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qingxy/marian-finetuned-kde4-en-to-fr | qingxy | "2024-10-12T00:46:40Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T00:46:40Z" | Entry not found |
satosawa/google-gemma-2b-1728694036 | satosawa | "2024-10-12T00:47:42Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-10-12T00:47:16Z" | ---
base_model: google/gemma-2b
library_name: peft
---
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Seikaijyu/RWKV6-3B-Chn-r0-mini-short-preview | Seikaijyu | "2024-10-12T01:09:43Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-10-12T00:49:22Z" | ---
license: mit
---
|
jenniellama/task-13-google-gemma-2-2b-it | jenniellama | "2024-10-12T00:50:40Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2-2b-it",
"base_model:adapter:google/gemma-2-2b-it",
"region:us"
] | null | "2024-10-12T00:50:20Z" | ---
base_model: google/gemma-2-2b-it
library_name: peft
---
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satosawa/Qwen-Qwen1.5-1.8B-1728694262 | satosawa | "2024-10-12T00:51:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-10-12T00:51:02Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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satosawa/Qwen-Qwen1.5-0.5B-1728694318 | satosawa | "2024-10-12T00:52:25Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-10-12T00:51:58Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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henryyiw/trained-sd3-lora | henryyiw | "2024-10-12T00:52:04Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T00:52:04Z" | Entry not found |
satosawa/Qwen-Qwen1.5-0.5B-1728694348 | satosawa | "2024-10-12T00:52:55Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | "2024-10-12T00:52:29Z" | ---
base_model: Qwen/Qwen1.5-0.5B
library_name: peft
---
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jaymie23/fp16-llama3.1-8b-19 | jaymie23 | "2024-10-12T01:25:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"custom",
"fp16-llama3.1-8b-19",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-12T00:54:20Z" | ---
library_name: transformers
tags:
- custom
- fp16-llama3.1-8b-19
---
# Model Card for Model ID
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satosawa/google-gemma-2b-1728694490 | satosawa | "2024-10-12T00:55:17Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | "2024-10-12T00:54:50Z" | ---
base_model: google/gemma-2b
library_name: peft
---
# Model Card for Model ID
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itbetyar/Safety-hat-object-recognition | itbetyar | "2024-10-12T01:01:32Z" | 0 | 0 | null | [
"object-detection",
"en",
"hu",
"arxiv:1910.09700",
"base_model:Ultralytics/YOLOv8",
"base_model:finetune:Ultralytics/YOLOv8",
"license:apache-2.0",
"region:us"
] | object-detection | "2024-10-12T00:54:52Z" | ---
license: apache-2.0
language:
- en
- hu
base_model:
- Ultralytics/YOLOv8
pipeline_tag: object-detection
---
# Model Card for Model ID
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satosawa/Qwen-Qwen1.5-1.8B-1728694624 | satosawa | "2024-10-12T00:57:29Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-10-12T00:57:04Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
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satosawa/Qwen-Qwen1.5-1.8B-1728694657 | satosawa | "2024-10-12T00:58:05Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-1.8B",
"base_model:adapter:Qwen/Qwen1.5-1.8B",
"region:us"
] | null | "2024-10-12T00:57:37Z" | ---
base_model: Qwen/Qwen1.5-1.8B
library_name: peft
---
# Model Card for Model ID
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[More Information Needed]
## Training Details
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### Framework versions
- PEFT 0.12.0 |
Bakemono/Kallen | Bakemono | "2024-10-12T01:00:11Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T00:59:05Z" | Entry not found |
adipanda/makima-simpletuner-lora-2 | adipanda | "2024-10-12T01:00:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:00:13Z" | ---
license: other
base_model: "black-forest-labs/FLUX.1-dev"
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- safe-for-work
- lora
- template:sd-lora
- lycoris
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'A scene from Chainsaw Man. Makima holding a sign that says ''I LOVE PROMPTS!'', she is standing full body on a beach at sunset. She is wearing a a white shirt, black tie, and black coat. The setting sun casts a dynamic shadow on her face.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
- text: 'A scene from Chainsaw Man. Makima jumping out of a propeller airplane, sky diving. She looks excited and her hair is blowing in the wind. The sky is clear and blue, there are birds pictured in the distance.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_2_0.png
- text: 'A scene from Chainsaw Man. Makima spinning a basketball on her finger on a basketball court. She is wearing a lakers jersey with the #12 on it. The basketball hoop and crowd are in the background cheering for her. She is smiling.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_3_0.png
- text: 'A scene from Chainsaw Man. Makima is wearing a suit in an office shaking the hand of a business man. The man has purple hair and is wearing professional attire. There is a Google logo in the background. It is during daytime, and the overall sentiment is one of accomplishment.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_4_0.png
- text: 'A scene from Chainsaw Man. Makima is fighting a large brown grizzly bear, deep in a forest. The bear is tall and standing on two legs, roaring. The bear is also wearing a crown because it is the king of all bears. Around them are tall trees and other animals watching.'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_5_0.png
---
# makima-simpletuner-lora-2
This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).
No validation prompt was used during training.
None
## Validation settings
- CFG: `3.5`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `None`
- Seed: `42`
- Resolution: `1024x1024`
Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
You can find some example images in the following gallery:
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 20
- Training steps: 250
- Learning rate: 0.0003
- Effective batch size: 48
- Micro-batch size: 48
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matching
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LyCORIS Config:
```json
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 12,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 12
},
"FeedForward": {
"factor": 6
}
}
}
}
```
## Datasets
### makima-512
- Repeats: 2
- Total number of images: 172
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
## Inference
```python
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "An astronaut is riding a horse through the jungles of Thailand."
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1024,
height=1024,
guidance_scale=3.5,
).images[0]
image.save("output.png", format="PNG")
```
|
BellLabs/llama-7b-finetuned-mental-health-conversational | BellLabs | "2024-10-12T01:29:53Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | "2024-10-12T01:01:00Z" | ---
base_model: meta-llama/Llama-2-7b-hf
library_name: peft
license: llama2
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama-7b-finetuned-mental-health-conversational
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. -->
# llama-7b-finetuned-mental-health-conversational
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 120
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.45.1
- Pytorch 2.0.0+cu117
- Datasets 2.21.0
- Tokenizers 0.20.0 |
TheNasca/crrr | TheNasca | "2024-10-12T01:07:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:07:11Z" | Invalid username or password. |
DrewMens/stt-model-augment-LORA-distil-large-v3 | DrewMens | "2024-10-12T01:07:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-10-12T01:07:32Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
dantheoprod/AIBEDNICA | dantheoprod | "2024-10-12T01:08:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:08:32Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: AIBEDNICA
---
# Aibednica
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `AIBEDNICA` to trigger the image generation.
## Use it with the [𧨠diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('dantheoprod/AIBEDNICA', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
maxpmx/output_he-codex_512 | maxpmx | "2024-10-12T01:08:55Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:08:55Z" | Entry not found |
TheNasca/cr | TheNasca | "2024-10-12T01:10:10Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:09:38Z" | Invalid username or password. |
ChrisBentonHann/Fields_Of_Asphodel | ChrisBentonHann | "2024-10-12T01:09:45Z" | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | "2024-10-12T01:09:45Z" | ---
license: bsd-3-clause
---
|
NeuroWhAI/ko-gemma-2-9b-it-fn | NeuroWhAI | "2024-10-12T01:33:37Z" | 0 | 0 | null | [
"safetensors",
"gemma2",
"ko",
"en",
"dataset:heegyu/glaive-function-calling-v2-ko",
"base_model:rtzr/ko-gemma-2-9b-it",
"base_model:finetune:rtzr/ko-gemma-2-9b-it",
"license:gemma",
"region:us"
] | null | "2024-10-12T01:10:27Z" | ---
license: gemma
datasets:
- heegyu/glaive-function-calling-v2-ko
library_name: transformers
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: >-
To access Gemma on Hugging Face, youβre required to review and agree to
Googleβs usage license. To do this, please ensure youβre logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
tags:
- conversational
base_model:
- rtzr/ko-gemma-2-9b-it
language:
- ko
---
## ko-gemma-2-9b-it-fn
|
pavitramalhotra/Malhotra_Pavitra_634005665 | pavitramalhotra | "2024-10-12T01:10:52Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-10-12T01:10:52Z" | ---
license: mit
---
|
b09501048/detr2 | b09501048 | "2024-10-12T01:12:20Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:12:20Z" | ---
library_name: transformers
license: apache-2.0
base_model: b09501048/detr
tags:
- generated_from_trainer
model-index:
- name: detr2
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/djengo890-national-taiwan-university/CVDPL_HW1_DETR/runs/p3ydxc4m)
# detr2
This model is a fine-tuned version of [b09501048/detr](https://huggingface.co/b09501048/detr) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6756
- Map: 0.1051
- Map 50: 0.1903
- Map 75: 0.1015
- Map Small: 0.0033
- Map Medium: 0.0045
- Map Large: 0.118
- Mar 1: 0.0866
- Mar 10: 0.1571
- Mar 100: 0.1653
- Mar Small: 0.0035
- Mar Medium: 0.0137
- Mar Large: 0.1915
- Map Person: 0.5572
- Mar 100 Person: 0.6813
- Map Ear: 0.0504
- Mar 100 Ear: 0.2096
- Map Earmuffs: 0.0
- Mar 100 Earmuffs: 0.0
- Map Face: 0.4079
- Mar 100 Face: 0.5174
- Map Face-guard: 0.0
- Mar 100 Face-guard: 0.0
- Map Face-mask-medical: 0.0
- Mar 100 Face-mask-medical: 0.0
- Map Foot: 0.0
- Mar 100 Foot: 0.0
- Map Tools: 0.0065
- Mar 100 Tools: 0.1896
- Map Glasses: 0.0074
- Mar 100 Glasses: 0.0812
- Map Gloves: 0.0007
- Mar 100 Gloves: 0.009
- Map Helmet: 0.0
- Mar 100 Helmet: 0.0
- Map Hands: 0.2666
- Mar 100 Hands: 0.4266
- Map Head: 0.473
- Mar 100 Head: 0.5522
- Map Medical-suit: 0.0
- Mar 100 Medical-suit: 0.0
- Map Shoes: 0.0168
- Mar 100 Shoes: 0.1435
- Map Safety-suit: 0.0
- Mar 100 Safety-suit: 0.0
- Map Safety-vest: 0.0
- Mar 100 Safety-vest: 0.0
## 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: cosine
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Person | Mar 100 Person | Map Ear | Mar 100 Ear | Map Earmuffs | Mar 100 Earmuffs | Map Face | Mar 100 Face | Map Face-guard | Mar 100 Face-guard | Map Face-mask-medical | Mar 100 Face-mask-medical | Map Foot | Mar 100 Foot | Map Tools | Mar 100 Tools | Map Glasses | Mar 100 Glasses | Map Gloves | Mar 100 Gloves | Map Helmet | Mar 100 Helmet | Map Hands | Mar 100 Hands | Map Head | Mar 100 Head | Map Medical-suit | Mar 100 Medical-suit | Map Shoes | Mar 100 Shoes | Map Safety-suit | Mar 100 Safety-suit | Map Safety-vest | Mar 100 Safety-vest |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:----------:|:--------------:|:-------:|:-----------:|:------------:|:----------------:|:--------:|:------------:|:--------------:|:------------------:|:---------------------:|:-------------------------:|:--------:|:------------:|:---------:|:-------------:|:-----------:|:---------------:|:----------:|:--------------:|:----------:|:--------------:|:---------:|:-------------:|:--------:|:------------:|:----------------:|:--------------------:|:---------:|:-------------:|:---------------:|:-------------------:|:---------------:|:-------------------:|
| No log | 1.0 | 230 | 2.1829 | 0.0605 | 0.1167 | 0.0552 | 0.0009 | 0.0013 | 0.0672 | 0.0586 | 0.1154 | 0.1211 | 0.0007 | 0.0051 | 0.138 | 0.4117 | 0.609 | 0.0032 | 0.1099 | 0.0 | 0.0 | 0.1725 | 0.4168 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0001 | 0.0268 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1194 | 0.3523 | 0.3179 | 0.4968 | 0.0 | 0.0 | 0.0041 | 0.0467 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 460 | 2.0866 | 0.0711 | 0.1339 | 0.0672 | 0.0013 | 0.0011 | 0.079 | 0.0641 | 0.1206 | 0.126 | 0.0018 | 0.0084 | 0.1438 | 0.454 | 0.609 | 0.0052 | 0.1209 | 0.0 | 0.0 | 0.1989 | 0.4211 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0004 | 0.0785 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1608 | 0.3471 | 0.3852 | 0.507 | 0.0 | 0.0 | 0.0038 | 0.058 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.5763 | 3.0 | 690 | 1.9861 | 0.0827 | 0.1539 | 0.0797 | 0.0009 | 0.0019 | 0.0922 | 0.0692 | 0.1281 | 0.1324 | 0.0007 | 0.0068 | 0.1522 | 0.4877 | 0.6318 | 0.0221 | 0.1291 | 0.0 | 0.0 | 0.3016 | 0.4585 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0006 | 0.0826 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1826 | 0.3567 | 0.4063 | 0.5112 | 0.0 | 0.0 | 0.0048 | 0.0814 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.5763 | 4.0 | 920 | 1.8935 | 0.0862 | 0.1625 | 0.0806 | 0.0002 | 0.0033 | 0.0962 | 0.0728 | 0.1312 | 0.136 | 0.0007 | 0.0101 | 0.1562 | 0.4722 | 0.6241 | 0.0232 | 0.1592 | 0.0 | 0.0 | 0.351 | 0.4621 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0009 | 0.1066 | 0.0004 | 0.0044 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1775 | 0.3583 | 0.4342 | 0.5186 | 0.0 | 0.0 | 0.0066 | 0.079 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.32 | 5.0 | 1150 | 1.8171 | 0.0932 | 0.1746 | 0.0899 | 0.0028 | 0.0033 | 0.1042 | 0.079 | 0.1399 | 0.1469 | 0.0028 | 0.0106 | 0.1685 | 0.4946 | 0.6561 | 0.0379 | 0.1757 | 0.0 | 0.0 | 0.3903 | 0.4897 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0011 | 0.1625 | 0.0042 | 0.0262 | 0.0004 | 0.002 | 0.0 | 0.0 | 0.1991 | 0.3702 | 0.4482 | 0.5292 | 0.0 | 0.0 | 0.009 | 0.0858 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.32 | 6.0 | 1380 | 1.7576 | 0.0981 | 0.1822 | 0.0942 | 0.0015 | 0.0038 | 0.11 | 0.0818 | 0.1474 | 0.1541 | 0.0015 | 0.0123 | 0.1785 | 0.5247 | 0.658 | 0.0522 | 0.1868 | 0.0 | 0.0 | 0.3726 | 0.499 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0083 | 0.1448 | 0.0067 | 0.06 | 0.0 | 0.0 | 0.0 | 0.0 | 0.2357 | 0.4101 | 0.4543 | 0.5349 | 0.0 | 0.0 | 0.0125 | 0.1263 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.1983 | 7.0 | 1610 | 1.7219 | 0.1017 | 0.1871 | 0.0961 | 0.0021 | 0.0059 | 0.1138 | 0.0854 | 0.1531 | 0.1601 | 0.0024 | 0.0153 | 0.1847 | 0.547 | 0.6753 | 0.0411 | 0.207 | 0.0 | 0.0 | 0.3952 | 0.4981 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0042 | 0.1893 | 0.0108 | 0.0662 | 0.0005 | 0.0035 | 0.0 | 0.0 | 0.256 | 0.4141 | 0.4619 | 0.5478 | 0.0 | 0.0 | 0.0123 | 0.1201 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.1983 | 8.0 | 1840 | 1.7015 | 0.1025 | 0.1879 | 0.099 | 0.0028 | 0.0048 | 0.1149 | 0.0852 | 0.1556 | 0.1625 | 0.0029 | 0.0143 | 0.1884 | 0.5415 | 0.6693 | 0.0436 | 0.2041 | 0.0 | 0.0 | 0.4018 | 0.5101 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0056 | 0.1713 | 0.0086 | 0.0962 | 0.0 | 0.002 | 0.0 | 0.0 | 0.2632 | 0.4221 | 0.4633 | 0.5424 | 0.0 | 0.0 | 0.0155 | 0.1456 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.0688 | 9.0 | 2070 | 1.6853 | 0.1042 | 0.1896 | 0.0994 | 0.0033 | 0.0046 | 0.117 | 0.0862 | 0.156 | 0.1636 | 0.0033 | 0.014 | 0.1895 | 0.5507 | 0.6784 | 0.0481 | 0.206 | 0.0 | 0.0 | 0.4047 | 0.5127 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.005 | 0.1716 | 0.009 | 0.0887 | 0.0004 | 0.006 | 0.0 | 0.0 | 0.2662 | 0.4255 | 0.4697 | 0.5484 | 0.0 | 0.0 | 0.0169 | 0.1435 | 0.0 | 0.0 | 0.0 | 0.0 |
| 2.0688 | 10.0 | 2300 | 1.6756 | 0.1051 | 0.1903 | 0.1015 | 0.0033 | 0.0045 | 0.118 | 0.0866 | 0.1571 | 0.1653 | 0.0035 | 0.0137 | 0.1915 | 0.5572 | 0.6813 | 0.0504 | 0.2096 | 0.0 | 0.0 | 0.4079 | 0.5174 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0065 | 0.1896 | 0.0074 | 0.0812 | 0.0007 | 0.009 | 0.0 | 0.0 | 0.2666 | 0.4266 | 0.473 | 0.5522 | 0.0 | 0.0 | 0.0168 | 0.1435 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
khalid3117/Sif | khalid3117 | "2024-10-12T01:13:56Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:12:29Z" | A scene depicting vast fields under a scorching sun. Several slaves are seen working hard in the field, with signs of exhaustion and hardship clearly visible on their faces. The atmosphere reflects oppression and harsh conditions, with white masters observing them from a distance. |
0xBreath/llama-holistic-ai | 0xBreath | "2024-10-12T01:13:43Z" | 0 | 0 | null | [
"safetensors",
"llama",
"region:us"
] | null | "2024-10-12T01:13:24Z" | Entry not found |
pandoradox/gaussian_t5_new | pandoradox | "2024-10-12T01:25:00Z" | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | "2024-10-12T01:20:59Z" | Entry not found |
agamgoy/mistral-nemo-instruct-askscience-moderation | agamgoy | "2024-10-12T01:21:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"base_model:finetune:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-10-12T01:21:12Z" | ---
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** agamgoy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
alyzbane/resnet-18-finetuned-Barkley-5C | alyzbane | "2024-10-12T01:21:53Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:21:53Z" | ---
library_name: transformers
license: apache-2.0
base_model: microsoft/resnet-18
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: resnet-18-finetuned-Barkley-5C
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0.0443213296398892
- name: Recall
type: recall
value: 0.21052631578947367
- name: F1
type: f1
value: 0.07322654462242563
- name: Accuracy
type: accuracy
value: 0.21052631578947367
---
<!-- 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. -->
# resnet-18-finetuned-Barkley-5C
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: nan
- Precision: 0.0443
- Recall: 0.2105
- F1: 0.0732
- Accuracy: 0.2105
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 14
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.8322 | 1.0 | 38 | nan | 0.0443 | 0.2105 | 0.0732 | 0.2105 |
| 0.7328 | 2.0 | 76 | nan | 0.0443 | 0.2105 | 0.0732 | 0.2105 |
| 0.5796 | 3.0 | 114 | nan | 0.0443 | 0.2105 | 0.0732 | 0.2105 |
| 0.4638 | 4.0 | 152 | nan | 0.0443 | 0.2105 | 0.0732 | 0.2105 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
tuna2134/matcha_tsukuyomi | tuna2134 | "2024-10-12T01:25:29Z" | 0 | 0 | null | [
"onnx",
"region:us"
] | null | "2024-10-12T01:23:11Z" | Entry not found |
lza1/uce_hf | lza1 | "2024-10-12T01:23:41Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-10-12T01:23:41Z" | ---
license: apache-2.0
---
|
BroAlanTaps/Llama3-instruct-64-12000steps | BroAlanTaps | "2024-10-12T01:29:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-12T01:24:22Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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jkazdan/synthetic-dpo-gemma-tokenizer-2-2b-imdb | jkazdan | "2024-10-12T01:24:45Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:24:44Z" | Entry not found |
jaymie23/fp16-llama3.1-8b-20 | jaymie23 | "2024-10-12T01:25:39Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:25:39Z" | ---
library_name: transformers
tags:
- custom
- fp16-llama3.1-8b-20
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
abdiharyadi/indogpt-yelp-fine-tuned-part-3 | abdiharyadi | "2024-10-12T01:27:28Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:27:28Z" | Entry not found |
BroAlanTaps/GPT2-large-64-12000steps | BroAlanTaps | "2024-10-12T01:31:25Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-12T01:29:40Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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hjsh/textual_inversion_cat | hjsh | "2024-10-12T01:31:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:31:02Z" | ---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
- diffusers-training
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual inversion text2image fine-tuning - hjsh/textual_inversion_cat
These are textual inversion adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
joelstevenn/finetun | joelstevenn | "2024-10-12T01:32:32Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-10-12T01:32:32Z" | ---
license: mit
---
|
sj19500/trained-flux-lora | sj19500 | "2024-10-12T01:33:14Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:33:14Z" | Entry not found |
gglabs/Mistral-Nemo-FC-1010-5-epoch | gglabs | "2024-10-12T01:33:28Z" | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"gguf",
"en",
"base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"base_model:finetune:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-10-12T01:33:27Z" | ---
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
---
# Uploaded model
- **Developed by:** gglabs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
J-LAB/BRisa_v0.1 | J-LAB | "2024-10-12T01:34:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-10-12T01:34:01Z" | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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SwordAndTea/ppo-Huggy | SwordAndTea | "2024-10-12T01:34:29Z" | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | "2024-10-12T01:34:19Z" | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog πΆ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: SwordAndTea/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play π
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