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allstax/20231216-045106
|
allstax
| 2023-12-16T05:07:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:AIDC-ai-business/Marcoroni-7B-v3",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T05:07:34Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: AIDC-ai-business/Marcoroni-7B-v3
model-index:
- name: 20231216-045106
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 20231216-045106
This model is a fine-tuned version of [AIDC-ai-business/Marcoroni-7B-v3](https://huggingface.co/AIDC-ai-business/Marcoroni-7B-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2843
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2541 | 0.02 | 1 | 2.1106 |
| 1.5517 | 0.03 | 2 | 2.1106 |
| 2.347 | 0.05 | 3 | 2.0350 |
| 2.006 | 0.06 | 4 | 1.9297 |
| 1.8398 | 0.08 | 5 | 1.8406 |
| 1.8506 | 0.09 | 6 | 1.8406 |
| 1.8804 | 0.11 | 7 | 1.7621 |
| 1.9034 | 0.12 | 8 | 1.6988 |
| 0.9835 | 0.14 | 9 | 1.6494 |
| 1.4725 | 0.15 | 10 | 1.5915 |
| 1.1824 | 0.17 | 11 | 1.5557 |
| 1.3356 | 0.18 | 12 | 1.5557 |
| 1.1077 | 0.2 | 13 | 1.5242 |
| 1.4959 | 0.22 | 14 | 1.4908 |
| 1.4881 | 0.23 | 15 | 1.4550 |
| 1.2674 | 0.25 | 16 | 1.4201 |
| 1.2133 | 0.26 | 17 | 1.3872 |
| 1.5423 | 0.28 | 18 | 1.3521 |
| 0.9551 | 0.29 | 19 | 1.3212 |
| 1.1868 | 0.31 | 20 | 1.2881 |
| 1.4799 | 0.32 | 21 | 1.2526 |
| 1.2903 | 0.34 | 22 | 1.2154 |
| 1.3357 | 0.35 | 23 | 1.1770 |
| 0.868 | 0.37 | 24 | 1.1383 |
| 1.1978 | 0.38 | 25 | 1.0982 |
| 1.2102 | 0.4 | 26 | 1.0575 |
| 0.8946 | 0.42 | 27 | 1.0575 |
| 1.0671 | 0.43 | 28 | 1.0151 |
| 1.0761 | 0.45 | 29 | 0.9715 |
| 0.9568 | 0.46 | 30 | 0.9263 |
| 0.8203 | 0.48 | 31 | 0.8848 |
| 0.8601 | 0.49 | 32 | 0.8408 |
| 0.8253 | 0.51 | 33 | 0.7945 |
| 0.8098 | 0.52 | 34 | 0.7504 |
| 0.6567 | 0.54 | 35 | 0.7110 |
| 0.6545 | 0.55 | 36 | 0.6695 |
| 0.6036 | 0.57 | 37 | 0.6338 |
| 0.6746 | 0.58 | 38 | 0.6018 |
| 0.5376 | 0.6 | 39 | 0.5731 |
| 0.5674 | 0.62 | 40 | 0.5471 |
| 0.4762 | 0.63 | 41 | 0.5218 |
| 0.4755 | 0.65 | 42 | 0.4965 |
| 0.3974 | 0.66 | 43 | 0.4756 |
| 0.8437 | 0.68 | 44 | 0.4607 |
| 0.4766 | 0.69 | 45 | 0.4485 |
| 0.5403 | 0.71 | 46 | 0.4369 |
| 0.4201 | 0.72 | 47 | 0.4244 |
| 0.4326 | 0.74 | 48 | 0.4115 |
| 0.4669 | 0.75 | 49 | 0.3978 |
| 0.5259 | 0.77 | 50 | 0.3867 |
| 0.3906 | 0.78 | 51 | 0.3782 |
| 0.369 | 0.8 | 52 | 0.3724 |
| 0.3314 | 0.82 | 53 | 0.3658 |
| 0.4164 | 0.83 | 54 | 0.3585 |
| 0.3339 | 0.85 | 55 | 0.3499 |
| 0.315 | 0.86 | 56 | 0.3405 |
| 0.5404 | 0.88 | 57 | 0.3340 |
| 0.3136 | 0.89 | 58 | 0.3302 |
| 0.3574 | 0.91 | 59 | 0.3275 |
| 0.3466 | 0.92 | 60 | 0.3249 |
| 0.3097 | 0.94 | 61 | 0.3226 |
| 0.4212 | 0.95 | 62 | 0.3208 |
| 0.3183 | 0.97 | 63 | 0.3184 |
| 0.2762 | 0.98 | 64 | 0.3162 |
| 0.3936 | 1.0 | 65 | 0.3143 |
| 0.303 | 1.02 | 66 | 0.3128 |
| 0.3813 | 1.03 | 67 | 0.3108 |
| 0.3065 | 1.05 | 68 | 0.3089 |
| 0.2457 | 1.06 | 69 | 0.3069 |
| 0.4008 | 1.08 | 70 | 0.3053 |
| 0.3131 | 1.09 | 71 | 0.3040 |
| 0.2483 | 1.11 | 72 | 0.3027 |
| 0.2445 | 1.12 | 73 | 0.3016 |
| 0.2717 | 1.14 | 74 | 0.3003 |
| 0.6308 | 1.15 | 75 | 0.2991 |
| 0.3271 | 1.17 | 76 | 0.2980 |
| 0.3013 | 1.18 | 77 | 0.2971 |
| 0.2951 | 1.2 | 78 | 0.2960 |
| 0.2514 | 1.22 | 79 | 0.2950 |
| 0.2836 | 1.23 | 80 | 0.2940 |
| 0.2538 | 1.25 | 81 | 0.2929 |
| 0.312 | 1.26 | 82 | 0.2921 |
| 0.2773 | 1.28 | 83 | 0.2912 |
| 0.2957 | 1.29 | 84 | 0.2904 |
| 0.3997 | 1.31 | 85 | 0.2896 |
| 0.3084 | 1.32 | 86 | 0.2889 |
| 0.2521 | 1.34 | 87 | 0.2882 |
| 0.2873 | 1.35 | 88 | 0.2877 |
| 0.2419 | 1.37 | 89 | 0.2872 |
| 0.2691 | 1.38 | 90 | 0.2867 |
| 0.6368 | 1.4 | 91 | 0.2864 |
| 0.2299 | 1.42 | 92 | 0.2860 |
| 0.4967 | 1.43 | 93 | 0.2857 |
| 0.2827 | 1.45 | 94 | 0.2855 |
| 0.2501 | 1.46 | 95 | 0.2853 |
| 0.2285 | 1.48 | 96 | 0.2851 |
| 0.269 | 1.49 | 97 | 0.2849 |
| 0.2539 | 1.51 | 98 | 0.2847 |
| 0.2798 | 1.52 | 99 | 0.2845 |
| 0.2695 | 1.54 | 100 | 0.2843 |
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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LoneStriker/Mistral-7B-Instruct-v0.2-DARE-3.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T05:12:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T05:10:13Z |
---
license: apache-2.0
language:
- en
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a
href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp)
2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling)
3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B)
- base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
The yaml config file for this model is here:
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: bfloat16
merge_method: dare_ties
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
parameters:
density: 0.8
weight: 0.4
- model: Q-bert/MetaMath-Cybertron-Starling
parameters:
density: 0.8
weight: 0.3
- model: v1olet/v1olet_marcoroni-go-bruins-merge-7B
parameters:
density: 0.8
weight: 0.3
parameters:
int8_mask: true
```
# Prompt template:
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- **Alpaca**
```
{system_message}
### Instruction:
{prompt}
### Response:
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)

# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
|
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] |
LoneStriker/Mistral-7B-Instruct-v0.2-DARE-4.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T05:17:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T05:15:24Z |
---
license: apache-2.0
language:
- en
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a
href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp)
2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling)
3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B)
- base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
The yaml config file for this model is here:
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: bfloat16
merge_method: dare_ties
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
parameters:
density: 0.8
weight: 0.4
- model: Q-bert/MetaMath-Cybertron-Starling
parameters:
density: 0.8
weight: 0.3
- model: v1olet/v1olet_marcoroni-go-bruins-merge-7B
parameters:
density: 0.8
weight: 0.3
parameters:
int8_mask: true
```
# Prompt template:
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- **Alpaca**
```
{system_message}
### Instruction:
{prompt}
### Response:
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)

# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
|
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] |
KokeCacao/mcstuco-f23-survival
|
KokeCacao
| 2023-12-16T05:26:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T05:15:59Z |
Entry not found
|
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LoneStriker/Mistral-7B-Instruct-v0.2-DARE-5.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T05:23:30Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T05:20:33Z |
---
license: apache-2.0
language:
- en
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a
href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp)
2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling)
3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B)
- base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
The yaml config file for this model is here:
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: bfloat16
merge_method: dare_ties
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
parameters:
density: 0.8
weight: 0.4
- model: Q-bert/MetaMath-Cybertron-Starling
parameters:
density: 0.8
weight: 0.3
- model: v1olet/v1olet_marcoroni-go-bruins-merge-7B
parameters:
density: 0.8
weight: 0.3
parameters:
int8_mask: true
```
# Prompt template:
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- **Alpaca**
```
{system_message}
### Instruction:
{prompt}
### Response:
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)

# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
|
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] |
greatakela/test-trainer
|
greatakela
| 2023-12-17T00:14:39Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-16T05:24:43Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: test-trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test-trainer
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4362
- Accuracy: 0.8056
- F1: 0.8066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4436 | 1.0 | 6787 | 0.4530 | 0.7934 | 0.7896 |
| 0.4519 | 2.0 | 13574 | 0.4363 | 0.8016 | 0.7982 |
| 0.4187 | 3.0 | 20361 | 0.4362 | 0.8056 | 0.8066 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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beauty-beach-keto-gummies/beauty-beach-keto-gummies
|
beauty-beach-keto-gummies
| 2023-12-16T05:25:34Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"Beauty Beach Keto Gummies",
"en",
"license:c-uda",
"region:us"
] | null | 2023-12-16T05:24:52Z |
---
license: c-uda
language:
- en
library_name: sentence-transformers
tags:
- Beauty Beach Keto Gummies
---
[Beauty Beach Keto Gummies](https://atozsupplement.com/beauty-beach-keto-gummies/) Notwithstanding, it's vital to take note of that despite the fact that they are low in carbs and sugar, they ought to in any case be consumed with some restraint as a feature of a fair diet.As forever, it's prescribed to peruse the nourishment marks and fixings list cautiously prior to buying any keto gummies to guarantee they line up with your dietary objectives and inclinations.
VISIT HERE FOR OFFICIAL WEBSITE:-https://atozsupplement.com/beauty-beach-keto-gummies/
|
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LoneStriker/Mistral-7B-Instruct-v0.2-DARE-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T05:29:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T05:25:44Z |
---
license: apache-2.0
language:
- en
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a
href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp)
2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling)
3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B)
- base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
The yaml config file for this model is here:
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: bfloat16
merge_method: dare_ties
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
parameters:
density: 0.8
weight: 0.4
- model: Q-bert/MetaMath-Cybertron-Starling
parameters:
density: 0.8
weight: 0.3
- model: v1olet/v1olet_marcoroni-go-bruins-merge-7B
parameters:
density: 0.8
weight: 0.3
parameters:
int8_mask: true
```
# Prompt template:
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- **Alpaca**
```
{system_message}
### Instruction:
{prompt}
### Response:
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)

# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
|
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vihangd/dopeyshearedplats-1.3b-v1-gguf
|
vihangd
| 2023-12-16T16:00:23Z | 0 | 0 | null |
[
"gguf",
"license:llama2",
"region:us"
] | null | 2023-12-16T05:26:08Z |
Invalid username or password.
|
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] |
bachngo/meta-Llama-2-7b-chat-vi-wiki
|
bachngo
| 2023-12-16T05:41:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gguf",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T05:26:26Z |
Entry not found
|
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] |
LoneStriker/Mistral-7B-Instruct-v0.2-DARE-8.0bpw-h8-exl2-2
|
LoneStriker
| 2023-12-16T05:35:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T05:30:54Z |
---
license: apache-2.0
language:
- en
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/janhq/jan/assets/89722390/35daac7d-b895-487c-a6ac-6663daaad78e" alt="Jan banner" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<p align="center">
<a
href="https://jan.ai/">Jan</a>
- <a href="https://discord.gg/AsJ8krTT3N">Discord</a>
</p>
<!-- header end -->
# Model Description
This model uses the `DARE` method to merge [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) with 3 leading models in 12th Dec on [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard):
1. [OpenHermes-2.5-neural-chat-v3-3-Slerp](https://huggingface.co/Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp)
2. [MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling)
3. [v1olet_marcoroni-go-bruins-merge-7B](https://huggingface.co/v1olet/v1olet_marcoroni-go-bruins-merge-7B)
- base model: [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
The yaml config file for this model is here:
```yaml
base_model: mistralai/Mistral-7B-Instruct-v0.2
dtype: bfloat16
merge_method: dare_ties
models:
- model: mistralai/Mistral-7B-Instruct-v0.2
- model: Weyaxi/OpenHermes-2.5-neural-chat-v3-3-Slerp
parameters:
density: 0.8
weight: 0.4
- model: Q-bert/MetaMath-Cybertron-Starling
parameters:
density: 0.8
weight: 0.3
- model: v1olet/v1olet_marcoroni-go-bruins-merge-7B
parameters:
density: 0.8
weight: 0.3
parameters:
int8_mask: true
```
# Prompt template:
- **ChatML**
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- **Alpaca**
```
{system_message}
### Instruction:
{prompt}
### Response:
```
# Run this model
You can run this model using [Jan Desktop](https://jan.ai/) on Mac, Windows, or Linux.
Jan is an open source, ChatGPT alternative that is:
- 💻 **100% offline on your machine**: Your conversations remain confidential, and visible only to you.
- 🗂️ **An Open File Format**: Conversations and model settings stay on your computer and can be exported or deleted at any time.
- 🌐 **OpenAI Compatible**: Local server on port `1337` with OpenAI compatible endpoints
- 🌍 **Open Source & Free**: We build in public; check out our [Github](https://github.com/janhq)

# About Jan
Jan believes in the need for an open-source AI ecosystem and is building the infra and tooling to allow open-source AIs to compete on a level playing field with proprietary ones.
Jan's long-term vision is to build a cognitive framework for future robots, who are practical, useful assistants for humans and businesses in everyday life.
# Jan Model Merger
This is a test project for merging models.
# Open LLM Leaderboard Evaluation Results
Detailed results can be found here.
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | ?|
| ARC (25-shot) | ? |
| HellaSwag (10-shot) | ? |
| MMLU (5-shot) | ?|
| TruthfulQA (0-shot) | ? |
| Winogrande (5-shot) | ? |
| GSM8K (5-shot) | ? |
# Acknowlegement
- [mergekit](https://github.com/cg123/mergekit)
- [DARE](https://github.com/yule-BUAA/MergeLM/blob/main/README.md)
- [SLERP](https://github.com/Digitous/LLM-SLERP-Merge)
- [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
|
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ChavyvAkvar/Mistral-7B-Instruct-v0.2-Nectar
|
ChavyvAkvar
| 2023-12-16T05:33:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T05:33:21Z |
Entry not found
|
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] |
aaa12963337/msi-dinat-mini-pretrain
|
aaa12963337
| 2023-12-16T09:41:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"dinat",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:shi-labs/dinat-mini-in1k-224",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T05:36:33Z |
---
license: mit
base_model: shi-labs/dinat-mini-in1k-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi-dinat-mini-pretrain
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8937325905292479
---
<!-- 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. -->
# msi-dinat-mini-pretrain
This model is a fine-tuned version of [shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5378
- Accuracy: 0.8937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1041 | 1.0 | 1562 | 0.4092 | 0.8756 |
| 0.0527 | 2.0 | 3125 | 0.6298 | 0.8765 |
| 0.0611 | 3.0 | 4686 | 0.5378 | 0.8937 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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tushar6378/mnnk
|
tushar6378
| 2023-12-16T06:28:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T05:38:20Z |
Entry not found
|
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] |
aaa12963337/msi-resnet-18-pretrain
|
aaa12963337
| 2023-12-16T08:15:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/resnet-18",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T05:43:34Z |
---
license: apache-2.0
base_model: microsoft/resnet-18
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi-resnet-18-pretrain
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8675487465181059
---
<!-- 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. -->
# msi-resnet-18-pretrain
This model is a fine-tuned version of [microsoft/resnet-18](https://huggingface.co/microsoft/resnet-18) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4121
- Accuracy: 0.8675
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1724 | 1.0 | 1562 | 0.3597 | 0.8806 |
| 0.0543 | 2.0 | 3125 | 0.3707 | 0.8875 |
| 0.0834 | 3.0 | 4686 | 0.4121 | 0.8675 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
v2ray/SchizoGPT-8x7B-QLoRA
|
v2ray
| 2023-12-16T06:12:01Z | 0 | 0 | null |
[
"safetensors",
"not-for-all-audiences",
"conversational",
"en",
"dataset:v2ray/r-chatgpt-general-dump",
"license:mit",
"region:us"
] |
conversational
| 2023-12-16T05:53:05Z |
---
license: mit
datasets:
- v2ray/r-chatgpt-general-dump
language:
- en
pipeline_tag: conversational
tags:
- not-for-all-audiences
---
# SchizoGPT 8x7B QLoRA
Fine tuned on r/ChatGPT Discord #general dump.
## Prompt Template
```
### Username
Message(s)(Can be stacked, separated by new line)
### Username(Can have multiturn multiuser chat, separated by new line)
(End of prompt)
```
Use `@username` to ping a user and `#channel name` to mention a channel. \
Prepend `<Re: username>` before a message to respond to a user. \
Use `<filename.ext>` to mention a file in a link, for example, if you have `https://example.com/image.jpg`, use `<image.jpg>`.
|
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linhqyy/animatediff-v3
|
linhqyy
| 2023-12-16T06:45:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T05:56:17Z |
Entry not found
|
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] |
LoneStriker/Metis-0.1-3.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T05:59:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T05:57:45Z |
---
base_model: HuggingFaceH4/mistral-7b-sft-beta
inference: false
license: mit
license_name: mit
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
metrics:
- accuracy
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
It works well with long system prompts.
It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension.
This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset.
# Prompt Format:
```
### System:
{add here the system prompt}
### Instruction:
{add here the instruction}
### Response:
{make sure you have a new line here}
```
|
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Annakingee/CalmCBDGummies
|
Annakingee
| 2023-12-16T06:00:41Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T06:00:23Z |
---
license: apache-2.0
---
➥✅ Official Website: Renew Calm CBD Gummies
➥✅ Product Name: Renew Calm CBD Gummies
➥✅ Benefits : Renew Calm CBD Gummies Helps you to get rid of chronic pain & aches.
➥✅ Category: Pain Relief Supplement
➥✅ Rating: ★★★★☆ (4.5/5.0)as
➥✅ Side Effects: No Major Side Effects
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Augyfyuse/uygu
|
Augyfyuse
| 2023-12-16T06:02:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T06:02:37Z |
Entry not found
|
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] |
LoneStriker/Metis-0.1-4.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T06:05:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T06:02:56Z |
---
base_model: HuggingFaceH4/mistral-7b-sft-beta
inference: false
license: mit
license_name: mit
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
metrics:
- accuracy
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
It works well with long system prompts.
It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension.
This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset.
# Prompt Format:
```
### System:
{add here the system prompt}
### Instruction:
{add here the instruction}
### Response:
{make sure you have a new line here}
```
|
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salma-remyx/corgy_dog_LoRA
|
salma-remyx
| 2023-12-16T06:03:19Z | 0 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-12-16T06:03:18Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of TOK dog
license: openrail++
---
# SDXL LoRA DreamBooth - salma-remyx/corgy_dog_LoRA
<Gallery />
## Model description
These are salma-remyx/corgy_dog_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK dog to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](salma-remyx/corgy_dog_LoRA/tree/main) them in the Files & versions tab.
|
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] |
seojin0128/hw-midm-7B-nsmc
|
seojin0128
| 2023-12-16T12:16:23Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"dataset:nsmc",
"base_model:KT-AI/midm-bitext-S-7B-inst-v1",
"region:us"
] | null | 2023-12-16T06:07:51Z |
---
library_name: peft
base_model: KT-AI/midm-bitext-S-7B-inst-v1
datasets:
- nsmc
metrics:
- accuracy 90.0%
---
# Model Card for Model ID
## Model Description
### midm-bitext-S-7B-inst-v1 미세 튜닝
해당 모델은 네이버 영화 리뷰 데이터셋인 NSMC에 대해 KT-AI/midm-bitext-S-7B-inst-v1을 미세튜닝한 모델입니다.
영화 리뷰 텍스트를 프롬프트에 포함하여 모델에 입력시,'긍정' 또는 '부정' 이라고 예측 텍스트를 직접 생성합니다.
결과적으로, 정확도 90.0%를 가지는 모델을 완성했습니다.
### Train, Test 데이터셋
해당 모델은 NSMC의 train 데이터의 상위 2,000개의 샘플을 학습에 사용했습니다.
해당 모델은 NSMC의 test 데이터의 상위 1,000개의 샘플을 평가에 사용했습니다.
## Training_step_loss

## Confusion_Matrix

## Accuracy_Classification_Report

## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0
|
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LoneStriker/Metis-0.1-5.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T06:11:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T06:08:05Z |
---
base_model: HuggingFaceH4/mistral-7b-sft-beta
inference: false
license: mit
license_name: mit
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
metrics:
- accuracy
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
It works well with long system prompts.
It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension.
This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset.
# Prompt Format:
```
### System:
{add here the system prompt}
### Instruction:
{add here the instruction}
### Response:
{make sure you have a new line here}
```
|
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] |
AI-Wheelz/NN-TraumaV2
|
AI-Wheelz
| 2023-12-16T06:09:53Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T06:08:23Z |
---
license: openrail
---
|
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] |
vivekdugale/Llama-2-7b-chat-finetuned_e50_172
|
vivekdugale
| 2023-12-16T17:55:04Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T06:08:50Z |
Entry not found
|
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ConnieLawso/BenCarsonCBDGummies
|
ConnieLawso
| 2023-12-16T06:09:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T06:08:58Z |
➥✅ Official Website: Ben Carson CBD Gummies
➥✅ Product Name: Ben Carson CBD Gummies
➥✅ Benefits : Ben Carson CBD Gummies Helps you to get rid of chronic pain & aches.
➥✅ Category: Pain Relief Supplement
➥✅ Rating: ★★★★☆ (4.5/5.0)as
➥✅ Side Effects: No Major Side Effects
➥✅ Availability: In Stock Voted #1 Product in the USA
OFFICIAL BLOGS @@@
https://www.facebook.com/BenCarsonCBDGummies/
https://www.facebook.com/Ben.Carson.CBD.Gummies.Official/
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/w-J7BJCZrl8
https://groups.google.com/g/comp.os.vms/c/FUCKAzZX9II
https://groups.google.com/g/mozilla.dev.platform/c/YbPSazUUE5A
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Rleew1udH9o
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/QWJIdh1Wbj4
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/2sdDU7Twug8
https://groups.google.com/g/ben-carson-cbd-gummies-health/c/JuzTrJ6d8r0
https://groups.google.com/g/ben-carson-cbd-gummies-price/c/HYs-acGR7AU
https://sites.google.com/view/bencarsoncbdgummies/home
https://sites.google.com/view/bencarsoncbdgummy/home
https://ben-carson-cbd-gummies-health.webflow.io/
https://medium.com/@danandersoneeee/ben-carson-cbd-gummies-fake-exposed-2023-ingredients-price-and-hidden-side-effects-e01888cf4ac2
https://medium.com/@danandersoneeee/ben-carson-cbd-gummies-scam-warning-beware-alert-also-about-read-before-buying-47a0a4dffb81
https://ben-carson-cbd-gummies.company.site/
https://gamma.app/public/Ben-Carson-CBD-Gummies-Legit-or-Scam-Updated-2023-Beware-Fake-Sho-7z6fclln1p4voky?mode=doc
https://gamma.app/public/Ben-Carson-CBD-Gummies-Fraudulent-Exposed-Reviews-Ingredients-Whe-iw1hjelttpafy8q?mode=doc
FACEBOOKS@@@
https://www.facebook.com/PureHarmonyCBDGummiesUS/
https://www.facebook.com/Pure.Harmony.CBD.Gummies.Official/
https://www.facebook.com/Renew.Calm.CBD.Gummies.Official/
https://www.facebook.com/RenewCalmCBDGummies/
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|
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LoneStriker/Metis-0.1-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-16T06:16:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T06:13:21Z |
---
base_model: HuggingFaceH4/mistral-7b-sft-beta
inference: false
license: mit
license_name: mit
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
metrics:
- accuracy
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
It works well with long system prompts.
It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension.
This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset.
# Prompt Format:
```
### System:
{add here the system prompt}
### Instruction:
{add here the instruction}
### Response:
{make sure you have a new line here}
```
|
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gatarnali/PureHarmonyCBDGummiesshop
|
gatarnali
| 2023-12-16T06:21:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T06:14:39Z |
<p style="text-align: justify;"><span style="background-color:#ffffff;font-size:18pt;">Official Website ➲➲➲ </span></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/jY7U7ZNdxZI"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/jY7U7ZNdxZI</span></u></a></p>
<p><br></p>
<p style="text-align: justify;"><span style="background-color:#ffffff;font-size:18pt;">Product Name</span><strong><span style="background-color:#ffffff;font-size:18pt;"> ➲➲➲ </span></strong><a href="https://www.facebook.com/PureHarmonyCBDGummiesInUS/"><u><span style="color:#1155cc;background-color:#ffffff;font-size:18pt;">Pure Harmony CBD Gummies</span></u></a></p>
<p><br></p>
<p style="text-align: justify;"><span style="background-color:#ffffff;font-size:15pt;">In this thorough survey, we will investigate the key parts, elements, benefits, and burdens of consuming Unadulterated Pure Harmony CBD Gummies. </span></p>
<p><br></p>
<p style="text-align: justify;"><span style="background-color:#ffffff;font-size:18pt;">Official facebook Link ➲➲➲ </span></p>
<p style="text-align: justify;"><a href="https://www.facebook.com/PureHarmonyCBDGummiesInUS/"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://www.facebook.com/PureHarmonyCBDGummiesInUS/</span></u></a></p>
<p><br></p>
<p style="text-align: justify;"><span style="background-color:#ffffff;font-size:18pt;">Official Blogs ➲➲➲</span></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/bdIuanVqaSo"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/bdIuanVqaSo</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xziKYac3e08"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xziKYac3e08</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Dmp2FKu2oAk"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Dmp2FKu2oAk</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/RHYyXhGF1bc"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/RHYyXhGF1bc</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/fVWUATRRXuM"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/fVWUATRRXuM</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/spE-984vkmE"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/spE-984vkmE</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/jAvshYs-xjw"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/jAvshYs-xjw</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/rI0w7MrH4Og"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/rI0w7MrH4Og</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/yWIUROCJzUA"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/yWIUROCJzUA</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/vd3_UjjlwkA"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/vd3_UjjlwkA</span></u></a></p>
<p style="text-align: justify;"><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/VFt_-d_YbY0"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/VFt_-d_YbY0</span></u></a></p>
<p style="text-align: justify;"><a href="https://www.linkedin.com/events/pureharmonycbdgummies7140230514451312640/"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://www.linkedin.com/events/pureharmonycbdgummies7140230514451312640/</span></u></a></p>
<p style="text-align: justify;"><a href="https://www.linkedin.com/events/pureharmonycbdgummiesreviews7140230816466362368/"><u><span style="color:#1155cc;background-color:#ffffff;font-size:15pt;">https://www.linkedin.com/events/pureharmonycbdgummiesreviews7140230816466362368/</span></u></a></p>
|
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Rijgersberg/GEITje-7B-chat-gguf
|
Rijgersberg
| 2023-12-16T08:03:09Z | 0 | 2 | null |
[
"gguf",
"GEITje",
"conversational",
"nl",
"dataset:Rijgersberg/no_robots_nl",
"dataset:Rijgersberg/ultrachat_10k_nl",
"license:apache-2.0",
"region:us"
] |
conversational
| 2023-12-16T06:14:50Z |
---
license: apache-2.0
datasets:
- Rijgersberg/no_robots_nl
- Rijgersberg/ultrachat_10k_nl
language:
- nl
pipeline_tag: conversational
tags:
- GEITje
---
GEITje-7B-chat GGUF q8_0
========================
GGUF version of [Rijgersberg/GEITje-7B-chat](https://huggingface.co/Rijgersberg/GEITje-7B-chat), using the `q8_0` settings.
Produced with [llama.ccp](https://github.com/ggerganov/llama.cpp):
```bash
$ python3 convert.py GEITje-7B-chat-hf \
--outfile GEITje-7B-chat.gguf \
--outtype q8_0
```
For more info on GEITje-chat, please visit:
- [README](https://github.com/Rijgersberg/GEITje/blob/main/README-en.md) on GitHub
- [Model card](https://huggingface.co/Rijgersberg/GEITje-7B-chat) on 🤗 Hugging Face Models
|
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] |
LoneStriker/Metis-0.1-8.0bpw-h8-exl2-2
|
LoneStriker
| 2023-12-16T06:23:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T06:18:31Z |
---
base_model: HuggingFaceH4/mistral-7b-sft-beta
inference: false
license: mit
license_name: mit
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
metrics:
- accuracy
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
It works well with long system prompts.
It isn't generic in a sense that it shouldn't be used for story telling, for example, but only for reasoning and text comprehension.
This model is trained on a private dataset. The high GSM8K score is **NOT** because of the MetaMath dataset.
# Prompt Format:
```
### System:
{add here the system prompt}
### Instruction:
{add here the instruction}
### Response:
{make sure you have a new line here}
```
|
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rebirthcbdgummies-ca/rebirth-cbd-gummies-canada
|
rebirthcbdgummies-ca
| 2023-12-16T06:23:03Z | 0 | 0 | null |
[
"rebirth-cbd-gummies-canada",
"en",
"license:openrail",
"region:us"
] | null | 2023-12-16T06:20:11Z |
---
license: openrail
language:
- en
tags:
- rebirth-cbd-gummies-canada
---
[Rebirth CBD Gummies Reviews](https://farmscbdoil.com/rebirth-cbd-gummies/) — The quantity of individuals with serious emotional health issues like tension, gloom, and consistent torment is going up. These are only a couple of the issues that a commonplace man needs to manage consistently in light of the pressure he feels at work and school and due to the heaviness of his obligations. Prescriptions that bring down how much dopamine in your body are really great for your health since they help your brain and body rest and feel less anxious. Yet, there are a couple of cases wherein taking an excessive number of prescriptions immediately can be terrible for your health. They are dependable enough that you can rely on them.
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|
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] |
gatarnali/PureHarmonyCBDGummiessale
|
gatarnali
| 2023-12-16T06:22:11Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T06:22:10Z |
---
license: openrail
---
|
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] |
homunculus/Reinforce-CartPole-v1
|
homunculus
| 2023-12-16T06:24:47Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T06:24:38Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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] |
RafaelBds1/Valentina
|
RafaelBds1
| 2023-12-16T06:35:55Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-12-16T06:26:27Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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|
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] |
aaa12963337/msi-dinat-mini
|
aaa12963337
| 2023-12-16T15:07:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"dinat",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T06:26:43Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-dinat-mini
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6307660050321499
- name: F1
type: f1
value: 0.45316219853017287
- name: Precision
type: precision
value: 0.6338497176777182
- name: Recall
type: recall
value: 0.3526379379782521
---
<!-- 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. -->
# msi-dinat-mini
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8735
- Accuracy: 0.6308
- F1: 0.4532
- Precision: 0.6338
- Recall: 0.3526
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5414 | 1.0 | 2015 | 0.7584 | 0.5874 | 0.3960 | 0.5427 | 0.3117 |
| 0.4715 | 2.0 | 4031 | 0.7695 | 0.6208 | 0.4593 | 0.6021 | 0.3712 |
| 0.4159 | 3.0 | 6047 | 0.7922 | 0.6230 | 0.4637 | 0.6056 | 0.3757 |
| 0.3774 | 4.0 | 8063 | 0.8166 | 0.6286 | 0.4589 | 0.6235 | 0.3630 |
| 0.3635 | 5.0 | 10078 | 0.8123 | 0.6349 | 0.4889 | 0.6225 | 0.4026 |
| 0.3471 | 6.0 | 12094 | 0.8481 | 0.6265 | 0.4575 | 0.6186 | 0.3630 |
| 0.3616 | 7.0 | 14110 | 0.8605 | 0.6284 | 0.4514 | 0.6279 | 0.3524 |
| 0.3517 | 8.0 | 16126 | 0.8661 | 0.6329 | 0.4600 | 0.6356 | 0.3604 |
| 0.3476 | 9.0 | 18141 | 0.8631 | 0.6330 | 0.4619 | 0.6346 | 0.3631 |
| 0.3469 | 10.0 | 20150 | 0.8735 | 0.6308 | 0.4532 | 0.6338 | 0.3526 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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kato96/Starter
|
kato96
| 2023-12-16T06:37:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T06:36:57Z |
Entry not found
|
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aniketr/WikiBias21
|
aniketr
| 2023-12-17T07:55:34Z | 0 | 0 | null |
[
"text-to-image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2023-12-16T06:40:40Z |
---
license: apache-2.0
pipeline_tag: text-to-image
---
|
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] |
beefsupreme321/b3x6-k1so-v1s8-0
|
beefsupreme321
| 2023-12-16T06:42:10Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-16T06:42:06Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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sogeeking/vqvae_burgers_2
|
sogeeking
| 2023-12-18T00:07:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pdetokenizer",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T06:45:17Z |
Entry not found
|
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] |
tiennguyen/VNCOOK
|
tiennguyen
| 2023-12-16T07:34:42Z | 0 | 0 | null |
[
"image-classification",
"arxiv:1910.09700",
"license:mit",
"region:us"
] |
image-classification
| 2023-12-16T06:49:09Z |
---
license: mit
metrics:
- accuracy
pipeline_tag: image-classification
---
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|
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] |
alwint3r/my_awesome_eli5_clm-model
|
alwint3r
| 2023-12-16T07:31:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T06:52:45Z |
---
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7387
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8743 | 1.0 | 1120 | 3.7583 |
| 3.7694 | 2.0 | 2240 | 3.7403 |
| 3.7272 | 3.0 | 3360 | 3.7387 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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sign/signwriting-illustration
|
sign
| 2023-12-16T06:54:37Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T06:54:37Z |
---
license: mit
---
|
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] |
PeterV09/mistral-7b-300k-10k-a100-6e-valid-1
|
PeterV09
| 2023-12-16T07:03:21Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T06:55:39Z |
---
tags:
- generated_from_trainer
model-index:
- name: mistral-7b-300k-10k-a100-6e-valid-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-300k-10k-a100-6e-valid-1
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 128
- total_train_batch_size: 512
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 6.0
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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] |
HsiehMengTing/esm2_t12_35M_UR50D-finetuned-localization
|
HsiehMengTing
| 2023-12-16T08:38:06Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"esm",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-16T06:58:07Z |
Entry not found
|
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] |
johnslegers/testlora4
|
johnslegers
| 2023-12-16T14:48:11Z | 0 | 0 | null |
[
"art",
"stable-diffusion",
"lora",
"ja",
"license:mit",
"license:openrail",
"region:us"
] | null | 2023-12-16T06:58:49Z |
---
license: [mit, openrail]
language:
- ja
tags:
- art
- stable-diffusion
- lora
---
|
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] |
goosebok/maximkrutoi
|
goosebok
| 2023-12-16T07:02:44Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T07:01:48Z |
---
license: openrail
---
|
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kat1203/114514
|
kat1203
| 2023-12-16T07:09:14Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T07:09:14Z |
Entry not found
|
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] |
lpkr/cnn_summarization
|
lpkr
| 2023-12-16T15:05:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-16T07:09:16Z |
Entry not found
|
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Echo22/mini-clip4clip-tokenizer
|
Echo22
| 2023-12-16T07:19:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T07:16:17Z |
Entry not found
|
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] |
donmanjo/manjo1
|
donmanjo
| 2023-12-16T07:26:26Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T07:21:27Z |
---
license: mit
---
|
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-0.0160664189606905,
0.2621476650238037,
0.04487830400466919,
-0.6869442462921143
] |
eunju365/videomae-base-finetuned-ucf101-subset
|
eunju365
| 2023-12-16T08:59:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-16T07:24:15Z |
Entry not found
|
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cyrmaster/mlc-chat-Mistral-7B-Instruct-v0.2-q3f16_1-768
|
cyrmaster
| 2023-12-16T14:10:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T07:24:49Z |
Entry not found
|
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] |
stvhuang/rcr-run-v57whqm7-61968-master-0_20231212T103333-ep10
|
stvhuang
| 2023-12-16T07:32:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T07:31:14Z |
Entry not found
|
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] |
Ericahooooo/testmodel
|
Ericahooooo
| 2023-12-16T08:33:49Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T07:31:17Z |
---
license: mit
---
|
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zi4dbek/pan
|
zi4dbek
| 2023-12-16T07:51:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T07:31:59Z |
Entry not found
|
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] |
coivmn/sullyoon
|
coivmn
| 2023-12-16T07:33:03Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T07:32:19Z |
---
license: openrail
---
|
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lazed-outt-for-real24/MatureScreamLiveMJ
|
lazed-outt-for-real24
| 2023-12-16T07:36:27Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T07:34:41Z |
---
license: openrail
---
|
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praneshgunner/llama-2-7b-chat-medical-gguf
|
praneshgunner
| 2023-12-16T07:40:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T07:35:48Z |
Entry not found
|
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owanr/SBIC-roberta-base-inter-sorted-human_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T07:38:13Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T07:37:51Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-sorted-human_annots_alpha0.5_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SBIC-roberta-base-inter-sorted-human_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9115
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.801 | 1.0 | 12516 | 0.8770 |
| 0.6653 | 2.0 | 25032 | 0.9115 |
| 0.6617 | 3.0 | 37548 | 0.9115 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Mokado/ModelosVoz
|
Mokado
| 2023-12-16T07:41:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T07:40:16Z |
Entry not found
|
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puraviveuk/Puraviveuk
|
puraviveuk
| 2023-12-16T07:43:03Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-12-16T07:43:03Z |
---
license: creativeml-openrail-m
---
|
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arun3936/nllb-200-3.3B-Malayalam_English_Translationt_nllb
|
arun3936
| 2023-12-17T23:58:43Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-16T07:45:40Z |
Entry not found
|
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lombardata/dino-large-2023_12_16-kornia_img-size518_batch-size32_epochs20
|
lombardata
| 2023-12-16T11:49:01Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"dinov2",
"generated_from_trainer",
"base_model:facebook/dinov2-large",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T07:55:46Z |
---
license: apache-2.0
base_model: facebook/dinov2-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dino-large-2023_12_16-kornia_img-size518_batch-size32_epochs20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# dino-large-2023_12_16-kornia_img-size518_batch-size32_epochs20
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1612
- F1 Micro: 0.7696
- F1 Macro: 0.7065
- Roc Auc: 0.8666
- Accuracy: 0.4241
- Learning Rate: 0.01
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:----:|
| No log | 1.0 | 268 | 0.2010 | 0.6522 | 0.5378 | 0.7692 | 0.4098 | 0.01 |
| 0.2142 | 2.0 | 536 | 0.1802 | 0.7267 | 0.6296 | 0.8364 | 0.4055 | 0.01 |
| 0.2142 | 3.0 | 804 | 0.1584 | 0.7616 | 0.7017 | 0.8440 | 0.4530 | 0.01 |
| 0.1898 | 4.0 | 1072 | 0.1590 | 0.7612 | 0.6768 | 0.8548 | 0.4337 | 0.01 |
| 0.1898 | 5.0 | 1340 | 0.1612 | 0.7696 | 0.7065 | 0.8666 | 0.4241 | 0.01 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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] |
yeeyea/Yoon-Suk-Yeol
|
yeeyea
| 2023-12-16T07:57:19Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2023-12-16T07:56:06Z |
---
license: unknown
---
|
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] |
AnonymousAuthorsForISSTA2024/LLM4Fin
|
AnonymousAuthorsForISSTA2024
| 2023-12-16T08:47:18Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T07:56:54Z |
---
license: mit
---
# Introduction
This is model for LLM4FIN, including two models based on rule filtering model and rule element extraction model.
To run the rule filtering model, use
```
model = AutoModelForSequenceClassification.from_pretrained("rule_filtering", num_labels=3)
tokenizer = AutoTokenizer.from_pretrained("rule_filtering")
```
to load the model and tokenizer.
To run the rule element extraction model, use
```
model = AutoModelForTokenClassification.from_pretrained("rule_element_extraction", num_labels=num_labels)
tokenizer = AutoTokenizer.from_pretrained("rule_element_extraction")
```
to load the model and tokenizer.
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freeloader3/odmb
|
freeloader3
| 2023-12-17T15:38:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T08:02:01Z |
Entry not found
|
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UsamaJ07/falcon-7b-instruct-ft-adapters
|
UsamaJ07
| 2023-12-16T08:02:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T08:02:29Z |
Entry not found
|
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owanr/SBIC-roberta-base-inter-sorted-model_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T08:04:25Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T08:04:05Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-sorted-model_annots_alpha0.5_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SBIC-roberta-base-inter-sorted-model_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9928
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.9791 | 1.0 | 12516 | 1.9092 |
| 1.9953 | 2.0 | 25032 | 1.9928 |
| 2.1287 | 3.0 | 37548 | 1.9928 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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sdpkjc/Phoenix-v5-dqn_atari-seed1
|
sdpkjc
| 2023-12-16T08:08:18Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"ALE/Phoenix-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T08:07:50Z |
---
tags:
- ALE/Phoenix-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ALE/Phoenix-v5
type: ALE/Phoenix-v5
metrics:
- type: mean_reward
value: 4097.00 +/- 846.48
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **ALE/Phoenix-v5**
This is a trained model of a DQN agent playing ALE/Phoenix-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/Phoenix-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Phoenix-v5-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/sdpkjc/Phoenix-v5-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Phoenix-v5-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/Phoenix-v5 --track
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'ALE/Phoenix-v5',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0001,
'learning_starts': 80000,
'num_envs': 1,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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] |
simonyeung/ediff11
|
simonyeung
| 2023-12-16T08:24:56Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T08:08:31Z |
---
license: apache-2.0
---
|
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sdhsdashdkh/c
|
sdhsdashdkh
| 2023-12-16T11:53:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T08:10:00Z |
Entry not found
|
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stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep15
|
stvhuang
| 2023-12-16T08:21:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-16T08:18:46Z |
Entry not found
|
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] |
Hazzzardous/RWKV-V5-1b5-Distilled-Translations-Unvalidated
|
Hazzzardous
| 2023-12-16T08:25:27Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T08:19:45Z |
---
license: mit
---
|
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] |
aaa12963337/msi-resnet-18
|
aaa12963337
| 2023-12-16T12:43:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T08:23:08Z |
---
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: msi-resnet-18
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6336664802907465
- name: F1
type: f1
value: 0.5299313932110667
- name: Precision
type: precision
value: 0.5977139389034999
- name: Recall
type: recall
value: 0.4759565042287555
---
<!-- 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. -->
# msi-resnet-18
This model was trained from scratch on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6854
- Accuracy: 0.6337
- F1: 0.5299
- Precision: 0.5977
- Recall: 0.4760
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.499 | 1.0 | 2015 | 0.7028 | 0.6189 | 0.4730 | 0.5911 | 0.3942 |
| 0.4738 | 2.0 | 4031 | 0.7003 | 0.6268 | 0.4981 | 0.5979 | 0.4268 |
| 0.4788 | 3.0 | 6047 | 0.7195 | 0.6148 | 0.4517 | 0.5906 | 0.3657 |
| 0.4523 | 4.0 | 8060 | 0.6854 | 0.6337 | 0.5299 | 0.5977 | 0.4760 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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0.14529767632484436,
-0.042073071002960205,
-0.8098481297492981
] |
sogeeking/vqvae_burgers_8
|
sogeeking
| 2023-12-18T00:28:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pdetokenizer",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T08:24:56Z |
Entry not found
|
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] |
arnabdhar/roberta-base-ontonotes
|
arnabdhar
| 2023-12-16T08:25:50Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"token-classification",
"pytorch",
"RobertaForTokenClassification",
"ontonotes",
"named-entity-recognition",
"roberta-base",
"generated_from_trainer",
"en",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-16T08:25:14Z |
---
language:
- en
license: mit
base_model: roberta-base
tags:
- pytorch
- RobertaForTokenClassification
- ontonotes
- named-entity-recognition
- roberta-base
- generated_from_trainer
metrics:
- recall
- precision
- f1
- accuracy
model-index:
- name: roberta-base-ontonotes
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-ontonotes
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tner/ontonotes5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0662
- Recall: 0.9255
- Precision: 0.9002
- F1: 0.9127
- Accuracy: 0.9826
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 32
- eval_batch_size: 160
- seed: 75241309
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- training_steps: 6000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Recall | Precision | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:---------:|:------:|:--------:|
| 0.1264 | 0.31 | 600 | 0.1044 | 0.8716 | 0.8475 | 0.8594 | 0.9739 |
| 0.0968 | 0.63 | 1200 | 0.0838 | 0.8998 | 0.8591 | 0.8790 | 0.9769 |
| 0.0881 | 0.94 | 1800 | 0.0768 | 0.9056 | 0.8759 | 0.8905 | 0.9788 |
| 0.0738 | 1.25 | 2400 | 0.0763 | 0.9048 | 0.8827 | 0.8936 | 0.9790 |
| 0.0606 | 1.56 | 3000 | 0.0714 | 0.9190 | 0.8845 | 0.9014 | 0.9808 |
| 0.058 | 1.88 | 3600 | 0.0663 | 0.9231 | 0.8973 | 0.9100 | 0.9820 |
| 0.0379 | 2.19 | 4200 | 0.0667 | 0.9240 | 0.8959 | 0.9098 | 0.9822 |
| 0.0332 | 2.5 | 4800 | 0.0664 | 0.9227 | 0.9008 | 0.9117 | 0.9825 |
| 0.0404 | 2.82 | 5400 | 0.0663 | 0.9247 | 0.9003 | 0.9123 | 0.9824 |
| 0.032 | 3.13 | 6000 | 0.0662 | 0.9255 | 0.9002 | 0.9127 | 0.9826 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
iknowai/Yi-6B-qlora-finetuned-french
|
iknowai
| 2023-12-16T08:25:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:01-ai/Yi-6B",
"region:us"
] | null | 2023-12-16T08:25:19Z |
---
library_name: peft
base_model: 01-ai/Yi-6B
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
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### Framework versions
- PEFT 0.7.2.dev0
|
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aaa12963337/efficientnet-b2-pretrain
|
aaa12963337
| 2023-12-16T08:29:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T08:29:08Z |
Entry not found
|
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] |
owanr/SBIC-roberta-base-intra-sorted-model_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T08:30:30Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T08:30:11Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-intra-sorted-model_annots_alpha0.5_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SBIC-roberta-base-intra-sorted-model_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6884
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6262 | 1.0 | 12516 | 1.6889 |
| 1.6889 | 2.0 | 25032 | 1.6884 |
| 1.5297 | 3.0 | 37548 | 1.6884 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Joshuquez/AnimaleMaleEnhancementSouthAfrica
|
Joshuquez
| 2023-12-16T08:32:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T08:31:03Z |
<h2><span style="color: #008000;"><strong>➢</strong></span><strong><span style="color: #ff0000;">Official website Page — </span> <a href="https://www.facebook.com/AnimaleMaleEnhancementSouthAfricaBuy/">https://www.facebook.com/AnimaleMaleEnhancementSouthAfricaBuy/</a></strong></h2>
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<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementGummiesCapsulesSouthAfrica/"><span style="color: #ff6600;">Disclaimer:</span></a> </strong>The above is a supported post, the perspectives communicated are those of the support/creator and don't address the stand and perspectives on Viewpoint Publication.</p>
<h1 style="text-align: center;"> </h1>
<h3><span style="background-color: #ff6600;"><strong>Animale Male Enhancement South Africa Facebook Pages-</strong></span></h3>
<p><strong><a href="https://www.facebook.com/people/Animale-Male-Enhancement-South-Africa/100092203209665/">https://www.facebook.com/people/Animale-Male-Enhancement-South-Africa/100092203209665/</a> </strong></p>
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<p> </p>
<h3><span style="background-color: #00ccff;"><strong>Animale Male Enhancement South Africa Blogs-</strong></span></h3>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/g7A48in_fLs">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/g7A48in_fLs</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/DBp51Urbohs">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/DBp51Urbohs</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/iVhQecCGzQw">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/iVhQecCGzQw</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/c-wFuwMgnFQ">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/c-wFuwMgnFQ</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Aw9tSvmSmKk">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Aw9tSvmSmKk</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/F7Ott0PGKcE">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/F7Ott0PGKcE</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/AeDda5EuV78">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/AeDda5EuV78</a> </strong></p>
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] |
sdpkjc/Qbert-v5-dqn_atari-seed1
|
sdpkjc
| 2023-12-16T08:31:49Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"ALE/Qbert-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T08:31:18Z |
---
tags:
- ALE/Qbert-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ALE/Qbert-v5
type: ALE/Qbert-v5
metrics:
- type: mean_reward
value: 9930.00 +/- 2476.26
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **ALE/Qbert-v5**
This is a trained model of a DQN agent playing ALE/Qbert-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/Qbert-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/Qbert-v5-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/sdpkjc/Qbert-v5-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/Qbert-v5-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/Qbert-v5 --track
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'ALE/Qbert-v5',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0001,
'learning_starts': 80000,
'num_envs': 1,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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Joshuquez/AnimaleMaleEnhancementSouthAfricaoffer
|
Joshuquez
| 2023-12-16T08:33:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T08:32:44Z |
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<p><strong><a href="https://groups.google.com/g/animale-male-enhancement-gummies--capsules-south-africa/c/VgcEIqv9afc">https://groups.google.com/g/animale-male-enhancement-gummies--capsules-south-africa/c/VgcEIqv9afc</a> </strong></p>
<p><strong><a href="https://groups.google.com/g/animale-male-enhancement-gummies--capsules-south-africa/c/TrOXo30F_I4">https://groups.google.com/g/animale-male-enhancement-gummies--capsules-south-africa/c/TrOXo30F_I4</a> </strong></p>
<p><strong><a href="https://lookerstudio.google.com/reporting/d12e9358-9753-4fa4-9427-3551a662342f/page/KjSRD">https://lookerstudio.google.com/reporting/d12e9358-9753-4fa4-9427-3551a662342f/page/KjSRD</a> </strong></p>
<p><strong><a href="https://lookerstudio.google.com/reporting/554936a2-a2ba-461b-8d55-bcf9804e0372/page/4i6aD">https://lookerstudio.google.com/reporting/554936a2-a2ba-461b-8d55-bcf9804e0372/page/4i6aD</a> </strong></p>
<p> </p>
<h3><span style="background-color: #ff6600;"><strong>Animale Male Enhancement Australia Facebook Pages-</strong></span></h3>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInAU/">https://www.facebook.com/AnimaleMaleEnhancementInAU/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementPills/">https://www.facebook.com/AnimaleMaleEnhancementPills/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInAustralia/">https://www.facebook.com/AnimaleMaleEnhancementInAustralia/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementAustraliaAU/">https://www.facebook.com/AnimaleMaleEnhancementAustraliaAU/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementAUAustralia/">https://www.facebook.com/AnimaleMaleEnhancementAUAustralia/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementAustraliaOfficial/">https://www.facebook.com/AnimaleMaleEnhancementAustraliaOfficial/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementGummiesCapsulesAustralia/">https://www.facebook.com/AnimaleMaleEnhancementGummiesCapsulesAustralia/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/people/Animale-Male-Enhancement-Australia/100093487323174/">https://www.facebook.com/people/Animale-Male-Enhancement-Australia/100093487323174/</a> </strong></p>
<p> </p>
<h3><span style="background-color: #99cc00;"><strong>Animale Male Enhancement Uruguay Facebook Pages-</strong></span></h3>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInUY/">https://www.facebook.com/AnimaleMaleEnhancementInUY/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementUruguay/">https://www.facebook.com/AnimaleMaleEnhancementUruguay/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementUYUruguay/">https://www.facebook.com/AnimaleMaleEnhancementUYUruguay/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementUruguayUY/">https://www.facebook.com/AnimaleMaleEnhancementUruguayUY/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementUruguayPrice/">https://www.facebook.com/AnimaleMaleEnhancementUruguayPrice/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/people/Animale-Male-Enhancement-Uruguay/100090983842331/">https://www.facebook.com/people/Animale-Male-Enhancement-Uruguay/100090983842331/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementPA/">https://www.facebook.com/AnimaleMaleEnhancementPA/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInPA/">https://www.facebook.com/AnimaleMaleEnhancementInPA/</a> </strong></p>
<p> </p>
<h3><span style="background-color: #ff00ff;"><strong>Animale Male Enhancement VE Facebook Pages-</strong></span></h3>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementVe/">https://www.facebook.com/AnimaleMaleEnhancementVe/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInVE/">https://www.facebook.com/AnimaleMaleEnhancementInVE/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementVenezuelaVE/">https://www.facebook.com/AnimaleMaleEnhancementVenezuelaVE/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementVEVenezuela/">https://www.facebook.com/AnimaleMaleEnhancementVEVenezuela/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInVenezuela/">https://www.facebook.com/AnimaleMaleEnhancementInVenezuela/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/people/Animale-Male-Enhancement-Venezuela/100090298477014/">https://www.facebook.com/people/Animale-Male-Enhancement-Venezuela/100090298477014/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementPanama/">https://www.facebook.com/AnimaleMaleEnhancementPanama/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInPanama/">https://www.facebook.com/AnimaleMaleEnhancementInPanama/</a> </strong></p>
<p> </p>
<h3><span style="background-color: #99cc00;"><strong>Animale Male Enhancement NZ Facebook Pages-</strong></span></h3>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementNZ/">https://www.facebook.com/AnimaleMaleEnhancementNZ/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementOfNZ/">https://www.facebook.com/AnimaleMaleEnhancementOfNZ/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementAtNZ/">https://www.facebook.com/AnimaleMaleEnhancementAtNZ/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInNewZealand/">https://www.facebook.com/AnimaleMaleEnhancementInNewZealand/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementOFNewZealand/">https://www.facebook.com/AnimaleMaleEnhancementOFNewZealand/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementAtNewZealand/">https://www.facebook.com/AnimaleMaleEnhancementAtNewZealand/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementMalaysia/">https://www.facebook.com/AnimaleMaleEnhancementMalaysia/</a> </strong></p>
<p><strong><a href="https://www.facebook.com/AnimaleMaleEnhancementInMalaysia/">https://www.facebook.com/AnimaleMaleEnhancementInMalaysia/</a></strong></p>
|
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] |
Homiebear/otamatone
|
Homiebear
| 2023-12-16T08:34:19Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T08:34:03Z |
---
license: openrail
---
|
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] |
Eric01/marian-finetuned-kde4-en-to-cn
|
Eric01
| 2023-12-16T09:11:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-16T08:36:39Z |
Entry not found
|
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] |
MyPdfChat/MyPdfChat
|
MyPdfChat
| 2023-12-16T09:19:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T08:39:50Z |
---
{}
---
# MyPdfChat - Private PDF Chat based on LLM can run on any PC.
**MyPdfChat** is using a private 7B RWKV language model designed to run locally and facilitate secure PDF-based chat conversations. With RWKV, you can have confidential and encrypted conversations in PDF format, ensuring the privacy of your discussions.
## Features
- **Privacy**: MyPdfChat runs locally on your machine, ensuring that your conversations remain private and secure.
- **PDF Chat**: MyPdfChat enables you to have chat conversations within PDF documents, providing a unique and secure communication method.
- **Encryption**: All chat messages are encrypted to protect the confidentiality of your discussions.
- **Offline Access**: Since RWKV runs locally, you can use it even without an internet connection.
## Installation
- To install MyPdfChat from the release, follow these instructions:
- ### Step 1:
- Download the Release1. Go to the [Mychatpdf huggingface repo](https://huggingface.co/MyPdfChat/MyPdfChat).2. Download the latest release zip (`mychatpdf-vX.X.X.zip`).
- ### Step 2:
- Extract the Release1. Locate the downloaded `mychatpdf-vX.X.X.zip` file on your system.2. Extract the contents of the zip file to a directory of your choice.
- ### Step 3:
- double click the MyPdfchat.exe
## Usage1.
- chat with you PDF file
## Contributions
- Contributions to MyPdfChat are welcome! If you encounter any issues or have suggestions for improvements, please open an issue on the [GitHub repository](https://github.com/mypdfchat/MypdfChat).
## License
- MyPdfChat is released under the [MIT License](https://opensource.org/licenses/MIT).
## Acknowledgements
- We would like to thank the open-source community for their invaluable contributions to the development of MyPdfChat.
## Contact
- For any inquiries or support, please contact us at [email protected]---Thank you for using RWKV! We hope you enjoy your private and secure PDF chat experience.
|
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sdpkjc/NameThisGame-v5-dqn_atari-seed1
|
sdpkjc
| 2023-12-16T08:40:57Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"ALE/NameThisGame-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T08:40:00Z |
---
tags:
- ALE/NameThisGame-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ALE/NameThisGame-v5
type: ALE/NameThisGame-v5
metrics:
- type: mean_reward
value: 5250.00 +/- 2013.20
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **ALE/NameThisGame-v5**
This is a trained model of a DQN agent playing ALE/NameThisGame-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/NameThisGame-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/NameThisGame-v5-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/sdpkjc/NameThisGame-v5-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/NameThisGame-v5-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/NameThisGame-v5 --track
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'ALE/NameThisGame-v5',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0001,
'learning_starts': 80000,
'num_envs': 1,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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steimel64/distilhubert-finetuned-gtzan
|
steimel64
| 2023-12-16T08:40:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T08:40:29Z |
Entry not found
|
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TheGreatP/JoaoLucasVoz
|
TheGreatP
| 2023-12-16T08:43:03Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T08:42:47Z |
---
license: openrail
---
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-3
|
kanishka
| 2023-12-16T22:04:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_without_prototypical",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T08:44:58Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_without_prototypical
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_without_prototypical
type: kanishka/counterfactual_babylm_without_prototypical
metrics:
- name: Accuracy
type: accuracy
value: 0.410371177110484
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-aann-no_prototypical-1e-3
This model was trained from scratch on the kanishka/counterfactual_babylm_without_prototypical dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4003
- Accuracy: 0.4104
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.6055 | 1.0 | 18593 | 3.7471 | 0.3605 |
| 3.3898 | 2.0 | 37186 | 3.5520 | 0.3816 |
| 3.2573 | 3.0 | 55779 | 3.4588 | 0.3926 |
| 3.1772 | 4.0 | 74372 | 3.4027 | 0.3981 |
| 3.1222 | 5.0 | 92965 | 3.3932 | 0.4025 |
| 3.0822 | 6.0 | 111558 | 3.3665 | 0.4043 |
| 3.0455 | 7.0 | 130151 | 3.3444 | 0.4067 |
| 3.0126 | 8.0 | 148744 | 3.3516 | 0.4075 |
| 2.9826 | 9.0 | 167337 | 3.3554 | 0.4078 |
| 2.9605 | 10.0 | 185930 | 3.3593 | 0.4088 |
| 2.9434 | 11.0 | 204523 | 3.3505 | 0.4095 |
| 2.9119 | 12.0 | 223116 | 3.3754 | 0.4095 |
| 2.8959 | 13.0 | 241709 | 3.3711 | 0.4097 |
| 2.8775 | 14.0 | 260302 | 3.3739 | 0.4101 |
| 2.8544 | 15.0 | 278895 | 3.3737 | 0.4102 |
| 2.834 | 16.0 | 297488 | 3.3739 | 0.4108 |
| 2.8149 | 17.0 | 316081 | 3.3841 | 0.4103 |
| 2.7956 | 18.0 | 334674 | 3.3839 | 0.4105 |
| 2.775 | 19.0 | 353267 | 3.3899 | 0.4106 |
| 2.7617 | 20.0 | 371860 | 3.4003 | 0.4104 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
wessmetal/poppy
|
wessmetal
| 2023-12-16T08:48:18Z | 0 | 0 | null |
[
"license:bsl-1.0",
"region:us"
] | null | 2023-12-16T08:46:51Z |
---
license: bsl-1.0
---
|
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Ritwik1998/masked-lm-tpu
|
Ritwik1998
| 2023-12-16T08:55:41Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-16T08:49:25Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_keras_callback
model-index:
- name: Ritwik1998/masked-lm-tpu
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Ritwik1998/masked-lm-tpu
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 9.9915
- Train Accuracy: 0.0002
- Validation Loss: 9.9088
- Validation Accuracy: 0.0015
- Epoch: 8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 0.0001, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 0.0001, 'decay_steps': 22325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1175, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.001}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 10.2835 | 0.0000 | 10.2748 | 0.0 | 0 |
| 10.2791 | 0.0000 | 10.2665 | 0.0000 | 1 |
| 10.2586 | 0.0 | 10.2434 | 0.0000 | 2 |
| 10.2455 | 0.0000 | 10.2100 | 0.0000 | 3 |
| 10.2063 | 0.0 | 10.1620 | 0.0000 | 4 |
| 10.1658 | 0.0 | 10.1147 | 0.0 | 5 |
| 10.1174 | 0.0 | 10.0558 | 0.0 | 6 |
| 10.0613 | 0.0000 | 9.9800 | 0.0002 | 7 |
| 9.9915 | 0.0002 | 9.9088 | 0.0015 | 8 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.12.0
- Tokenizers 0.15.0
|
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nand-tmp/gray2color_on_m1guelpf_nouns
|
nand-tmp
| 2023-12-16T13:31:57Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"diffusion",
"color-diffusion",
"black&white2color",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T08:50:30Z |
---
license: apache-2.0
language:
- en
tags:
- diffusion
- color-diffusion
- black&white2color
---
Converting Black&White images to color with Diffusion
Results.

More...
[blog](https://1bytenand.hashnode.dev/unconditional-image-generation)
|
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] |
sdpkjc/BattleZone-v5-dqn_atari-seed1
|
sdpkjc
| 2023-12-16T08:52:20Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"ALE/BattleZone-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T08:51:50Z |
---
tags:
- ALE/BattleZone-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ALE/BattleZone-v5
type: ALE/BattleZone-v5
metrics:
- type: mean_reward
value: 15300.00 +/- 3348.13
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **ALE/BattleZone-v5**
This is a trained model of a DQN agent playing ALE/BattleZone-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/BattleZone-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/BattleZone-v5-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/sdpkjc/BattleZone-v5-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/BattleZone-v5-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/BattleZone-v5 --track
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'ALE/BattleZone-v5',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0001,
'learning_starts': 80000,
'num_envs': 1,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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] |
rudyTzhan/dummy-model
|
rudyTzhan
| 2023-12-16T08:56:16Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-12-16T08:53:26Z |
Entry not found
|
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sdpkjc/DoubleDunk-v5-dqn_atari-seed1
|
sdpkjc
| 2023-12-16T08:56:27Z | 0 | 0 |
cleanrl
|
[
"cleanrl",
"tensorboard",
"ALE/DoubleDunk-v5",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T08:55:58Z |
---
tags:
- ALE/DoubleDunk-v5
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
library_name: cleanrl
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: ALE/DoubleDunk-v5
type: ALE/DoubleDunk-v5
metrics:
- type: mean_reward
value: -12.40 +/- 4.27
name: mean_reward
verified: false
---
# (CleanRL) **DQN** Agent Playing **ALE/DoubleDunk-v5**
This is a trained model of a DQN agent playing ALE/DoubleDunk-v5.
The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the most up-to-date training code can be
found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py).
## Get Started
To use this model, please install the `cleanrl` package with the following command:
```
pip install "cleanrl[dqn_atari]"
python -m cleanrl_utils.enjoy --exp-name dqn_atari --env-id ALE/DoubleDunk-v5
```
Please refer to the [documentation](https://docs.cleanrl.dev/get-started/zoo/) for more detail.
## Command to reproduce the training
```bash
curl -OL https://huggingface.co/sdpkjc/DoubleDunk-v5-dqn_atari-seed1/raw/main/dqn_atari.py
curl -OL https://huggingface.co/sdpkjc/DoubleDunk-v5-dqn_atari-seed1/raw/main/pyproject.toml
curl -OL https://huggingface.co/sdpkjc/DoubleDunk-v5-dqn_atari-seed1/raw/main/poetry.lock
poetry install --all-extras
python dqn_atari.py --save-model --upload-model --hf-entity sdpkjc --env-id ALE/DoubleDunk-v5 --track
```
# Hyperparameters
```python
{'batch_size': 32,
'buffer_size': 1000000,
'capture_video': False,
'cuda': True,
'end_e': 0.01,
'env_id': 'ALE/DoubleDunk-v5',
'exp_name': 'dqn_atari',
'exploration_fraction': 0.1,
'gamma': 0.99,
'hf_entity': 'sdpkjc',
'learning_rate': 0.0001,
'learning_starts': 80000,
'num_envs': 1,
'save_model': True,
'seed': 1,
'start_e': 1,
'target_network_frequency': 1000,
'tau': 1.0,
'torch_deterministic': True,
'total_timesteps': 10000000,
'track': True,
'train_frequency': 4,
'upload_model': True,
'wandb_entity': None,
'wandb_project_name': 'cleanRL'}
```
|
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owanr/SBIC-roberta-base-inter-shuffle-human_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T08:56:43Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T08:56:24Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-shuffle-human_annots_alpha0.5_whole_1e-05
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SBIC-roberta-base-inter-shuffle-human_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2854
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1782 | 1.0 | 12516 | 1.2856 |
| 1.1015 | 2.0 | 25032 | 1.2854 |
| 1.1408 | 3.0 | 37548 | 1.2854 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Ghali20/Zephyr_beta_2
|
Ghali20
| 2023-12-16T08:59:17Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:HuggingFaceH4/zephyr-7b-alpha",
"region:us"
] | null | 2023-12-16T08:58:22Z |
---
library_name: peft
base_model: HuggingFaceH4/zephyr-7b-alpha
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.7.1
|
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] |
Penguin-N/Reinforce-CartPole-v1
|
Penguin-N
| 2023-12-16T09:02:47Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T09:02:37Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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jat-project/jat-medium
|
jat-project
| 2023-12-17T14:58:31Z | 0 | 0 |
transformers
|
[
"transformers",
"jat",
"custom_code",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T09:10:19Z |
Entry not found
|
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