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10.1k
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RazhK/Newmodel1 | RazhK | "2025-02-25T22:36:47" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2025-02-25T21:54:45" | ---
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
--- |
lesso10/6928e628-a636-4c50-938e-ff99497eeb6e | lesso10 | "2025-01-26T06:37:07" | 6 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-32k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-32k",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-26T06:00:32" | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-32k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 6928e628-a636-4c50-938e-ff99497eeb6e
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Solar-10b-32k
bf16: true
chat_template: llama3
datasets:
- data_files:
- 7bb5c8c129066fca_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/7bb5c8c129066fca_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 5
eval_table_size: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: lesso10/6928e628-a636-4c50-938e-ff99497eeb6e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 25
micro_batch_size: 2
mlflow_experiment_name: /tmp/7bb5c8c129066fca_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 10
sequence_len: 512
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 36a43860-f8fa-4c32-afb5-c665be741dc4
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 36a43860-f8fa-4c32-afb5-c665be741dc4
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 6928e628-a636-4c50-938e-ff99497eeb6e
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-32k](https://huggingface.co/NousResearch/Yarn-Solar-10b-32k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0002 | 1 | nan |
| 0.0 | 0.0008 | 5 | nan |
| 0.0 | 0.0016 | 10 | nan |
| 0.0 | 0.0024 | 15 | nan |
| 0.0 | 0.0032 | 20 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
SemilleroCV/resnet50-finetuned-bwmp2-224 | SemilleroCV | "2025-01-12T19:03:11" | 14 | 1 | null | [
"onnx",
"resnet",
"image-classification",
"es",
"en",
"dataset:SemilleroCV/BWMP2",
"doi:10.57967/hf/4039",
"license:mit",
"region:us"
] | image-classification | "2024-09-02T12:48:12" | ---
license: mit
datasets:
- SemilleroCV/BWMP2
language:
- es
- en
pipeline_tag: image-classification
---
# BWMP2: Dataset RGB para Clasificación de Materiales con un Modelo Fundacional Finamente Ajustado"
<p align="center">
<img src="https://github.com/Sneider-exe/Clasificacion_Materiales/raw/main/logo.jpg" alt="Descripción alternativa de la imagen">
</p>
Este proyecto presenta un modelo fundacional(ResNet50) finamente ajustado para la clasificación de materiales. Utilizando un dataset propio de imágenes RGB que contiene cinco clases (Ladrillo, Metal, Madera, Papel, Plástico), el modelo es capaz de identificar y clasificar correctamente una imagen dentro de estas categorías
# Sobre Resnet50
<p align="center">
<img src="https://miro.medium.com/v2/resize:fit:1400/0*tH9evuOFqk8F41FG.png" alt="Descripción alternativa de la imagen">
</p>
Resnet50 es una arquitectura CNN, perteneciente a la familia de ResNet(redes residuales), modelos diseñados para trabajar con el entrenamiento de redes neuronales profundas
Fue desarrollada por investigador de Microsoft Research Asia, conocida por su profundidad y eficiencia en tareas de clasificación de imágenes. |
devlancer/sn29_llama_11 | devlancer | "2025-02-02T14:16:58" | 52 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-02T14:11:13" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
bibuai/pijamaintermiami | bibuai | "2025-01-21T01:38:32" | 181 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-01-21T01:15:43" | ---
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: pijamiami
---
# Pijamaintermiami
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `pijamiami` to trigger the image generation.
## Use it with the [🧨 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('bibuai/pijamaintermiami', 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)
|
Zyphra/Mamba-370M | Zyphra | "2024-03-21T00:03:33" | 36 | 8 | transformers | [
"transformers",
"pytorch",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-02-10T01:44:32" | ---
license: apache-2.0
---
Install Zyphra's fork of mamba-ssm (https://github.com/Zyphra/mamba)
1. `git clone https://github.com/Zyphra/mamba.git`
2. `cd mamba`
3. You need to install from source: `pip install .` or `pip install -e .` for editable mode (if you want to make changes to mamba code).
Then one should be able to load any iteration using the following snippet (say iteration 10,000):
```
from mamba_ssm.models.mixer_seq_simple import MambaLMHeadModel
model = MambaLMHeadModel.from_pretrained("Zyphra/Mamba-370M", iteration=10_000, device="cuda")
```
If iteration is not specified, then the model from the root of the repository is loaded, which is the final iteration (610,351).
The model was trained using `"EleutherAI/gpt-neox-20b"` tokenizer.
Here is a snippet for text generation:
```
import transformers, torch
tokenizer = transformers.AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
inp_ids = torch.as_tensor([tokenizer.encode("Hello! How are you?")]).to("cuda")
out_ids = model.generate(inp_ids, max_length=100)
print(tokenizer.decode(out_ids[0]))
``` |
miknoj/llama3.1-8b-gpro-gsm8k-lora | miknoj | "2025-02-12T18:17:05" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-02-12T18:16:48" | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** miknoj
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF | mradermacher | "2024-12-30T06:53:39" | 36 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2024-12-30T05:58:52" | ---
base_model: DreadPoor/The_Damn_Train-8B-Model_Stock
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/DreadPoor/The_Damn_Train-8B-Model_Stock
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/The_Damn_Train-8B-Model_Stock-i1-GGUF/resolve/main/The_Damn_Train-8B-Model_Stock.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
somnathsingh31/llava-1.5-7b-hf-ft-museum | somnathsingh31 | "2024-05-03T04:15:26" | 4 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:llava-hf/llava-1.5-7b-hf",
"base_model:adapter:llava-hf/llava-1.5-7b-hf",
"region:us"
] | null | "2024-04-30T12:09:56" | ---
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: llava-hf/llava-1.5-7b-hf
model-index:
- name: llava-1.5-7b-hf-ft-museum-updated
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. -->
# llava-1.5-7b-hf-ft-museum-updated
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on the The Metropolitan
Museum of Art(https://www.metmuseum.org/art/collection) dataset.
## Model description
This project designed to enrich museum experiences by enabling users to explore and comprehend museum artifacts through image-based queries.
## Intended uses & limitations
**Intended uses:**
1. Enhance museum visitor experiences through interactive exploration via image-based queries.
2. Aid researchers and curators in retrieving information from museum artifacts.
3. Democratize access to museum knowledge by catering to a broader audience.
**Limitation:**
The model is fine tuned on a subset of data from the Met's collection that pertains to Indian heritage.
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.4e-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
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1 |
MayBashendy/ArabicNewSplits7_FineTuningAraBERT_run1_AugV5_k6_task7_organization | MayBashendy | "2025-01-13T04:13:01" | 10 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-01-13T04:05:22" | ---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits7_FineTuningAraBERT_run1_AugV5_k6_task7_organization
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. -->
# ArabicNewSplits7_FineTuningAraBERT_run1_AugV5_k6_task7_organization
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8725
- Qwk: 0.3867
- Mse: 0.8725
- Rmse: 0.9341
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.1176 | 2 | 2.7268 | -0.0262 | 2.7268 | 1.6513 |
| No log | 0.2353 | 4 | 1.4160 | 0.0771 | 1.4160 | 1.1900 |
| No log | 0.3529 | 6 | 1.0447 | -0.1304 | 1.0447 | 1.0221 |
| No log | 0.4706 | 8 | 1.2835 | -0.2437 | 1.2835 | 1.1329 |
| No log | 0.5882 | 10 | 1.5149 | -0.2448 | 1.5149 | 1.2308 |
| No log | 0.7059 | 12 | 1.6208 | -0.1307 | 1.6208 | 1.2731 |
| No log | 0.8235 | 14 | 1.3323 | -0.1803 | 1.3323 | 1.1542 |
| No log | 0.9412 | 16 | 1.0674 | 0.0327 | 1.0674 | 1.0331 |
| No log | 1.0588 | 18 | 0.9372 | 0.2227 | 0.9372 | 0.9681 |
| No log | 1.1765 | 20 | 0.9146 | 0.2504 | 0.9146 | 0.9564 |
| No log | 1.2941 | 22 | 0.8599 | 0.3425 | 0.8599 | 0.9273 |
| No log | 1.4118 | 24 | 0.8638 | 0.1407 | 0.8638 | 0.9294 |
| No log | 1.5294 | 26 | 0.9672 | 0.0 | 0.9672 | 0.9834 |
| No log | 1.6471 | 28 | 1.0828 | -0.0660 | 1.0828 | 1.0406 |
| No log | 1.7647 | 30 | 1.0018 | -0.0426 | 1.0018 | 1.0009 |
| No log | 1.8824 | 32 | 0.8917 | 0.0 | 0.8917 | 0.9443 |
| No log | 2.0 | 34 | 0.8040 | 0.0327 | 0.8040 | 0.8967 |
| No log | 2.1176 | 36 | 0.8083 | 0.2407 | 0.8083 | 0.8990 |
| No log | 2.2353 | 38 | 0.8258 | 0.2463 | 0.8258 | 0.9087 |
| No log | 2.3529 | 40 | 0.8547 | 0.3294 | 0.8547 | 0.9245 |
| No log | 2.4706 | 42 | 0.8759 | 0.3099 | 0.8759 | 0.9359 |
| No log | 2.5882 | 44 | 0.8845 | 0.0840 | 0.8845 | 0.9405 |
| No log | 2.7059 | 46 | 0.8996 | 0.1327 | 0.8996 | 0.9485 |
| No log | 2.8235 | 48 | 0.8627 | 0.1754 | 0.8627 | 0.9288 |
| No log | 2.9412 | 50 | 0.8310 | 0.1372 | 0.8310 | 0.9116 |
| No log | 3.0588 | 52 | 0.7444 | 0.0428 | 0.7444 | 0.8628 |
| No log | 3.1765 | 54 | 0.7285 | 0.1508 | 0.7285 | 0.8535 |
| No log | 3.2941 | 56 | 0.7495 | 0.1863 | 0.7495 | 0.8657 |
| No log | 3.4118 | 58 | 0.7724 | 0.1407 | 0.7724 | 0.8788 |
| No log | 3.5294 | 60 | 0.8269 | -0.0511 | 0.8269 | 0.9093 |
| No log | 3.6471 | 62 | 0.9254 | -0.0392 | 0.9254 | 0.9620 |
| No log | 3.7647 | 64 | 1.1030 | -0.0264 | 1.1030 | 1.0502 |
| No log | 3.8824 | 66 | 1.2248 | 0.0081 | 1.2248 | 1.1067 |
| No log | 4.0 | 68 | 1.0681 | -0.0112 | 1.0681 | 1.0335 |
| No log | 4.1176 | 70 | 1.0519 | 0.2173 | 1.0519 | 1.0256 |
| No log | 4.2353 | 72 | 1.0235 | 0.2076 | 1.0235 | 1.0117 |
| No log | 4.3529 | 74 | 1.0578 | 0.0120 | 1.0578 | 1.0285 |
| No log | 4.4706 | 76 | 1.0491 | -0.0497 | 1.0491 | 1.0243 |
| No log | 4.5882 | 78 | 1.0570 | 0.1101 | 1.0570 | 1.0281 |
| No log | 4.7059 | 80 | 1.0229 | 0.1289 | 1.0229 | 1.0114 |
| No log | 4.8235 | 82 | 1.0278 | 0.1827 | 1.0278 | 1.0138 |
| No log | 4.9412 | 84 | 1.0840 | 0.1709 | 1.0840 | 1.0411 |
| No log | 5.0588 | 86 | 1.0419 | 0.2537 | 1.0419 | 1.0207 |
| No log | 5.1765 | 88 | 1.0101 | 0.0244 | 1.0101 | 1.0050 |
| No log | 5.2941 | 90 | 0.9973 | -0.0108 | 0.9973 | 0.9987 |
| No log | 5.4118 | 92 | 1.0215 | 0.2124 | 1.0215 | 1.0107 |
| No log | 5.5294 | 94 | 1.2082 | 0.0948 | 1.2082 | 1.0992 |
| No log | 5.6471 | 96 | 1.2811 | 0.0852 | 1.2811 | 1.1319 |
| No log | 5.7647 | 98 | 1.1839 | 0.0881 | 1.1839 | 1.0881 |
| No log | 5.8824 | 100 | 1.0396 | 0.2728 | 1.0396 | 1.0196 |
| No log | 6.0 | 102 | 0.8852 | 0.2149 | 0.8852 | 0.9408 |
| No log | 6.1176 | 104 | 0.8421 | 0.1353 | 0.8421 | 0.9177 |
| No log | 6.2353 | 106 | 0.8525 | 0.2270 | 0.8525 | 0.9233 |
| No log | 6.3529 | 108 | 0.8804 | 0.3817 | 0.8804 | 0.9383 |
| No log | 6.4706 | 110 | 0.9760 | 0.3398 | 0.9760 | 0.9879 |
| No log | 6.5882 | 112 | 1.0358 | 0.2708 | 1.0358 | 1.0177 |
| No log | 6.7059 | 114 | 0.9867 | 0.28 | 0.9867 | 0.9933 |
| No log | 6.8235 | 116 | 0.9717 | 0.1843 | 0.9717 | 0.9857 |
| No log | 6.9412 | 118 | 1.0157 | 0.3043 | 1.0157 | 1.0078 |
| No log | 7.0588 | 120 | 1.2547 | 0.1409 | 1.2547 | 1.1201 |
| No log | 7.1765 | 122 | 1.3189 | 0.1357 | 1.3189 | 1.1484 |
| No log | 7.2941 | 124 | 1.1721 | 0.2231 | 1.1721 | 1.0826 |
| No log | 7.4118 | 126 | 0.9737 | 0.2076 | 0.9737 | 0.9868 |
| No log | 7.5294 | 128 | 0.9136 | 0.1373 | 0.9136 | 0.9558 |
| No log | 7.6471 | 130 | 0.9125 | 0.1867 | 0.9125 | 0.9553 |
| No log | 7.7647 | 132 | 0.9566 | 0.3110 | 0.9566 | 0.9780 |
| No log | 7.8824 | 134 | 0.8952 | 0.3069 | 0.8952 | 0.9462 |
| No log | 8.0 | 136 | 0.8672 | 0.2547 | 0.8672 | 0.9312 |
| No log | 8.1176 | 138 | 0.8373 | 0.2661 | 0.8373 | 0.9151 |
| No log | 8.2353 | 140 | 0.8693 | 0.2670 | 0.8693 | 0.9324 |
| No log | 8.3529 | 142 | 0.9026 | 0.4085 | 0.9026 | 0.9500 |
| No log | 8.4706 | 144 | 0.8102 | 0.4464 | 0.8102 | 0.9001 |
| No log | 8.5882 | 146 | 0.8687 | 0.3760 | 0.8687 | 0.9320 |
| No log | 8.7059 | 148 | 1.0536 | 0.2613 | 1.0536 | 1.0264 |
| No log | 8.8235 | 150 | 1.2256 | 0.2299 | 1.2256 | 1.1070 |
| No log | 8.9412 | 152 | 1.0067 | 0.3431 | 1.0067 | 1.0034 |
| No log | 9.0588 | 154 | 0.8420 | 0.3011 | 0.8420 | 0.9176 |
| No log | 9.1765 | 156 | 0.9171 | 0.4321 | 0.9171 | 0.9577 |
| No log | 9.2941 | 158 | 1.2507 | 0.1841 | 1.2507 | 1.1184 |
| No log | 9.4118 | 160 | 1.5506 | 0.1759 | 1.5506 | 1.2452 |
| No log | 9.5294 | 162 | 1.4346 | 0.1965 | 1.4346 | 1.1977 |
| No log | 9.6471 | 164 | 1.2422 | 0.1776 | 1.2422 | 1.1146 |
| No log | 9.7647 | 166 | 0.9861 | 0.3128 | 0.9861 | 0.9930 |
| No log | 9.8824 | 168 | 0.8802 | 0.3255 | 0.8802 | 0.9382 |
| No log | 10.0 | 170 | 0.8676 | 0.2402 | 0.8676 | 0.9315 |
| No log | 10.1176 | 172 | 0.8841 | 0.2993 | 0.8841 | 0.9403 |
| No log | 10.2353 | 174 | 0.9120 | 0.2937 | 0.9120 | 0.9550 |
| No log | 10.3529 | 176 | 0.9544 | 0.3667 | 0.9544 | 0.9769 |
| No log | 10.4706 | 178 | 0.9906 | 0.3582 | 0.9906 | 0.9953 |
| No log | 10.5882 | 180 | 0.9758 | 0.3425 | 0.9758 | 0.9878 |
| No log | 10.7059 | 182 | 0.9097 | 0.3125 | 0.9097 | 0.9538 |
| No log | 10.8235 | 184 | 0.8349 | 0.3609 | 0.8349 | 0.9137 |
| No log | 10.9412 | 186 | 0.8148 | 0.2172 | 0.8148 | 0.9027 |
| No log | 11.0588 | 188 | 0.8589 | 0.2988 | 0.8589 | 0.9268 |
| No log | 11.1765 | 190 | 0.9714 | 0.3483 | 0.9714 | 0.9856 |
| No log | 11.2941 | 192 | 1.0116 | 0.2905 | 1.0116 | 1.0058 |
| No log | 11.4118 | 194 | 0.9017 | 0.3645 | 0.9017 | 0.9496 |
| No log | 11.5294 | 196 | 0.8513 | 0.3520 | 0.8513 | 0.9227 |
| No log | 11.6471 | 198 | 0.8394 | 0.3520 | 0.8394 | 0.9162 |
| No log | 11.7647 | 200 | 0.8733 | 0.3520 | 0.8733 | 0.9345 |
| No log | 11.8824 | 202 | 0.8863 | 0.3520 | 0.8863 | 0.9414 |
| No log | 12.0 | 204 | 0.9484 | 0.3456 | 0.9484 | 0.9738 |
| No log | 12.1176 | 206 | 0.9953 | 0.3333 | 0.9953 | 0.9976 |
| No log | 12.2353 | 208 | 1.1126 | 0.2754 | 1.1126 | 1.0548 |
| No log | 12.3529 | 210 | 1.0385 | 0.2577 | 1.0385 | 1.0191 |
| No log | 12.4706 | 212 | 0.9103 | 0.3100 | 0.9103 | 0.9541 |
| No log | 12.5882 | 214 | 0.8826 | 0.3100 | 0.8826 | 0.9395 |
| No log | 12.7059 | 216 | 0.9827 | 0.2602 | 0.9827 | 0.9913 |
| No log | 12.8235 | 218 | 1.1467 | 0.2622 | 1.1467 | 1.0709 |
| No log | 12.9412 | 220 | 1.0604 | 0.2524 | 1.0604 | 1.0298 |
| No log | 13.0588 | 222 | 0.8075 | 0.3976 | 0.8075 | 0.8986 |
| No log | 13.1765 | 224 | 0.7876 | 0.3316 | 0.7876 | 0.8875 |
| No log | 13.2941 | 226 | 0.8008 | 0.3239 | 0.8008 | 0.8949 |
| No log | 13.4118 | 228 | 0.7293 | 0.3079 | 0.7293 | 0.8540 |
| No log | 13.5294 | 230 | 0.8008 | 0.3760 | 0.8008 | 0.8949 |
| No log | 13.6471 | 232 | 0.9776 | 0.2547 | 0.9776 | 0.9887 |
| No log | 13.7647 | 234 | 0.9390 | 0.2894 | 0.9390 | 0.9690 |
| No log | 13.8824 | 236 | 0.8179 | 0.3565 | 0.8179 | 0.9044 |
| No log | 14.0 | 238 | 0.8232 | 0.2479 | 0.8232 | 0.9073 |
| No log | 14.1176 | 240 | 0.8592 | 0.2747 | 0.8592 | 0.9269 |
| No log | 14.2353 | 242 | 0.9239 | 0.2873 | 0.9239 | 0.9612 |
| No log | 14.3529 | 244 | 1.0360 | 0.2636 | 1.0360 | 1.0178 |
| No log | 14.4706 | 246 | 1.1456 | 0.2312 | 1.1456 | 1.0703 |
| No log | 14.5882 | 248 | 1.0731 | 0.2312 | 1.0731 | 1.0359 |
| No log | 14.7059 | 250 | 0.9686 | 0.2677 | 0.9686 | 0.9842 |
| No log | 14.8235 | 252 | 0.9177 | 0.1884 | 0.9177 | 0.9580 |
| No log | 14.9412 | 254 | 0.8797 | 0.2616 | 0.8797 | 0.9379 |
| No log | 15.0588 | 256 | 0.8861 | 0.2887 | 0.8861 | 0.9413 |
| No log | 15.1765 | 258 | 0.9328 | 0.2564 | 0.9328 | 0.9658 |
| No log | 15.2941 | 260 | 0.9601 | 0.2777 | 0.9601 | 0.9798 |
| No log | 15.4118 | 262 | 1.0995 | 0.2354 | 1.0995 | 1.0486 |
| No log | 15.5294 | 264 | 0.9717 | 0.3022 | 0.9717 | 0.9857 |
| No log | 15.6471 | 266 | 0.8374 | 0.2975 | 0.8374 | 0.9151 |
| No log | 15.7647 | 268 | 0.8447 | 0.2832 | 0.8447 | 0.9191 |
| No log | 15.8824 | 270 | 0.9461 | 0.3022 | 0.9461 | 0.9727 |
| No log | 16.0 | 272 | 1.0755 | 0.2271 | 1.0755 | 1.0370 |
| No log | 16.1176 | 274 | 1.1718 | 0.2687 | 1.1718 | 1.0825 |
| No log | 16.2353 | 276 | 1.0668 | 0.2777 | 1.0668 | 1.0328 |
| No log | 16.3529 | 278 | 0.8756 | 0.2853 | 0.8756 | 0.9357 |
| No log | 16.4706 | 280 | 0.7682 | 0.4134 | 0.7682 | 0.8765 |
| No log | 16.5882 | 282 | 0.7629 | 0.3867 | 0.7629 | 0.8735 |
| No log | 16.7059 | 284 | 0.8001 | 0.3194 | 0.8001 | 0.8945 |
| No log | 16.8235 | 286 | 0.9030 | 0.2964 | 0.9030 | 0.9502 |
| No log | 16.9412 | 288 | 0.9448 | 0.2964 | 0.9448 | 0.9720 |
| No log | 17.0588 | 290 | 0.8439 | 0.3371 | 0.8439 | 0.9186 |
| No log | 17.1765 | 292 | 0.7099 | 0.3433 | 0.7099 | 0.8426 |
| No log | 17.2941 | 294 | 0.6892 | 0.3253 | 0.6892 | 0.8302 |
| No log | 17.4118 | 296 | 0.6987 | 0.3525 | 0.6987 | 0.8359 |
| No log | 17.5294 | 298 | 0.7808 | 0.4295 | 0.7808 | 0.8837 |
| No log | 17.6471 | 300 | 1.0687 | 0.3080 | 1.0687 | 1.0338 |
| No log | 17.7647 | 302 | 1.3421 | 0.2633 | 1.3421 | 1.1585 |
| No log | 17.8824 | 304 | 1.2996 | 0.3435 | 1.2996 | 1.1400 |
| No log | 18.0 | 306 | 1.0384 | 0.3131 | 1.0384 | 1.0190 |
| No log | 18.1176 | 308 | 0.8093 | 0.3255 | 0.8093 | 0.8996 |
| No log | 18.2353 | 310 | 0.7627 | 0.2508 | 0.7627 | 0.8733 |
| No log | 18.3529 | 312 | 0.7271 | 0.2838 | 0.7271 | 0.8527 |
| No log | 18.4706 | 314 | 0.7212 | 0.3677 | 0.7212 | 0.8492 |
| No log | 18.5882 | 316 | 0.7803 | 0.4038 | 0.7803 | 0.8833 |
| No log | 18.7059 | 318 | 0.8165 | 0.3803 | 0.8165 | 0.9036 |
| No log | 18.8235 | 320 | 0.7928 | 0.3803 | 0.7928 | 0.8904 |
| No log | 18.9412 | 322 | 0.7570 | 0.3868 | 0.7570 | 0.8701 |
| No log | 19.0588 | 324 | 0.7420 | 0.4104 | 0.7420 | 0.8614 |
| No log | 19.1765 | 326 | 0.7206 | 0.3567 | 0.7206 | 0.8489 |
| No log | 19.2941 | 328 | 0.7507 | 0.3732 | 0.7507 | 0.8664 |
| No log | 19.4118 | 330 | 0.8377 | 0.3699 | 0.8377 | 0.9153 |
| No log | 19.5294 | 332 | 0.9011 | 0.2830 | 0.9011 | 0.9493 |
| No log | 19.6471 | 334 | 0.8605 | 0.3217 | 0.8605 | 0.9276 |
| No log | 19.7647 | 336 | 0.8494 | 0.2702 | 0.8494 | 0.9216 |
| No log | 19.8824 | 338 | 0.8319 | 0.2958 | 0.8319 | 0.9121 |
| No log | 20.0 | 340 | 0.8117 | 0.2958 | 0.8117 | 0.9009 |
| No log | 20.1176 | 342 | 0.8372 | 0.2958 | 0.8372 | 0.9150 |
| No log | 20.2353 | 344 | 0.8763 | 0.2627 | 0.8763 | 0.9361 |
| No log | 20.3529 | 346 | 0.8889 | 0.2578 | 0.8889 | 0.9428 |
| No log | 20.4706 | 348 | 0.8182 | 0.3356 | 0.8182 | 0.9045 |
| No log | 20.5882 | 350 | 0.7596 | 0.3567 | 0.7596 | 0.8715 |
| No log | 20.7059 | 352 | 0.7057 | 0.3914 | 0.7057 | 0.8401 |
| No log | 20.8235 | 354 | 0.7064 | 0.4044 | 0.7064 | 0.8405 |
| No log | 20.9412 | 356 | 0.7592 | 0.4104 | 0.7592 | 0.8713 |
| No log | 21.0588 | 358 | 0.7634 | 0.4334 | 0.7634 | 0.8737 |
| No log | 21.1765 | 360 | 0.7164 | 0.4404 | 0.7164 | 0.8464 |
| No log | 21.2941 | 362 | 0.6770 | 0.4408 | 0.6770 | 0.8228 |
| No log | 21.4118 | 364 | 0.6566 | 0.4260 | 0.6566 | 0.8103 |
| No log | 21.5294 | 366 | 0.6542 | 0.4448 | 0.6542 | 0.8088 |
| No log | 21.6471 | 368 | 0.6998 | 0.4085 | 0.6998 | 0.8366 |
| No log | 21.7647 | 370 | 0.7260 | 0.4017 | 0.7260 | 0.8521 |
| No log | 21.8824 | 372 | 0.8142 | 0.4012 | 0.8142 | 0.9023 |
| No log | 22.0 | 374 | 0.9140 | 0.3941 | 0.9140 | 0.9560 |
| No log | 22.1176 | 376 | 0.8714 | 0.3803 | 0.8714 | 0.9335 |
| No log | 22.2353 | 378 | 0.7615 | 0.3776 | 0.7615 | 0.8726 |
| No log | 22.3529 | 380 | 0.6998 | 0.3051 | 0.6998 | 0.8365 |
| No log | 22.4706 | 382 | 0.6589 | 0.3171 | 0.6589 | 0.8117 |
| No log | 22.5882 | 384 | 0.6579 | 0.3417 | 0.6579 | 0.8111 |
| No log | 22.7059 | 386 | 0.6688 | 0.3931 | 0.6688 | 0.8178 |
| No log | 22.8235 | 388 | 0.7676 | 0.4759 | 0.7676 | 0.8761 |
| No log | 22.9412 | 390 | 0.9496 | 0.4186 | 0.9496 | 0.9745 |
| No log | 23.0588 | 392 | 0.9566 | 0.4003 | 0.9566 | 0.9781 |
| No log | 23.1765 | 394 | 0.8132 | 0.3766 | 0.8132 | 0.9018 |
| No log | 23.2941 | 396 | 0.7108 | 0.4759 | 0.7108 | 0.8431 |
| No log | 23.4118 | 398 | 0.6689 | 0.3887 | 0.6689 | 0.8179 |
| No log | 23.5294 | 400 | 0.6973 | 0.3149 | 0.6973 | 0.8350 |
| No log | 23.6471 | 402 | 0.7150 | 0.2279 | 0.7150 | 0.8456 |
| No log | 23.7647 | 404 | 0.7614 | 0.3567 | 0.7614 | 0.8726 |
| No log | 23.8824 | 406 | 0.9621 | 0.2905 | 0.9621 | 0.9809 |
| No log | 24.0 | 408 | 1.2825 | 0.2671 | 1.2825 | 1.1325 |
| No log | 24.1176 | 410 | 1.5997 | 0.2721 | 1.5997 | 1.2648 |
| No log | 24.2353 | 412 | 1.5959 | 0.2185 | 1.5959 | 1.2633 |
| No log | 24.3529 | 414 | 1.3474 | 0.2101 | 1.3474 | 1.1608 |
| No log | 24.4706 | 416 | 1.0286 | 0.2323 | 1.0286 | 1.0142 |
| No log | 24.5882 | 418 | 0.8202 | 0.2697 | 0.8202 | 0.9056 |
| No log | 24.7059 | 420 | 0.7379 | 0.2419 | 0.7379 | 0.8590 |
| No log | 24.8235 | 422 | 0.7354 | 0.2690 | 0.7354 | 0.8576 |
| No log | 24.9412 | 424 | 0.7780 | 0.4023 | 0.7780 | 0.8821 |
| No log | 25.0588 | 426 | 0.9200 | 0.2578 | 0.9200 | 0.9592 |
| No log | 25.1765 | 428 | 1.0892 | 0.3273 | 1.0892 | 1.0437 |
| No log | 25.2941 | 430 | 1.1873 | 0.3541 | 1.1873 | 1.0896 |
| No log | 25.4118 | 432 | 1.1145 | 0.3557 | 1.1145 | 1.0557 |
| No log | 25.5294 | 434 | 0.9873 | 0.3936 | 0.9873 | 0.9936 |
| No log | 25.6471 | 436 | 0.8466 | 0.3424 | 0.8466 | 0.9201 |
| No log | 25.7647 | 438 | 0.7454 | 0.4263 | 0.7454 | 0.8634 |
| No log | 25.8824 | 440 | 0.7024 | 0.4013 | 0.7024 | 0.8381 |
| No log | 26.0 | 442 | 0.6954 | 0.3504 | 0.6954 | 0.8339 |
| No log | 26.1176 | 444 | 0.7044 | 0.3504 | 0.7044 | 0.8393 |
| No log | 26.2353 | 446 | 0.7238 | 0.3078 | 0.7238 | 0.8508 |
| No log | 26.3529 | 448 | 0.7341 | 0.3078 | 0.7341 | 0.8568 |
| No log | 26.4706 | 450 | 0.7577 | 0.3820 | 0.7577 | 0.8705 |
| No log | 26.5882 | 452 | 0.8260 | 0.2905 | 0.8260 | 0.9088 |
| No log | 26.7059 | 454 | 0.9116 | 0.3074 | 0.9116 | 0.9548 |
| No log | 26.8235 | 456 | 1.0230 | 0.2921 | 1.0230 | 1.0114 |
| No log | 26.9412 | 458 | 1.2075 | 0.2601 | 1.2075 | 1.0988 |
| No log | 27.0588 | 460 | 1.3118 | 0.2928 | 1.3118 | 1.1453 |
| No log | 27.1765 | 462 | 1.3052 | 0.3142 | 1.3052 | 1.1425 |
| No log | 27.2941 | 464 | 1.2559 | 0.2601 | 1.2559 | 1.1207 |
| No log | 27.4118 | 466 | 1.1044 | 0.2278 | 1.1044 | 1.0509 |
| No log | 27.5294 | 468 | 0.9884 | 0.2958 | 0.9884 | 0.9942 |
| No log | 27.6471 | 470 | 0.9145 | 0.2516 | 0.9145 | 0.9563 |
| No log | 27.7647 | 472 | 0.8596 | 0.2805 | 0.8596 | 0.9272 |
| No log | 27.8824 | 474 | 0.8144 | 0.2523 | 0.8144 | 0.9024 |
| No log | 28.0 | 476 | 0.7989 | 0.2521 | 0.7989 | 0.8938 |
| No log | 28.1176 | 478 | 0.7984 | 0.2724 | 0.7984 | 0.8935 |
| No log | 28.2353 | 480 | 0.8129 | 0.3159 | 0.8129 | 0.9016 |
| No log | 28.3529 | 482 | 0.8975 | 0.2964 | 0.8975 | 0.9473 |
| No log | 28.4706 | 484 | 0.9907 | 0.3074 | 0.9907 | 0.9954 |
| No log | 28.5882 | 486 | 1.0228 | 0.3357 | 1.0228 | 1.0113 |
| No log | 28.7059 | 488 | 0.9472 | 0.3431 | 0.9472 | 0.9732 |
| No log | 28.8235 | 490 | 0.8254 | 0.3867 | 0.8254 | 0.9085 |
| No log | 28.9412 | 492 | 0.7523 | 0.4038 | 0.7523 | 0.8673 |
| No log | 29.0588 | 494 | 0.6974 | 0.4684 | 0.6974 | 0.8351 |
| No log | 29.1765 | 496 | 0.6882 | 0.4764 | 0.6882 | 0.8296 |
| No log | 29.2941 | 498 | 0.7176 | 0.4684 | 0.7176 | 0.8471 |
| 0.3337 | 29.4118 | 500 | 0.7451 | 0.4334 | 0.7451 | 0.8632 |
| 0.3337 | 29.5294 | 502 | 0.7823 | 0.3973 | 0.7823 | 0.8845 |
| 0.3337 | 29.6471 | 504 | 0.7687 | 0.4334 | 0.7687 | 0.8768 |
| 0.3337 | 29.7647 | 506 | 0.7375 | 0.4979 | 0.7375 | 0.8588 |
| 0.3337 | 29.8824 | 508 | 0.7668 | 0.4173 | 0.7668 | 0.8756 |
| 0.3337 | 30.0 | 510 | 0.8392 | 0.3867 | 0.8392 | 0.9161 |
| 0.3337 | 30.1176 | 512 | 0.9017 | 0.3867 | 0.9017 | 0.9496 |
| 0.3337 | 30.2353 | 514 | 0.9147 | 0.3867 | 0.9147 | 0.9564 |
| 0.3337 | 30.3529 | 516 | 0.8725 | 0.3867 | 0.8725 | 0.9341 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
sd-concepts-library/pokemon-gens-1-to-8 | sd-concepts-library | "2022-12-11T13:09:11" | 0 | 4 | null | [
"license:mit",
"region:us"
] | null | "2022-12-11T12:39:24" | ---
license: mit
---
### Pokemon gens 1 to 8 on Stable Diffusion
Pokémon Kanto/Johto/Hoenn/Sinnoh/Unova/Kalos/Alola/Galar battle image concept (96x96 upscaled to 512x512)
Includes mega-evolutions, gigamax, regional and alternate forms. Duplicate mega-evolutions/gigamax forms were discarded (Latios/Latias, Toxtricity)
Female/Unown variants are excluded, as well as Arceus/Silvally/other recolours (to avoid same-species overrepresentation)
This is the `<pkmn-galar>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
neulab/omnitab-large-16shot | neulab | "2022-11-29T02:07:05" | 48 | 2 | transformers | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"tapex",
"table-question-answering",
"en",
"dataset:wikitablequestions",
"arxiv:2207.03637",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | table-question-answering | "2022-11-29T02:05:27" | ---
language: en
tags:
- tapex
- table-question-answering
datasets:
- wikitablequestions
---
# OmniTab
OmniTab is a table-based QA model proposed in [OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering](https://arxiv.org/pdf/2207.03637.pdf). The original Github repository is [https://github.com/jzbjyb/OmniTab](https://github.com/jzbjyb/OmniTab).
## Description
`neulab/omnitab-large-16shot` (based on BART architecture) is initialized with `microsoft/tapex-large` and continuously pretrained on natural and synthetic data (SQL2NL model trained in the 16-shot setting).
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd
tokenizer = AutoTokenizer.from_pretrained("neulab/omnitab-large-16shot")
model = AutoModelForSeq2SeqLM.from_pretrained("neulab/omnitab-large-16shot")
data = {
"year": [1896, 1900, 1904, 2004, 2008, 2012],
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
}
table = pd.DataFrame.from_dict(data)
query = "In which year did beijing host the Olympic Games?"
encoding = tokenizer(table=table, query=query, return_tensors="pt")
outputs = model.generate(**encoding)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# [' 2008']
```
## Reference
```bibtex
@inproceedings{jiang-etal-2022-omnitab,
title = "{O}mni{T}ab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering",
author = "Jiang, Zhengbao and Mao, Yi and He, Pengcheng and Neubig, Graham and Chen, Weizhu",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
}
```
|
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Dataset Card for Hugging Face Hub Model Cards
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