L8\_f
=============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9635
* F1 Score: 0.8322
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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null | peft |
<!-- 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. -->
# GUE_mouse_3-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_3-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_16384_512_56M-L32_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_3-seqsight\_16384\_512\_56M-L32\_f
==============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_3 dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3820
* F1 Score: 0.8534
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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text-generation | transformers | <a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a>
# Llama-3 8B Gradient Instruct 1048k
Gradient incorporates your data to deploy autonomous assistants that power critical operations acr... | {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3"], "pipeline_tag": "text-generation"} | blockblockblock/Llama-3-8B-Instruct-Gradient-1048k-bpw5-exl2 | null | [
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| [<img src="URL width="200"/>](URL)
Llama-3 8B Gradient Instruct 1048k
==================================
Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message contact@URL.
For m... | [
"### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.",
"#### Transformers pipeline",
"#### Transformers AutoModelForCausalLM",
"### Use with 'llama3'\n\n\n... | [
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null | peft |
<!-- 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. -->
# GUE_mouse_2-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:/... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_2-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_16384_512_56M-L1_f | null | [
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"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:10:15+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_2-seqsight\_16384\_512\_56M-L1\_f
=============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3112
* F1 Score: 0.8810
* A... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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sentence-similarity | transformers |
# LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fin... | {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["text-embedding", "embeddings", "information-retrieval", "beir", "text-classification", "language-model", "text-clustering", "text-semantic-similarity", "text-evaluation", "text-reranking", "feature-extraction", "sentence-similarity", "Sent... | McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp | null | [
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# LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
> LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fin... | [
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null | peft |
<!-- 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. -->
# GUE_mouse_2-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:/... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_2-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_16384_512_56M-L8_f | null | [
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"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_2-seqsight\_16384\_512\_56M-L8\_f
=============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6632
* F1 Score: 0.8841
* A... | [
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text-classification | transformers | ## TextAttack Model Card
This `bert` model was fine-tuned using TextAttack. The model was fine-tuned
for 3 epochs with a batch size of 8,
a maximum sequence length of 512, and an initial learning rate of 3e-05.
Since this was a classification task, the model was trained... | {"language": ["zh"], "metrics": ["accuracy"], "pipeline_tag": "text-classification"} | WangA/roberta-base-finetuned-jd | null | [
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"endpoints_compatible",
"region:us"
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"zh"
] | TAGS
#transformers #safetensors #bert #text-classification #zh #autotrain_compatible #endpoints_compatible #region-us
| ## TextAttack Model Card
This 'bert' model was fine-tuned using TextAttack. The model was fine-tuned
for 3 epochs with a batch size of 8,
a maximum sequence length of 512, and an initial learning rate of 3e-05.
Since this was a classification task, the model was trained... | [
"## TextAttack Model Card\n\n This 'bert' model was fine-tuned using TextAttack. The model was fine-tuned\n for 3 epochs with a batch size of 8,\n a maximum sequence length of 512, and an initial learning rate of 3e-05.\n Since this was a classification task, the model w... | [
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text-generation | transformers |
<!-- 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. -->
# ppo_zephyr9
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistr... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "ppo_zephyr9", "results": []}]} | vwxyzjn/ppo_zephyr9 | null | [
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"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T03:10:55+00:00 | [] | [] | TAGS
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|
# ppo_zephyr9
This model is a fine-tuned version of HuggingFaceH4/mistral-7b-sft-beta on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperpar... | [
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"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
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null | peft |
<!-- 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. -->
# GUE_mouse_2-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https:... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_mouse_2-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_16384_512_56M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:11:03+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_mouse\_2-seqsight\_16384\_512\_56M-L32\_f
==============================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_mouse\_2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9767
* F1 Score: 0.8779
*... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_splice_reconstructed-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_splice_reconstructed-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_16384_512_56M-L1_f | null | [
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"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:11:11+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_splice\_reconstructed-seqsight\_16384\_512\_56M-L1\_f
==========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_splice\_reconstructed dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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null | transformers |
# Uploaded model
- **Developed by:** 1024m
- **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/mai... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | 1024m/LLAMA3-SMM4H-Task6-LoRA | null | [
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|
# Uploaded model
- Developed by: 1024m
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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null | peft |
<!-- 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. -->
# out
This model is a fine-tuned version of [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it) on the None da... | {"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "google/gemma-1.1-2b-it", "model-index": [{"name": "out", "results": []}]} | cohesionet/out | null | [
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] | null | 2024-04-30T03:14:16+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-google/gemma-1.1-2b-it #license-gemma #region-us
| out
===
This model is a fine-tuned version of google/gemma-1.1-2b-it on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 5.1992
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Trai... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1... | [
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text-generation | transformers |
# Uploaded model
- **Developed by:** 1024m
- **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/mai... | {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | 1024m/LLAMA3-SMM4H-Task6-4bit | null | [
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"region:us"
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|
# Uploaded model
- Developed by: 1024m
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
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text-generation | transformers |
<!-- 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. -->
# llama2-7b-dpo-full-sft-wo-healthsearch_qa
This model is a fine-tuned version of [Minbyul/llama2-7b-wo-healthsearch_qa-sft](https... | {"tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/llama2-7b-wo-healthsearch_qa-sft", "model-index": [{"name": "llama2-7b-dpo-full-sft-wo-healthsearch_qa", "results": []}]} | Minbyul/llama2-7b-dpo-full-sft-wo-healthsearch_qa | null | [
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"base_model:Minbyul/llama2-7b-wo-healthsearch_qa-sft",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-in... | null | 2024-04-30T03:16:12+00:00 | [] | [] | TAGS
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|
# llama2-7b-dpo-full-sft-wo-healthsearch_qa
This model is a fine-tuned version of Minbyul/llama2-7b-wo-healthsearch_qa-sft on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6914
- Rewards/chosen: -0.0016
- Rewards/rejected: -0.0049
- Rewards/acc... | [
"# llama2-7b-dpo-full-sft-wo-healthsearch_qa\n\nThis model is a fine-tuned version of Minbyul/llama2-7b-wo-healthsearch_qa-sft on the HuggingFaceH4/ultrafeedback_binarized dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6914\n- Rewards/chosen: -0.0016\n- Rewards/rejected: -0.0049\n- Re... | [
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null | peft |
<!-- 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. -->
# GUE_splice_reconstructed-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_splice_reconstructed-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_16384_512_56M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:19:14+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_splice\_reconstructed-seqsight\_16384\_512\_56M-L8\_f
==========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_splice\_reconstructed dataset.
It achieves the following results on the evaluation set:
... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_splice_reconstructed-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_51... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_splice_reconstructed-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_16384_512_56M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:19:52+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_splice\_reconstructed-seqsight\_16384\_512\_56M-L32\_f
===========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_splice\_reconstructed dataset.
It achieves the following results on the evaluation set... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_tf_0-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_0-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_0-seqsight_16384_512_56M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:20:30+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_0-seqsight\_16384\_512\_56M-L1\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4064
* F1 Score: 0.8127
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_tf_0-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_0-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_0-seqsight_16384_512_56M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:21:00+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_0-seqsight\_16384\_512\_56M-L8\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3572
* F1 Score: 0.8419
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_tf_0-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://h... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_0-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_0-seqsight_16384_512_56M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:21:28+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_0-seqsight\_16384\_512\_56M-L32\_f
===========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_0 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3661
* F1 Score: 0.8357
* Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_tf_1-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_1-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_1-seqsight_16384_512_56M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:21:40+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_1-seqsight\_16384\_512\_56M-L1\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3366
* F1 Score: 0.8579
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_tf_1-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_1-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_1-seqsight_16384_512_56M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:21:51+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_1-seqsight\_16384\_512\_56M-L8\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3495
* F1 Score: 0.8466
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_tf_1-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://h... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_1-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_1-seqsight_16384_512_56M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:22:11+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_1-seqsight\_16384\_512\_56M-L32\_f
===========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_1 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3980
* F1 Score: 0.8258
* Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_tf_4-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_4-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_16384_512_56M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:22:45+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_4-seqsight\_16384\_512\_56M-L1\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3561
* F1 Score: 0.8409
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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text-generation | null |
# kat33/Llama-3-8B-Instruct-Gradient-1048k-Q6_K-GGUF
This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-1048k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo)... | {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation"} | kat33/Llama-3-8B-Instruct-Gradient-1048k-Q6_K-GGUF | null | [
"gguf",
"meta",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:llama3",
"region:us"
] | null | 2024-04-30T03:23:45+00:00 | [] | [
"en"
] | TAGS
#gguf #meta #llama-3 #llama-cpp #gguf-my-repo #text-generation #en #license-llama3 #region-us
|
# kat33/Llama-3-8B-Instruct-Gradient-1048k-Q6_K-GGUF
This model was converted to GGUF format from 'gradientai/Llama-3-8B-Instruct-Gradient-1048k' using URL via the URL's GGUF-my-repo space.
Refer to the original model card for more details on the model.
## Use with URL
Install URL through brew.
Invoke the URL serve... | [
"# kat33/Llama-3-8B-Instruct-Gradient-1048k-Q6_K-GGUF\nThis model was converted to GGUF format from 'gradientai/Llama-3-8B-Instruct-Gradient-1048k' using URL via the URL's GGUF-my-repo space.\nRefer to the original model card for more details on the model.",
"## Use with URL\n\nInstall URL through brew.\n\n\nInvo... | [
"TAGS\n#gguf #meta #llama-3 #llama-cpp #gguf-my-repo #text-generation #en #license-llama3 #region-us \n",
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43,
92,
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"TAGS\n#gguf #meta #llama-3 #llama-cpp #gguf-my-repo #text-generation #en #license-llama3 #region-us \n# kat33/Llama-3-8B-Instruct-Gradient-1048k-Q6_K-GGUF\nThis model was converted to GGUF format from 'gradientai/Llama-3-8B-Instruct-Gradient-1048k' using URL via the URL's GGUF-my-repo space.\nRefer to the original... |
null | transformers |
<p align="center">
<img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%">
</p>
**Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch**
## Task: detection
https://github.com/mindee/doctr
### Example usage:
```python
>>> from d... | {"language": "en"} | Alexleetw/detection_test | null | [
"transformers",
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"endpoints_compatible",
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"en"
] | TAGS
#transformers #pytorch #en #endpoints_compatible #region-us
|
<p align="center">
<img src="URL width="60%">
</p>
Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch
## Task: detection
URL
### Example usage:
### Run Configuration
{
"train_path": "/workspace/donut_train/doctr/train/",
"val_path": "/workspace/donut_train/... | [
"## Task: detection\n\nURL",
"### Example usage:",
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | cilantro9246/kd7qkzx | null | [
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"text-generation-inference",
"region:us"
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#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | shallow6414/yghjnvs | null | [
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|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License... | [
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text-generation | transformers | <a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a>
# Llama-3 8B Gradient Instruct 1048k
Gradient incorporates your data to deploy autonomous assistants that power critical operations acr... | {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3"], "pipeline_tag": "text-generation"} | blockblockblock/Llama-3-8B-Instruct-Gradient-1048k-bpw5.5-exl2 | null | [
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"en"
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#transformers #safetensors #llama #text-generation #meta #llama-3 #conversational #en #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| [<img src="URL width="200"/>](URL)
Llama-3 8B Gradient Instruct 1048k
==================================
Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message contact@URL.
For m... | [
"### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.",
"#### Transformers pipeline",
"#### Transformers AutoModelForCausalLM",
"### Use with 'llama3'\n\n\n... | [
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text-to-image | diffusers |
# LoRA text2image fine-tuning - lerle144/criminal-sketch-lora-v2-2-test
These are LoRA adaption weights for SujinHwang/criminal-sketch-lora-v2-2. The weights were fine-tuned on the SujinHwang/criminal-sketch-Hr dataset. You can find some example images in the following.
![img_1](./image_1... | {"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "SujinHwang/criminal-sketch-lora-v2-2", "inference": true} | lerle144/criminal-sketch-lora-v2-2-test | null | [
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#diffusers #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-SujinHwang/criminal-sketch-lora-v2-2 #license-creativeml-openrail-m #region-us
|
# LoRA text2image fine-tuning - lerle144/criminal-sketch-lora-v2-2-test
These are LoRA adaption weights for SujinHwang/criminal-sketch-lora-v2-2. The weights were fine-tuned on the SujinHwang/criminal-sketch-Hr dataset. You can find some example images in the following.
!img_0
!img_1
!img_2
!img_3
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"# LoRA text2image fine-tuning - lerle144/criminal-sketch-lora-v2-2-test\nThese are LoRA adaption weights for SujinHwang/criminal-sketch-lora-v2-2. The weights were fine-tuned on the SujinHwang/criminal-sketch-Hr dataset. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3"
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null | peft |
<!-- 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. -->
# GUE_tf_4-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_4-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_16384_512_56M-L8_f | null | [
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"safetensors",
"generated_from_trainer",
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"region:us"
] | null | 2024-04-30T03:28:20+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_4-seqsight\_16384\_512\_56M-L8\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3725
* F1 Score: 0.8399
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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reinforcement-learning | stable-baselines3 |
# **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 ... | {"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": "LunarL... | slepox/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-30T03:31:44+00:00 | [] | [] | TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us
|
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.
## Usage (with Stable-baselines3)
TODO: Add your code
| [
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] |
null | peft |
<!-- 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. -->
# GUE_tf_4-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://h... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_4-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_4-seqsight_16384_512_56M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:32:48+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_4-seqsight\_16384\_512\_56M-L32\_f
===========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_4 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5689
* F1 Score: 0.8359
* Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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null | peft |
<!-- 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. -->
# GUE_tf_3-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_3-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_16384_512_56M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_16384_512_56M",
"region:us"
] | null | 2024-04-30T03:33:18+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_3-seqsight\_16384\_512\_56M-L1\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5605
* F1 Score: 0.7155
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
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text2text-generation | transformers |
<!-- 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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_da... | {"tags": ["generated_from_trainer"], "datasets": ["samsum"], "base_model": "google/pegasus-cnn_dailymail", "model-index": [{"name": "pegasus-samsum", "results": []}]} | wahyubagus/pegasus-samsum | null | [
"transformers",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T03:33:48+00:00 | [] | [] | TAGS
#transformers #safetensors #pegasus #text2text-generation #generated_from_trainer #dataset-samsum #base_model-google/pegasus-cnn_dailymail #autotrain_compatible #endpoints_compatible #region-us
|
# pegasus-samsum
This model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparam... | [
"# pegasus-samsum\n\nThis model is a fine-tuned version of google/pegasus-cnn_dailymail on the samsum dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
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null | peft |
<!-- 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. -->
# GUE_tf_3-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_3-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_16384_512_56M-L8_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_3-seqsight\_16384\_512\_56M-L8\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5387
* F1 Score: 0.7233
* Accuracy: ... | [
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null | peft |
<!-- 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. -->
# GUE_tf_3-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://h... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_3-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_3-seqsight_16384_512_56M-L32_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_3-seqsight\_16384\_512\_56M-L32\_f
===========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_3 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5441
* F1 Score: 0.7119
* Accuracy... | [
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | justinphan3110/zephyr_short_circuit_init | null | [
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|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
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null | peft |
<!-- 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. -->
# GUE_tf_2-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_2-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_16384_512_56M-L1_f | null | [
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"region:us"
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_2-seqsight\_16384\_512\_56M-L1\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4574
* F1 Score: 0.7978
* Accuracy: ... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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null | peft |
<!-- 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. -->
# GUE_tf_2-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://hu... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_2-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_16384_512_56M-L8_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_2-seqsight\_16384\_512\_56M-L8\_f
==========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4601
* F1 Score: 0.78
* Accuracy: 0.... | [
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null | peft |
<!-- 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. -->
# GUE_tf_2-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](https://h... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_tf_2-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_tf_2-seqsight_16384_512_56M-L32_f | null | [
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"generated_from_trainer",
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"region:us"
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_tf\_2-seqsight\_16384\_512\_56M-L32\_f
===========================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_tf\_2 dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4468
* F1 Score: 0.7817
* Accuracy... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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null | peft |
<!-- 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. -->
# tiny-llama-20p
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.c... | {"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "model-index": [{"name": "tiny-llama-20p", "results": []}]} | terry69/tiny-llama-20p | null | [
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| tiny-llama-20p
==============
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the HuggingFaceH4/ultrachat\_200k dataset.
It achieves the following results on the evaluation set:
* Loss: nan
Model description
-----------------
More information needed
Intended uses &... | [
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text-classification | transformers | ## TextAttack Model Card
This `albert` model was fine-tuned using TextAttack. The model was fine-tuned
for 3 epochs with a batch size of 8,
a maximum sequence length of 512, and an initial learning rate of 3e-05.
Since this was a classification task, the model was train... | {"language": ["zh"], "license": "apache-2.0", "metrics": ["accuracy"], "pipeline_tag": "text-classification"} | WangA/albert-base-finetuned-jd | null | [
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text-to-image | diffusers |
# SDXL LoRA DreamBooth - kuei1026/3d-icon-sdxl-dora-rank-64
<Gallery />
## Model description
### These are kuei1026/3d-icon-sdxl-dora-rank-64 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: down... | {"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "a <s0><s1> icon of an astronaut riding a horse, in the style of <s0><s1>", "output": {"url": "image_0.png"}}, {"text": "a <s0><s1> icon of an astrona... | kuei1026/3d-icon-sdxl-dora-rank-64 | null | [
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|
# SDXL LoRA DreamBooth - kuei1026/3d-icon-sdxl-dora-rank-64
<Gallery />
## Model description
### These are kuei1026/3d-icon-sdxl-dora-rank-64 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
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null | peft |
<!-- 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. -->
# GUE_virus_covid-seqsight_16384_512_56M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](htt... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_virus_covid-seqsight_16384_512_56M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_16384_512_56M-L1_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_virus\_covid-seqsight\_16384\_512\_56M-L1\_f
=================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5162
* F1 Score... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
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text-generation | transformers | <a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a>
# Llama-3 8B Gradient Instruct 1048k
Gradient incorporates your data to deploy autonomous assistants that power critical operations acr... | {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3"], "pipeline_tag": "text-generation"} | blockblockblock/Llama-3-8B-Instruct-Gradient-1048k-bpw6-exl2 | null | [
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| [<img src="URL width="200"/>](URL)
Llama-3 8B Gradient Instruct 1048k
==================================
Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message contact@URL.
For m... | [
"### Use with transformers\n\n\nYou can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the 'generate()' function. Let's see examples of both.",
"#### Transformers pipeline",
"#### Transformers AutoModelForCausalLM",
"### Use with 'llama3'\n\n\n... | [
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null | transformers |
# 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. -->
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null | transformers |
# 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. -->
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | KaifengGGG/Llama-2-7b-spider | null | [
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# Model Card for Model ID
## Model Details
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | cohesionet/gemma-1.1-2b-it-test | null | [
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# Model Card for Model ID
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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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text-generation | transformers |
<!-- 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. -->
# meditron-7b-dpo-full-sft-wo-healthsearch_qa
This model is a fine-tuned version of [Minbyul/meditron-7b-wo-healthsearch_qa-sft](h... | {"license": "llama2", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/meditron-7b-wo-healthsearch_qa-sft", "model-index": [{"name": "meditron-7b-dpo-full-sft-wo-healthsearch_qa",... | Minbyul/meditron-7b-dpo-full-sft-wo-healthsearch_qa | null | [
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"license:llama2",
"autotrain_compatible",
"endpoints_compatible",... | null | 2024-04-30T03:44:10+00:00 | [] | [] | TAGS
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|
# meditron-7b-dpo-full-sft-wo-healthsearch_qa
This model is a fine-tuned version of Minbyul/meditron-7b-wo-healthsearch_qa-sft on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6911
- Rewards/chosen: 0.0010
- Rewards/rejected: -0.0039
- Rewards/... | [
"# meditron-7b-dpo-full-sft-wo-healthsearch_qa\n\nThis model is a fine-tuned version of Minbyul/meditron-7b-wo-healthsearch_qa-sft on the HuggingFaceH4/ultrafeedback_binarized dataset.\nIt achieves the following results on the evaluation set:\n- Loss: 0.6911\n- Rewards/chosen: 0.0010\n- Rewards/rejected: -0.0039\n-... | [
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image-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.2648889720439911
f1_macro: 0.7457675172458867
f1_micro: 0.904404233526801
f1_weighted: 0.9015634064092323
precision_macro: 0.8165440763859227
precision_micro: 0.904404233526801
precision_weighted: 0.9040411595949... | {"tags": ["autotrain", "image-classification"], "datasets": ["autotrain-9e6d1-2u0z9/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "exam... | Kushagra07/autotrain-9e6d1-2u0z9 | null | [
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] | null | 2024-04-30T03:44:40+00:00 | [] | [] | TAGS
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|
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 0.2648889720439911
f1_macro: 0.7457675172458867
f1_micro: 0.904404233526801
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precision_weighted: 0.9040411595949... | [
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null | peft |
<!-- 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. -->
# GUE_virus_covid-seqsight_16384_512_56M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](htt... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_virus_covid-seqsight_16384_512_56M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_16384_512_56M-L8_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_virus\_covid-seqsight\_16384\_512\_56M-L8\_f
=================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1462
* F1 Score... | [
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null | peft |
<!-- 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. -->
# GUE_virus_covid-seqsight_16384_512_56M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_16384_512_56M](ht... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_16384_512_56M", "model-index": [{"name": "GUE_virus_covid-seqsight_16384_512_56M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_virus_covid-seqsight_16384_512_56M-L32_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_16384_512_56M #region-us
| GUE\_virus\_covid-seqsight\_16384\_512\_56M-L32\_f
==================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_16384\_512\_56M on the mahdibaghbanzadeh/GUE\_virus\_covid dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9754
* F1 Sco... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_300_tata-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_3... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_32768_512_30M-L1_f | null | [
"peft",
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_300\_tata-seqsight\_32768\_512\_30M-L1\_f
==========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_tata dataset.
It achieves the following results on the evaluation set:
... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_300_tata-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_3... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_32768_512_30M-L8_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_300\_tata-seqsight\_32768\_512\_30M-L8\_f
==========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_tata dataset.
It achieves the following results on the evaluation set:
... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_300_tata-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_tata-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_tata-seqsight_32768_512_30M-L32_f | null | [
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"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
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] | null | 2024-04-30T03:46:38+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_300\_tata-seqsight\_32768\_512\_30M-L32\_f
===========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_tata dataset.
It achieves the following results on the evaluation set... | [
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... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_300_notata-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_32768_512_30M-L1_f | null | [
"peft",
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_300\_notata-seqsight\_32768\_512\_30M-L1\_f
============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_notata dataset.
It achieves the following results on the evaluation... | [
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... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_300_notata-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_32768_512_30M-L8_f | null | [
"peft",
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"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
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] | null | 2024-04-30T03:47:04+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_300\_notata-seqsight\_32768\_512\_30M-L8\_f
============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_notata dataset.
It achieves the following results on the evaluation... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_300_notata-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_51... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_300_notata-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_300_notata-seqsight_32768_512_30M-L32_f | null | [
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"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
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] | null | 2024-04-30T03:49:44+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_300\_notata-seqsight\_32768\_512\_30M-L32\_f
=============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_300\_notata dataset.
It achieves the following results on the evaluati... | [
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... | [
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text-generation | transformers |
# 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 generate... | {"library_name": "transformers", "tags": []} | cilantro9246/be3df63 | null | [
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|
# Model Card for Model ID
## Model Details
### Model Description
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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- Funded by [optional]:
- Shared by [optional]:
- Model type:
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- License... | [
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null | peft |
<!-- 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. -->
# nash_dpo_iter_1
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-h... | {"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo"], "datasets": ["updated", "original"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "nash_dpo_iter_1", "results": []}]} | YYYYYYibo/nash_dpo_iter_1 | null | [
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| nash\_dpo\_iter\_1
==================
This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the updated and the original datasets.
It achieves the following results on the evaluation set:
* Loss: 0.6285
* Rewards/chosen: -0.1131
* Rewards/rejected: -0.2857
* Rewards/accuracies: 0.7000
* Rew... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 16\n* total\\_train\\_batch\\_size: 128\... | [
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text-classification | transformers | # SWOT Analysis Model based on DistilBERT
This repository hosts a fine-tuned version of `distilbert-base-uncased`, specifically trained to classify SWOT elements (Strength, Weakness, Opportunity, Threat) in Amazon product reviews of smartphones. This model serves as a "Synthetic Expert", with annotations derived from ... | {"license": "mit"} | jcaponigro/SWOT_Classifier | null | [
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"distilbert",
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"region:us"
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#transformers #safetensors #distilbert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us
| # SWOT Analysis Model based on DistilBERT
This repository hosts a fine-tuned version of 'distilbert-base-uncased', specifically trained to classify SWOT elements (Strength, Weakness, Opportunity, Threat) in Amazon product reviews of smartphones. This model serves as a "Synthetic Expert", with annotations derived from ... | [
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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text-generation | transformers |
# Model Card for Model ID
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null | transformers |
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null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# GUE_prom_prom_core_all-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_3... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_32768_512_30M-L1_f | null | [
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| GUE\_prom\_prom\_core\_all-seqsight\_32768\_512\_30M-L1\_f
==========================================================
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... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_core_all-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_3... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_32768_512_30M-L8_f | null | [
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| GUE\_prom\_prom\_core\_all-seqsight\_32768\_512\_30M-L8\_f
==========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset.
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... | [
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null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# GUE_prom_prom_core_all-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_all-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_all-seqsight_32768_512_30M-L32_f | null | [
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| GUE\_prom\_prom\_core\_all-seqsight\_32768\_512\_30M-L32\_f
===========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_all dataset.
It achieves the following results on the evaluation set... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_core_notata-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_51... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_30M-L1_f | null | [
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#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_core\_notata-seqsight\_32768\_512\_30M-L1\_f
=============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset.
It achieves the following results on the evaluat... | [
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null | peft |
<!-- 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. -->
# GUE_prom_prom_core_notata-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_51... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_30M-L8_f | null | [
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| GUE\_prom\_prom\_core\_notata-seqsight\_32768\_512\_30M-L8\_f
=============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset.
It achieves the following results on the evaluat... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* ... | [
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"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimi... |
null | peft |
<!-- 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. -->
# GUE_prom_prom_core_notata-seqsight_32768_512_30M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_5... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_notata-seqsight_32768_512_30M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_notata-seqsight_32768_512_30M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T03:59:07+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_core\_notata-seqsight\_32768\_512\_30M-L32\_f
==============================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_notata dataset.
It achieves the following results on the evalu... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* ... | [
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"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimi... |
null | peft |
<!-- 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. -->
# GUE_prom_prom_core_tata-seqsight_32768_512_30M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_30M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_30M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T03:59:13+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_core\_tata-seqsight\_32768\_512\_30M-L1\_f
===========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset.
It achieves the following results on the evaluation se... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* ... | [
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"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimi... |
text-to-audio | transformers |
<!-- 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. -->
# fil_b64_le3_s4000
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) ... | {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "fil_b64_le3_s4000", "results": []}]} | mikhail-panzo/fil_b64_le3_s4000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T03:59:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us
| fil\_b64\_le3\_s4000
====================
This model is a fine-tuned version of microsoft/speecht5\_tts on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.5467
Model description
-----------------
More information needed
Intended uses & limitations
-----------------------... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=... | [
"TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\... | [
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"TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch... |
null | peft |
<!-- 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. -->
# GUE_prom_prom_core_tata-seqsight_32768_512_30M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_32768_512_... | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_32768_512_30M", "model-index": [{"name": "GUE_prom_prom_core_tata-seqsight_32768_512_30M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_prom_prom_core_tata-seqsight_32768_512_30M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_32768_512_30M",
"region:us"
] | null | 2024-04-30T04:01:48+00:00 | [] | [] | TAGS
#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us
| GUE\_prom\_prom\_core\_tata-seqsight\_32768\_512\_30M-L8\_f
===========================================================
This model is a fine-tuned version of mahdibaghbanzadeh/seqsight\_32768\_512\_30M on the mahdibaghbanzadeh/GUE\_prom\_prom\_core\_tata dataset.
It achieves the following results on the evaluation se... | [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* training\\_steps: 10000",
... | [
"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* ... | [
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"TAGS\n#peft #safetensors #generated_from_trainer #base_model-mahdibaghbanzadeh/seqsight_32768_512_30M #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0005\n* train\\_batch\\_size: 128\n* eval\\_batch\\_size: 128\n* seed: 42\n* optimi... |
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