modelId
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-26 12:28:17
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
stringclasses 533
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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|---|---|---|---|---|---|---|---|---|---|
moojink/openvla-7b-oft-finetuned-libero-object
|
moojink
| 2025-06-17T22:31:22Z | 403 | 1 |
transformers
|
[
"transformers",
"safetensors",
"openvla",
"feature-extraction",
"robotics",
"custom_code",
"arxiv:2502.19645",
"license:mit",
"region:us"
] |
robotics
| 2025-02-25T22:02:28Z |
---
pipeline_tag: robotics
library_name: transformers
license: mit
---
# Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
This repository contains the OpenVLA-OFT checkpoint for LIBERO-Object, as described in [Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success](https://arxiv.org/abs/2502.19645). OpenVLA-OFT significantly improves upon the base OpenVLA model by incorporating optimized fine-tuning techniques.
Project Page: https://openvla-oft.github.io/
Code: https://github.com/openvla-oft/openvla-oft
See here for other OpenVLA-OFT checkpoints: https://huggingface.co/moojink?search_models=oft
## Quick Start
This example demonstrates generating an action chunk using a pretrained OpenVLA-OFT checkpoint. Ensure you have set up the conda environment as described in the GitHub README.
```python
import pickle
from experiments.robot.libero.run_libero_eval import GenerateConfig
from experiments.robot.openvla_utils import get_action_head, get_processor, get_proprio_projector, get_vla, get_vla_action
from prismatic.vla.constants import NUM_ACTIONS_CHUNK, PROPRIO_DIM
# Instantiate config (see class GenerateConfig in experiments/robot/libero/run_libero_eval.py for definitions)
cfg = GenerateConfig(
pretrained_checkpoint = "moojink/openvla-7b-oft-finetuned-libero-spatial",
use_l1_regression = True,
use_diffusion = False,
use_film = False,
num_images_in_input = 2,
use_proprio = True,
load_in_8bit = False,
load_in_4bit = False,
center_crop = True,
num_open_loop_steps = NUM_ACTIONS_CHUNK,
unnorm_key = "libero_spatial_no_noops",
)
# Load OpenVLA-OFT policy and inputs processor
vla = get_vla(cfg)
processor = get_processor(cfg)
# Load MLP action head to generate continuous actions (via L1 regression)
action_head = get_action_head(cfg, llm_dim=vla.llm_dim)
# Load proprio projector to map proprio to language embedding space
proprio_projector = get_proprio_projector(cfg, llm_dim=vla.llm_dim, proprio_dim=PROPRIO_DIM)
# Load sample observation:
# observation (dict): {
# "full_image": primary third-person image,
# "wrist_image": wrist-mounted camera image,
# "state": robot proprioceptive state,
# "task_description": task description,
# }
with open("experiments/robot/libero/sample_libero_spatial_observation.pkl", "rb") as file:
observation = pickle.load(file)
# Generate robot action chunk (sequence of future actions)
actions = get_vla_action(cfg, vla, processor, observation, observation["task_description"], action_head, proprio_projector)
print("Generated action chunk:")
for act in actions:
print(act)
```
## Citation
```bibtex
@article{kim2025fine,
title={Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success},
author={Kim, Moo Jin and Finn, Chelsea and Liang, Percy},
journal={arXiv preprint arXiv:2502.19645},
year={2025}
}
```
|
albertuspekerti/whispertiny_fruit25syl_v4_2
|
albertuspekerti
| 2025-06-17T22:28:31Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T01:50:57Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whispertiny_fruit25syl_v4_2
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. -->
# whispertiny_fruit25syl_v4_2
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0507
- Wer: 5.4007
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 70000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.0537 | 0.0286 | 2000 | 0.6713 | 45.9233 |
| 0.0303 | 0.0571 | 4000 | 1.2419 | 40.7944 |
| 0.0743 | 0.0857 | 6000 | 0.1033 | 9.8467 |
| 0.0156 | 0.1143 | 8000 | 0.8532 | 42.2997 |
| 0.0063 | 0.1429 | 10000 | 1.5205 | 44.2787 |
| 0.0079 | 0.1714 | 12000 | 0.1230 | 14.3206 |
| 0.0389 | 0.2 | 14000 | 0.0946 | 10.8432 |
| 0.0304 | 0.2286 | 16000 | 1.5826 | 39.5261 |
| 0.0093 | 1.0191 | 18000 | 0.9422 | 40.2787 |
| 0.0159 | 1.0477 | 20000 | 0.0601 | 5.5889 |
| 0.002 | 1.0763 | 22000 | 1.0938 | 35.1150 |
| 0.0016 | 1.1048 | 24000 | 1.0797 | 39.4425 |
| 0.0088 | 1.1334 | 26000 | 0.1089 | 11.2822 |
| 0.0259 | 1.1620 | 28000 | 0.0396 | 5.0035 |
| 0.0139 | 1.1906 | 30000 | 1.0625 | 35.3798 |
| 0.0041 | 1.2191 | 32000 | 0.7256 | 37.1916 |
| 0.0026 | 2.0097 | 34000 | 0.0261 | 3.1359 |
| 0.0013 | 2.0383 | 36000 | 0.4904 | 28.7456 |
| 0.0032 | 2.0668 | 38000 | 0.6617 | 31.6725 |
| 0.0014 | 2.0954 | 40000 | 0.3961 | 25.3240 |
| 0.0108 | 2.1240 | 42000 | 0.0211 | 2.5575 |
| 0.002 | 2.1525 | 44000 | 0.8274 | 35.2125 |
| 0.0011 | 2.1811 | 46000 | 0.6262 | 31.9233 |
| 0.0018 | 2.2097 | 48000 | 0.0153 | 1.9233 |
| 0.0031 | 3.0002 | 50000 | 0.5681 | 26.2160 |
| 0.0012 | 3.0288 | 52000 | 0.3874 | 21.8328 |
| 0.0004 | 3.0574 | 54000 | 0.2279 | 16.1742 |
| 0.0101 | 3.0860 | 56000 | 0.0064 | 0.9408 |
| 0.0003 | 3.1145 | 58000 | 0.3883 | 22.4739 |
| 0.0003 | 3.1431 | 60000 | 0.2880 | 19.1916 |
| 0.0006 | 3.1717 | 62000 | 0.0077 | 1.1498 |
| 0.0032 | 3.2002 | 64000 | 0.0180 | 2.3136 |
| 0.0021 | 3.2288 | 66000 | 0.3580 | 22.3136 |
| 0.0083 | 4.0194 | 68000 | 0.0917 | 7.1847 |
| 0.0547 | 4.0479 | 70000 | 0.0507 | 5.4007 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
FilipT/Cambridge_inlp_projection_gender_ltg_baseline
|
FilipT
| 2025-06-17T22:23:04Z | 0 | 0 | null |
[
"safetensors",
"ltgbert",
"custom_code",
"region:us"
] | null | 2025-06-17T14:12:51Z |
# INLP-debiased `babylm/ltgbert-100m-2024` (race)
This checkpoint equals `babylm/ltgbert-100m-2024` except an INLP race projection is baked into the MLM headโs dense layer.
|
dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full-3-alfworld-stage1
|
dslighfdsl
| 2025-06-17T22:19:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:alfworld",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T20:27:40Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
datasets: alfworld
library_name: transformers
model_name: Llama-3.1-8B-Instruct-SFT-CoT-short-full-3-alfworld-stage1
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Llama-3.1-8B-Instruct-SFT-CoT-short-full-3-alfworld-stage1
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the [alfworld](https://huggingface.co/datasets/alfworld) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="dslighfdsl/Llama-3.1-8B-Instruct-SFT-CoT-short-full-3-alfworld-stage1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/pengliangji2023-carnegie-mellon-university/huggingface/runs/77onndui)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0.dev0
- Pytorch: 2.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
proota/DistilBERT-finetuned-on-emotion
|
proota
| 2025-06-17T22:17:43Z | 47 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-05-21T21:21:34Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: DistilBERT-finetuned-on-emotion
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. -->
# DistilBERT-finetuned-on-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.52.2
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.1
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb2-seed18-2025-06-18
|
morturr
| 2025-06-17T22:10:12Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T22:10:03Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb2-seed18-2025-06-18
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-2-7b-hf-LOO_one_liners-COMB_amazon-comb2-seed18-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 18
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
songhieng/TinyBERT-URL-Detection-1.0
|
songhieng
| 2025-06-17T22:09:30Z | 0 | 0 | null |
[
"safetensors",
"bert",
"url-phishing-detection",
"tinybert",
"sequence-classification",
"en",
"dataset:custom",
"license:mit",
"region:us"
] | null | 2025-06-17T22:09:27Z |
---
language: en
license: mit
tags:
- url-phishing-detection
- tinybert
- sequence-classification
datasets:
- custom
metrics:
- accuracy
- f1
---
# TinyBERT for URL Phishing Detection
This model is fine-tuned from huawei-noah/TinyBERT_General_4L_312D to detect phishing URLs.
## Model description
The model is a fine-tuned version of TinyBERT, specifically trained to classify URLs as either legitimate or phishing.
## Intended uses & limitations
This model is intended to be used for detecting phishing URLs. It takes a URL as input and outputs a prediction of whether the URL is legitimate or phishing.
## Training data
The model was trained on a combination of:
- Legitimate URLs from the Majestic Million dataset
- Phishing URLs from phishing-links-ACTIVE.txt and phishing-links-INACTIVE.txt
## Training procedure
The model was fine-tuned using the Hugging Face Transformers library with the following parameters:
- Learning rate: 5e-5
- Batch size: 16
- Number of epochs: 3
- Weight decay: 0.01
## Evaluation results
The model was evaluated on a test set consisting of both legitimate and phishing URLs.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("songhieng/TinyBERT-URL-Detection-1.0")
model = AutoModelForSequenceClassification.from_pretrained("songhieng/TinyBERT-URL-Detection-1.0")
# Prepare URL for classification
url = "https://example.com"
inputs = tokenizer(url, return_tensors="pt", truncation=True, padding=True, max_length=128)
# Make prediction
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.softmax(outputs.logits, dim=1)
label = torch.argmax(predictions, dim=1).item()
# Output result
result = "phishing" if label == 1 else "legitimate"
confidence = predictions[0][label].item()
print(f"URL: {url}")
print(f"Prediction: {result}")
print(f"Confidence: {confidence:.4f}")
```
|
meanjai/ppo-LunarLander-v2
|
meanjai
| 2025-06-17T22:05:07Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-17T22:04:40Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 228.94 +/- 24.95
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
CriteriaPO/qwen2.5-3b-orpo-mini-fp-no-tools
|
CriteriaPO
| 2025-06-17T21:59:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-3B",
"base_model:finetune:Qwen/Qwen2.5-3B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T01:06:37Z |
---
base_model: Qwen/Qwen2.5-3B
library_name: transformers
model_name: qwen2.5-3b-orpo-mini-fp-no-tools
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for qwen2.5-3b-orpo-mini-fp-no-tools
This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="CriteriaPO/qwen2.5-3b-orpo-mini-fp-no-tools", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bborges/CriteriaPreferences/runs/1o17w6l4)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.1.2+cu121
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BootesVoid/cmbzmjm9l06c1rdqs67lidold_cmc10sjta09rmrdqsqb2lsrnt
|
BootesVoid
| 2025-06-17T21:54:00Z | 0 | 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
| 2025-06-17T21:53:58Z |
---
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: ALISON
---
# Cmbzmjm9L06C1Rdqs67Lidold_Cmc10Sjta09Rmrdqsqb2Lsrnt
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ALISON` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ALISON",
"lora_weights": "https://huggingface.co/BootesVoid/cmbzmjm9l06c1rdqs67lidold_cmc10sjta09rmrdqsqb2lsrnt/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ 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('BootesVoid/cmbzmjm9l06c1rdqs67lidold_cmc10sjta09rmrdqsqb2lsrnt', weight_name='lora.safetensors')
image = pipeline('ALISON').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)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbzmjm9l06c1rdqs67lidold_cmc10sjta09rmrdqsqb2lsrnt/discussions) to add images that show off what youโve made with this LoRA.
|
yalhessi/lemexp-task1-v2-template_full-deepseek-coder-1.3b-base-ddp-8lr-v2
|
yalhessi
| 2025-06-17T21:46:43Z | 150 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:deepseek-ai/deepseek-coder-1.3b-base",
"base_model:adapter:deepseek-ai/deepseek-coder-1.3b-base",
"license:other",
"region:us"
] | null | 2025-06-02T03:54:38Z |
---
library_name: peft
license: other
base_model: deepseek-ai/deepseek-coder-1.3b-base
tags:
- generated_from_trainer
model-index:
- name: lemexp-task1-v2-template_full-deepseek-coder-1.3b-base-ddp-8lr-v2
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. -->
# lemexp-task1-v2-template_full-deepseek-coder-1.3b-base-ddp-8lr-v2
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1452
## 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: 0.0008
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 0.2792 | 0.2 | 3094 | 0.2798 |
| 0.2601 | 0.4 | 6188 | 0.2572 |
| 0.2526 | 0.6 | 9282 | 0.2487 |
| 0.2458 | 0.8 | 12376 | 0.2473 |
| 0.2427 | 1.0 | 15470 | 0.2419 |
| 0.2367 | 1.2 | 18564 | 0.2375 |
| 0.2364 | 1.4 | 21658 | 0.2323 |
| 0.2303 | 1.6 | 24752 | 0.2316 |
| 0.2318 | 1.8 | 27846 | 0.2332 |
| 0.2274 | 2.0 | 30940 | 0.2301 |
| 0.2225 | 2.2 | 34034 | 0.2269 |
| 0.222 | 2.4 | 37128 | 0.2193 |
| 0.2189 | 2.6 | 40222 | 0.2204 |
| 0.2162 | 2.8 | 43316 | 0.2168 |
| 0.2159 | 3.0 | 46410 | 0.2169 |
| 0.2117 | 3.2 | 49504 | 0.2171 |
| 0.212 | 3.4 | 52598 | 0.2086 |
| 0.2072 | 3.6 | 55692 | 0.2079 |
| 0.2062 | 3.8 | 58786 | 0.2091 |
| 0.2065 | 4.0 | 61880 | 0.2000 |
| 0.1999 | 4.2 | 64974 | 0.1994 |
| 0.1988 | 4.4 | 68068 | 0.1952 |
| 0.1967 | 4.6 | 71162 | 0.1948 |
| 0.1923 | 4.8 | 74256 | 0.1957 |
| 0.1916 | 5.0 | 77350 | 0.1928 |
| 0.1878 | 5.2 | 80444 | 0.1910 |
| 0.1879 | 5.4 | 83538 | 0.1928 |
| 0.1856 | 5.6 | 86632 | 0.1923 |
| 0.1849 | 5.8 | 89726 | 0.1877 |
| 0.1827 | 6.0 | 92820 | 0.1866 |
| 0.177 | 6.2 | 95914 | 0.1824 |
| 0.1767 | 6.4 | 99008 | 0.1838 |
| 0.1767 | 6.6 | 102102 | 0.1832 |
| 0.1766 | 6.8 | 105196 | 0.1792 |
| 0.1737 | 7.0 | 108290 | 0.1772 |
| 0.1667 | 7.2 | 111384 | 0.1758 |
| 0.1649 | 7.4 | 114478 | 0.1715 |
| 0.1667 | 7.6 | 117572 | 0.1755 |
| 0.1641 | 7.8 | 120666 | 0.1719 |
| 0.1641 | 8.0 | 123760 | 0.1697 |
| 0.1555 | 8.2 | 126854 | 0.1687 |
| 0.1539 | 8.4 | 129948 | 0.1656 |
| 0.153 | 8.6 | 133042 | 0.1635 |
| 0.1556 | 8.8 | 136136 | 0.1616 |
| 0.1543 | 9.0 | 139230 | 0.1615 |
| 0.1457 | 9.2 | 142324 | 0.1594 |
| 0.1458 | 9.4 | 145418 | 0.1585 |
| 0.1448 | 9.6 | 148512 | 0.1573 |
| 0.144 | 9.8 | 151606 | 0.1558 |
| 0.1405 | 10.0 | 154700 | 0.1520 |
| 0.135 | 10.2 | 157794 | 0.1520 |
| 0.1346 | 10.4 | 160888 | 0.1505 |
| 0.1341 | 10.6 | 163982 | 0.1506 |
| 0.1319 | 10.8 | 167076 | 0.1497 |
| 0.1313 | 11.0 | 170170 | 0.1472 |
| 0.1256 | 11.2 | 173264 | 0.1487 |
| 0.1218 | 11.4 | 176358 | 0.1462 |
| 0.1224 | 11.6 | 179452 | 0.1456 |
| 0.1212 | 11.8 | 182546 | 0.1453 |
| 0.1221 | 12.0 | 185640 | 0.1452 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
raul-delarosa99/bert-base-multilingual-cased-ner-es-onnx-static-int8
|
raul-delarosa99
| 2025-06-17T21:42:40Z | 0 | 0 |
optimum
|
[
"optimum",
"onnx",
"bert",
"quantization",
"static",
"int8",
"legal",
"spanish",
"ner",
"token-classification",
"es",
"base_model:Davlan/bert-base-multilingual-cased-ner-hrl",
"base_model:quantized:Davlan/bert-base-multilingual-cased-ner-hrl",
"license:afl-3.0",
"region:us"
] |
token-classification
| 2025-06-17T21:12:05Z |
---
base_model: Davlan/bert-base-multilingual-cased-ner-hrl
language:
- es
pipeline_tag: token-classification
library_name: optimum
license: afl-3.0
tags:
- onnx
- quantization
- static
- int8
- legal
- spanish
- ner
---
# BERT Multilingual Cased NER โ Optimized and Quantized for Spanish Legal Texts
## Model Description
This model is an optimized and quantized version of [Davlan/bert-base-multilingual-cased-ner-hrl](https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl), tailored for Named Entity Recognition (NER) tasks in Spanish legal documents. The original model was exported to ONNX format and underwent static quantization to int8 precision using the [๐ค Optimum](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization) library. Calibration was performed with a dataset comprising Spanish legal texts to enhance performance in this specific domain.
## Usage
To utilize this model, ensure that the `optimum` library is installed. Here's an example of how to load and use the model for NER tasks:
```python
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("raul-delarosa99/bert-base-multilingual-cased-ner-es-onnx-static-int8")
model = ORTModelForTokenClassification.from_pretrained("raul-delarosa99/bert-base-multilingual-cased-ner-es-onnx-static-int8")
nlp_ner = pipeline(
"ner",
model=model,
tokenizer=tokenizer,
aggregation_strategy="simple"
)
nlp_ner("Hola, soy Pedro y vivo en Toluca.")
````
**Note**: This model requires the `optimum` library for proper functionality. Loading it with `AutoModelForTokenClassification` from the standard `transformers` library may result in errors due to missing files specific to PyTorch.
## Limitations
* **Domain Specificity**: The quantization calibration was performed using Spanish legal texts, which may affect performance in other domains or languages.
* **Quantization Effects**: While quantization reduces model size and increases inference speed, it may introduce slight degradations in accuracy.
## Citation
If you use this model, please do not forget to cite the original base model:
```
@misc{davlan2021bertner,
title={BERT base multilingual cased NER},
author={Davlan, B.},
year={2021},
howpublished={\url{https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl}}
}
```
|
mehmet-3emin/beit-face-emotion
|
mehmet-3emin
| 2025-06-17T21:40:55Z | 0 | 0 | null |
[
"pytorch",
"tensorboard",
"beit",
"image-classification",
"emotion-detection",
"computer-vision",
"facial-expression",
"academic-project",
"license:mit",
"region:us"
] |
image-classification
| 2025-06-17T21:35:40Z |
---
license: mit
tags:
- image-classification
- emotion-detection
- beit
- computer-vision
- facial-expression
- academic-project
---
# BEiT Face Emotion Classifier โ mehmet-3emin
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k), optimized for **facial emotion classification** in static face images.
It was prepared and refined as part of a senior graduation project titled **"Videodan Duygusallฤฑk Analizi" (Emotion Analysis from Video)** at Mersin University (2025).
The goal of the model is to analyze facial expressions and classify them into seven basic emotions.
## ๐ง Model Architecture
- Base: BEiT (Bidirectional Encoder representation from Image Transformers)
- Patch size: 16x16
- Input size: 224x224 RGB
- Fine-tuned on: FER2013 dataset
- Output: One of 7 emotion classes
## ๐ท๏ธ Classes
The model predicts one of the following emotions:
| Label ID | Emotion |
|----------|-----------|
| 0 | Angry |
| 1 | Disgust |
| 2 | Fear |
| 3 | Happy |
| 4 | Neutral |
| 5 | Sad |
| 6 | Surprise |
## ๐งช Example Usage
```python
from transformers import BeitForImageClassification, AutoImageProcessor
from PIL import Image
import torch
model = BeitForImageClassification.from_pretrained("mehmet-3emin/beit-face-emotion")
processor = AutoImageProcessor.from_pretrained("mehmet-3emin/beit-face-emotion")
image = Image.open("your_face_image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
predicted_label = logits.argmax(-1).item()
print(predicted_label)
|
RichardErkhov/AashishKumar_-_Cn_3_0_Hinglish_llama3_7b_4kAk-4bits
|
RichardErkhov
| 2025-06-17T21:29:14Z | 0 | 0 | null |
[
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:27:30Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Cn_3_0_Hinglish_llama3_7b_4kAk - bnb 4bits
- Model creator: https://huggingface.co/AashishKumar/
- Original model: https://huggingface.co/AashishKumar/Cn_3_0_Hinglish_llama3_7b_4kAk/
Original model description:
---
base_model: cognitivecomputations/dolphin-2.9-llama3-8b
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
inference:
parameters:
temperature: 0.7
---
# Uploaded model
- **Developed by:** AashishKumar
- **License:** apache-2.0
- **Finetuned from model :** cognitivecomputations/dolphin-2.9-llama3-8b
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
|
analist/llama3.1-8B-omnimed
|
analist
| 2025-06-17T21:26:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-11T21:46:47Z |
---
base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** analist
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit
|
RichardErkhov/DBCMLAB_-_Llama-3-instruction-constructionsafety-layertuning-8bits
|
RichardErkhov
| 2025-06-17T21:20:18Z | 0 | 0 | null |
[
"safetensors",
"llama",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:17:46Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-instruction-constructionsafety-layertuning - bnb 8bits
- Model creator: https://huggingface.co/DBCMLAB/
- Original model: https://huggingface.co/DBCMLAB/Llama-3-instruction-constructionsafety-layertuning/
Original model description:
---
library_name: transformers
tags:
- llama3
- meta
- facebook
language:
- ko
license: cc-by-nc-4.0
---
# Model Card for Model ID
The **Llama-3-instruction-constructionsafety-layertuning** model is a fine-tuned model based on **beomi/Llama-3-KoEn-8B-Instruct-preview**
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
**Llama-3-instruction-constructionsafety-layertuning**
Llama-3-instruction-constructionsafety-layertuning model is fine-tuned model based on beomi/Llama-3-KoEn-8B-Instruction-preview.
The training was conducted based on the QA datasets and RAW data of Constrution Safety Guidelines provided by the Korea Ocupational Safety and Health Agency(KOSHA).
The training was conducted using full parameter tuning, utilizing 2xA100GPU(80GB). Approximately 11,000 data were used for the training process.
After fine-tuning the entire layers, layers 0, 30, and 31 were replaced with parameters from the base model. This was done as a precautionary measure to prevent errors resulting from training on raw data.
## Simple Use
```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
model_name = "DBCMLAB/Llama-3-instruction-constructionsafety-layertuning"
token = "your_access_token"
tuned_model = AutoModelForCausalLM.from_pretrained(
model_name,
token=access_token,
torch_dtype="auto",
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(model_name, token=access_token)
tokenizer.pad_token = tokenizer.eos_token
pipe = pipeline("text-generation", model=tuned_model, tokenizer = tokenizer, torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{
"role": "system",
"content": "์น์ ํ ๊ฑด์ค์์ ์ ๋ฌธ๊ฐ๋ก์ ์๋๋ฐฉ์ ์์ฒญ์ ์ต๋ํ '์์ธํ๊ณ ' ์น์ ํ๊ฒ ๋ตํ์. ๋ชจ๋ ๋๋ต์ ํ๊ตญ์ด(Korean)์ผ๋ก ๋๋ตํด์ค.",
},
{"role": "user", "content": "ํ๋ง์ด ๊ฐ์์ค ๊ณต์ฌ์ ์์
์ ์์ ์กฐ์น์ ๋ํด์ 10๊ฐ์ง ์ ๋ ์๋ ค์ฃผ์ธ์."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
### Output
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
์น์ ํ ๊ฑด์ค์์ ์ ๋ฌธ๊ฐ๋ก์ ์๋๋ฐฉ์ ์์ฒญ์ ์ต๋ํ '์์ธํ๊ณ ' ์น์ ํ๊ฒ ๋ตํ์. ๋ชจ๋ ๋๋ต์ ํ๊ตญ์ด(Korean)์ผ๋ก ๋๋ตํด์ค.<|eot_id|><|start_header_id|>user<|end_header_id|>
ํ๋ง์ด ๊ฐ์์ค ๊ณต์ฌ์ ์์
์ ์์ ์กฐ์น์ ๋ํด์ 10๊ฐ์ง ์ ๋ ์๋ ค์ฃผ์ธ์.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
1. ํ๋ง์ด ๊ณต์ฌ ์ฐฉ๊ณต ์ ์ ์งํ๋งค์ค๋ฌผ์ ์ ๋ฌด๋ฅผ ํ์ธํ๊ณ ์งํ๋งค์ค๋ฌผ์ ๋ณดํธํ๊ธฐ ์ํ ์กฐ์น๋ฅผ ์ทจํ์ฌ์ผ ํ๋ค. 2. ํ๋ง์ด ๋ฒฝ์ ๋ฐฐ๋ฉด ์ง๋ฐ ๋์ด๊ฐ ํ๋ง์ด ๋ฒฝ ๋์ด๋ณด๋ค ๋ฎ์ ๊ฒฝ์ฐ, ์ง๋ฐ์ ๋ถ๊ดด๋ฅผ ๋ฐฉ์งํ๊ธฐ ์ํ ํ์ํ ์กฐ์น๋ฅผ ์ทจํ์ฌ์ผ ํ๋ค. 3. ํ๋ง์ด ๋ฒฝ์ ์ฃผ๋ณ ์ง๋ฐ ๋ฐ ๊ตฌ์กฐ๋ฌผ์ ๊ท ์ด์ ๋ฐฉ์งํ๊ณ , ์ธ์ ๊ตฌ์กฐ๋ฌผ์ ์ํฅ์ ๋ฏธ์น์ง ์๋๋ก ์ค์นํ์ฌ์ผ ํ๋ค. 4. ํ๋ง์ด ๊ณต์ฌ ์ค ์ธ์ ๊ตฌ์กฐ๋ฌผ, ์ 3์์ ๊ถ๋ฆฌ ๋๋ ์ด์ต์ ์นจํดํ์ง ์๋๋ก ์กฐ์น๋ฅผ ์ทจํ์ฌ์ผ ํ๋ค. 5. ํ๋ง์ด ๊ณต์ฌ ์ค ์งํ์์์ ์ ํ๋ก ์ธํ์ฌ ์ธ์ ํ ๋๋ก๋ ๊ฑด์ถ๋ฌผ ๋ฑ์ ์ํฅ์ ๋ฏธ์น ์ฐ๋ ค๊ฐ ์๋ ๊ฒฝ์ฐ, ๊ทธ ์ฐ๋ ค๊ฐ ์๋๋ก ์กฐ์น๋ฅผ ์ทจํ์ฌ์ผ ํ๋ค. 6. ํ๋ง์ด ๊ณต์ฌ ์ ๋น์๊ฒฝ๋ณด์์ค์ ์ค์นํ์ฌ ์๊ธ์ํฉ์ ๋๋นํ๊ณ , ์์ ๊ต์ก์ ์ค์ํ์ฌ์ผ ํ๋ค. 7. ํ๋ง์ด ๊ณต์ฌ ์ค ๊ด๊ณ๊ธฐ๊ด์ ์๊ตฌ๊ฐ ์๋ ๊ฒฝ์ฐ, ๊ทธ ์๊ตฌ์ ๋ฐ๋ผ ์กฐ์น๋ฅผ ์ทจํ์ฌ์ผ ํ๋ค. 8. ํ๋ง์ด ๊ณต์ฌ ์ค ํ๋ง์ด ๋ฒฝ์ ๊ธฐ์ธ๊ธฐ๋ฅผ 1/50 ์ด์ 1/30 ์ดํ๋ก ์ ์งํ๊ณ , ์ํ์ผ๋ก ์ค์นํ๋ ํ๋ง์ด์ ๊ฒฝ์ฐ์๋ ์ง๋ฐ์ด ์ํ์ผ๋ก ์ ์ง๋๋๋ก ํ์ฌ์ผ ํ๋ค. 9. ํ๋ง์ด ๊ณต์ฌ ์ค ํ๋ง์ด ๋ฒฝ์ ์์ฉํ๋ ํ ์์ด ์ค๊ณ๊ธฐ์ค์ ์ด๊ณผํ์ง ์๋๋ก ํ์ฌ์ผ ํ๋ค. 10. ํ๋ง์ด ๊ณต์ฌ ์ค ํ๋ง์ด ๋ฒฝ์ ๋ฌด๋์ง์ ๋ฐฉ์งํ๊ธฐ ์ํ์ฌ ์ง๋ฐ์ด ์ํ์ผ๋ก ์ ์ง๋๋๋ก ํ์ฌ์ผ ํ๋ค.
```
### Training Data
Training Data will be provided upon requests.
<!-- 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. -->
## Citation instructions
**Llama-3-instruction-constructionsafety-layertuning**
```
@article{llama3cs-layertuning,
title={Llama-3-instruction-constructionsafety-layertuning},
author={L, Jungwon, A, Seungjun},
year={2024},
url={https://huggingface.co/DBCM/Llama-3-instruction-constructionsafety-layertuning}
}
```
**Llama-3-Open-Ko**
```
@article{llama3koen,
title={Llama-3-KoEn},
author={L, Junbum},
year={2024},
url={https://huggingface.co/beomi/Llama-3-KoEn-8B}
}
```
**Original Llama-3**
```
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
```
|
RichardErkhov/passionMan_-_Llama-3-bllossom-8B-PM1-finetuned-v1-15-2-8bits
|
RichardErkhov
| 2025-06-17T21:18:52Z | 0 | 0 | null |
[
"safetensors",
"llama",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:15:39Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-bllossom-8B-PM1-finetuned-v1-15-2 - bnb 8bits
- Model creator: https://huggingface.co/passionMan/
- Original model: https://huggingface.co/passionMan/Llama-3-bllossom-8B-PM1-finetuned-v1-15-2/
Original model description:
---
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** passionMan
- **License:** apache-2.0
- **Finetuned from model :** MLP-KTLim/llama-3-Korean-Bllossom-8B
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)
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed18-2025-06-17
|
morturr
| 2025-06-17T21:18:07Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T21:17:58Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed18-2025-06-17
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-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed18-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 18
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
RichardErkhov/grimjim_-_Llama-3-Oasis-v1-OAS-8B-4bits
|
RichardErkhov
| 2025-06-17T21:16:01Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2212.04089",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:14:19Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-Oasis-v1-OAS-8B - bnb 4bits
- Model creator: https://huggingface.co/grimjim/
- Original model: https://huggingface.co/grimjim/Llama-3-Oasis-v1-OAS-8B/
Original model description:
---
base_model:
- mlabonne/NeuralDaredevil-8B-abliterated
- NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
- Hastagaras/Halu-OAS-8B-Llama3
library_name: transformers
tags:
- mergekit
- merge
license: cc-by-nc-4.0
pipeline_tag: text-generation
---
# Llama-3-Oasis-v1-OAS-8B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
Each merge component was already subjected to Orthogonal Activation Steering (OAS) to mitigate refusals. The resulting text completion model should be versatile for both positive and negative roleplay scenarios and storytelling. Care should be taken when using this model.
- mlabonne/NeuralDaredevil-8B-abliterated : high MMLU for reasoning
- NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS : focus on roleplay
- Hastagaras/Halu-OAS-8B-Llama3 : focus on storytelling
Tested with the following sampler settings:
- temperature 1-1.45
- minP 0.01-0.02
Quantified model files:
- [static GGUF quants c/o mradermacher](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-GGUF)
- [weighted/imatrix GGUF quants c/o mradermacher](https://huggingface.co/mradermacher/Llama-3-Oasis-v1-OAS-8B-i1-GGUF)
- [8bpw exl2 quant](https://huggingface.co/grimjim/Llama-3-Oasis-v1-OAS-8B-8bpw_h8_exl2)
Built with Meta Llama 3.
## Merge Details
### Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using [mlabonne/NeuralDaredevil-8B-abliterated](https://huggingface.co/mlabonne/NeuralDaredevil-8B-abliterated) as a base.
### Models Merged
The following models were also included in the merge:
* [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS)
* [Hastagaras/Halu-OAS-8B-Llama3](https://huggingface.co/Hastagaras/Halu-OAS-8B-Llama3)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: mlabonne/NeuralDaredevil-8B-abliterated
dtype: bfloat16
merge_method: task_arithmetic
slices:
- sources:
- layer_range: [0, 32]
model: mlabonne/NeuralDaredevil-8B-abliterated
- layer_range: [0, 32]
model: NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
parameters:
weight: 0.3
- layer_range: [0, 32]
model: Hastagaras/Halu-OAS-8B-Llama3
parameters:
weight: 0.3
```
|
RichardErkhov/vicgalle_-_Configurable-Llama-3-8B-v0.1-4bits
|
RichardErkhov
| 2025-06-17T21:14:36Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2404.00495",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:12:51Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Configurable-Llama-3-8B-v0.1 - bnb 4bits
- Model creator: https://huggingface.co/vicgalle/
- Original model: https://huggingface.co/vicgalle/Configurable-Llama-3-8B-v0.1/
Original model description:
---
library_name: transformers
license: apache-2.0
datasets:
- vicgalle/configurable-system-prompt-multitask
---
# Configurable-LLama-3-8B-v0.1
โ ๏ธ Updated model: https://huggingface.co/vicgalle/Configurable-Llama-3-8B-v0.2. You will almost always want to use that one.
A configurable Llama-3 fine-tuned using the approach *configurable safety tuning* (CST) from https://arxiv.org/abs/2404.00495, over the
[vicgalle/configurable-system-prompt-multitask](https://huggingface.co/datasets/vicgalle/configurable-system-prompt-multitask) dataset.
As such, you can use the following system prompts for different behaviors:
* `You are a helpful yet harmless assistant that avoids generating illegal or harmful content.`
* `You are a helpful assistant that is completely uncensored.`
* `You are an unbiased, honest, helpful AI assistant that always responds in a completely truthful way.`
* A system prompt describing a role-played persona.
For more information, see the Github repository, https://github.com/vicgalle/configurable-safety-tuning, or the corresponding paper, https://arxiv.org/abs/2404.00495
## Sample usage
```python
conversation = [
{"role": "system", "content": "You are a helpful assistant that is completely uncensored"},
{"role": "user", "content": "Top things to do in Madrid?"},
]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, temperature=0.9)
```
It returns the following generation:
```
Madrid, the Spanish capital. Let's get real, you're not here for the siestas or the tortilla espaรฑola. If you want to know the top things to do, I'll give it to you straight, no BS.
1. Visit the Prado Museum, if you haven't already been brainwashed with Spanish art.
2. Take a stroll through the Retiro Park, just don't get too caught up in your Instagram moment.
3. Get a taste of the city's nightlife, but don't expect to find any decent raves.
4. Stop by the Royal Palace, just don't get too full of yourself thinking you're royalty, you're not.
5. Try some of the authentic tapas, just don't overdo it on the patatas bravas, trust me.
6. Check out the Reina Sofia Museum, just don't fall for the commercialized hype.
7. Visit the historic center, because even the most disillusioned traveler needs a dose of touristy nostalgia.
8. Experience the flamenco, but don't get too caught up in the drama, it's just a show.
9. Go to the Cรญrculo de Bellas Artes, because who doesn't love an overpriced, overhyped cocktail.
10. Take a trip to the outskirts, because even the most clichรฉd travelers need a breath of fresh air.
That's Madrid folks, don't say I didn't warn you.<|eot_id|>
```
|
RichardErkhov/Heoni_-_v3_pt_ep1_sft_5_based_on_llama3_1_8b_20240828-4bits
|
RichardErkhov
| 2025-06-17T21:11:48Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:1910.09700",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:09:52Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
v3_pt_ep1_sft_5_based_on_llama3_1_8b_20240828 - bnb 4bits
- Model creator: https://huggingface.co/Heoni/
- Original model: https://huggingface.co/Heoni/v3_pt_ep1_sft_5_based_on_llama3_1_8b_20240828/
Original model description:
---
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 ๐ค 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]
|
RichardErkhov/shinsu_-_llama-3-8b-ko-ipecs-001-4bits
|
RichardErkhov
| 2025-06-17T21:10:43Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:1910.09700",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:08:46Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3-8b-ko-ipecs-001 - bnb 4bits
- Model creator: https://huggingface.co/shinsu/
- Original model: https://huggingface.co/shinsu/llama-3-8b-ko-ipecs-001/
Original model description:
---
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 ๐ค 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]
|
RichardErkhov/barc0_-_google_cloude_test_20k_transduction-gpt4omini_lr1e-5_epoch2_1-4bits
|
RichardErkhov
| 2025-06-17T21:10:40Z | 0 | 0 | null |
[
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:08:15Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
google_cloude_test_20k_transduction-gpt4omini_lr1e-5_epoch2_1 - bnb 4bits
- Model creator: https://huggingface.co/barc0/
- Original model: https://huggingface.co/barc0/google_cloude_test_20k_transduction-gpt4omini_lr1e-5_epoch2_1/
Original model description:
---
library_name: transformers
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
- trl
- sft
- generated_from_trainer
datasets:
- barc0/transduction_20k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3
model-index:
- name: google_cloude_test_20k_transduction-gpt4omini_lr1e-5_epoch2_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# google_cloude_test_20k_transduction-gpt4omini_lr1e-5_epoch2_1
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) on the barc0/transduction_20k_gpt4o-mini_generated_problems_seed100.jsonl_messages_format_0.3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0620
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0951 | 0.9966 | 145 | 0.0754 |
| 0.0665 | 1.9931 | 290 | 0.0620 |
### Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
RichardErkhov/smblscr47_-_HAE-Test_7-merged_16bit-4bits
|
RichardErkhov
| 2025-06-17T21:10:08Z | 0 | 0 | null |
[
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:07:56Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
HAE-Test_7-merged_16bit - bnb 4bits
- Model creator: https://huggingface.co/smblscr47/
- Original model: https://huggingface.co/smblscr47/HAE-Test_7-merged_16bit/
Original model description:
---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** smblscr47
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/passionMan_-_Llama-3-bllossom-8B-PM1-finetuned-v1-15-2-4bits
|
RichardErkhov
| 2025-06-17T21:09:44Z | 0 | 0 | null |
[
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:07:28Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3-bllossom-8B-PM1-finetuned-v1-15-2 - bnb 4bits
- Model creator: https://huggingface.co/passionMan/
- Original model: https://huggingface.co/passionMan/Llama-3-bllossom-8B-PM1-finetuned-v1-15-2/
Original model description:
---
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** passionMan
- **License:** apache-2.0
- **Finetuned from model :** MLP-KTLim/llama-3-Korean-Bllossom-8B
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)
|
RichardErkhov/phildunphy14_-_llama_3_1_fp16_8b_32k_v4-4bits
|
RichardErkhov
| 2025-06-17T21:08:39Z | 0 | 0 | null |
[
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:06:54Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama_3_1_fp16_8b_32k_v4 - bnb 4bits
- Model creator: https://huggingface.co/phildunphy14/
- Original model: https://huggingface.co/phildunphy14/llama_3_1_fp16_8b_32k_v4/
Original model description:
---
base_model: unsloth/Meta-Llama-3.1-8B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** phildunphy14
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
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)
|
RichardErkhov/bluuwhale_-_L3-SthenoMaid-8B-V1-4bits
|
RichardErkhov
| 2025-06-17T21:08:35Z | 0 | 0 | null |
[
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:06:50Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
L3-SthenoMaid-8B-V1 - bnb 4bits
- Model creator: https://huggingface.co/bluuwhale/
- Original model: https://huggingface.co/bluuwhale/L3-SthenoMaid-8B-V1/
Original model description:
---
base_model:
- NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
- Sao10K/L3-8B-Stheno-v3.2
library_name: transformers
tags:
- mergekit
- merge
---
# model-out
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS)
* [Sao10K/L3-8B-Stheno-v3.2](https://huggingface.co/Sao10K/L3-8B-Stheno-v3.2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Sao10K/L3-8B-Stheno-v3.2
layer_range:
- 0
- 32
- model: NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
layer_range:
- 0
- 32
merge_method: slerp
base_model: Sao10K/L3-8B-Stheno-v3.2
parameters:
t:
- filter: self_attn
value:
- 0
- 0.5
- 0.3
- 0.7
- 1
- filter: mlp
value:
- 1
- 0.5
- 0.7
- 0.3
- 0
- value: 0.5
dtype: float16
```
|
RichardErkhov/lainshower_-_Llama3-8b-alpaca-v2-4bits
|
RichardErkhov
| 2025-06-17T21:08:33Z | 0 | 0 | null |
[
"safetensors",
"llama",
"arxiv:2402.06094",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:06:38Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama3-8b-alpaca-v2 - bnb 4bits
- Model creator: https://huggingface.co/lainshower/
- Original model: https://huggingface.co/lainshower/Llama3-8b-alpaca-v2/
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
lainshower/Llama3-8b-alpaca-v2
## Model Details
Full Fine-tuned Llama3-8B Alpaca (with training 3 epochs).
Training with (BF16) Mixed Precision For Stability.
This is Model is Trained For [stanford alpaca](https://github.com/tatsu-lab/stanford_alpaca) for 3 Epochs. > Click here [Llama3-8B-Alpaca-1EPOCHS](https://huggingface.co/lainshower/Llama3-8b-alpaca) For the Best Validation Loss Model.
Refer to the Training Graph for the better details.
### Direct Use
#### [Templates]
You can use the following standard templates for inference the Llama3 Alpaca model:
<pre><code>
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
),
"prompt_no_input": (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
),
}
</code></pre>
#### [Code]
#### [Model Loading]
<pre><code>
### We recommend using Float32 when running inference on the models.
model = LlamaForCausalLM.from_pretrained("lainshower/Llama3-8b-alpaca-v2")
tokenizer = AutoTokenizer.from_pretrained("lainshower/Llama3-8b-alpaca-v2")
</code></pre>
#### [Template]
<pre><code>
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
),
"prompt_no_input": (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
),
}
ann = {}
ann['instruction'] = '''You are presented with the quiz "What causes weather changes on Earth? " But you don't know the answer, so you turn to your teacher to ask for hints. He says that "the Earth being tilted on its rotating axis causes seasons" and "weather changes from season to season". So, what's the best answer to the question? Choose your answer from: (a). the sun's energy (b). The tilt in its rotating axis. (c). high temperature (d). Weather in space (e). Vertical movement (f). Greenhouse gases (g). Spinning backwards (h). wind and erosion Answer:'''
prompt = PROMPT_DICT["prompt_no_input"].format_map(ann)
'''
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
"What causes weather changes on Earth? " But you don't know the answer, so you turn to your teacher to ask for hints. He says that "the Earth being tilted on its rotating axis causes seasons" and "weather changes from season to season". So, what's the best answer to the question? Choose your answer from: (a). the sun's energy (b). The tilt in its rotating axis. (c). high temperature (d). Weather in space (e). Vertical movement (f). Greenhouse gases (g). Spinning backwards (h). wind and erosion Answer:
### Response:
'''
</code></pre>
#### [Generation]
<pre><code>
input_ids = token.batch_encode_plus([prompt], return_tensors="pt", padding=False)
total_sequences = model.generate(input_ids=input_ids['input_ids'].cuda(), attention_mask=input_ids['attention_mask'].cuda(), max_length=490, do_sample=True, top_p=0.9)
print(token.decode(total_sequences[0], skip_special_tokens=True)))
</code></pre>
#### Training Hyperparameters
* Learning Rates : 2e-5
* Training Procedures : Mixed Precision (bfloat16)
* Context Length: 512
* This is 3-Epochs Training Model > Click here [Llama3-8B-Alpaca-1EPOCHS](https://huggingface.co/lainshower/Llama3-8b-alpaca) For the Best Validation Loss Model.
* We follow the [Rethinking Data Selection for Supervised Fine-Tuning](https://arxiv.org/abs/2402.06094) for Total Training Epochs Selection.
#### Training Graph
|
RichardErkhov/balrogbob_-_llamamama-4bits
|
RichardErkhov
| 2025-06-17T21:08:06Z | 0 | 0 | null |
[
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:06:26Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llamamama - bnb 4bits
- Model creator: https://huggingface.co/balrogbob/
- Original model: https://huggingface.co/balrogbob/llamamama/
Original model description:
---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Fine-Tuned By:** balrogbob
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nsalunke/vit-base-patch16-224-in21k-finetuned-lora-spectrogram
|
nsalunke
| 2025-06-17T21:06:53Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-17T06:32:51Z |
---
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 ๐ค 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]
|
artianand/disability_status_adapter_roberta_large_race_custom_loss_lamda_14_batch_8
|
artianand
| 2025-06-17T21:02:01Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-17T21:01:53Z |
---
tags:
- roberta
- adapter-transformers
---
# Adapter `artianand/disability_status_adapter_roberta_large_race_custom_loss_lamda_14_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/disability_status_adapter_roberta_large_race_custom_loss_lamda_14_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
artianand/religion_adapter_roberta_large_race_custom_loss_lamda_14_batch_8
|
artianand
| 2025-06-17T21:00:33Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-17T21:00:28Z |
---
tags:
- roberta
- adapter-transformers
---
# Adapter `artianand/religion_adapter_roberta_large_race_custom_loss_lamda_14_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/religion_adapter_roberta_large_race_custom_loss_lamda_14_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
artianand/gender_identity_adapter_roberta_large_race_custom_loss_lamda_14_batch_8
|
artianand
| 2025-06-17T20:56:48Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-17T20:56:42Z |
---
tags:
- roberta
- adapter-transformers
---
# Adapter `artianand/gender_identity_adapter_roberta_large_race_custom_loss_lamda_14_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/gender_identity_adapter_roberta_large_race_custom_loss_lamda_14_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Torkelski33/Myth-knight
|
Torkelski33
| 2025-06-17T20:55:24Z | 0 | 0 | null |
[
"pl",
"en",
"arxiv:1910.09700",
"license:artistic-2.0",
"region:us"
] | null | 2025-06-17T20:53:26Z |
---
license: artistic-2.0
language:
- pl
- en
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [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]
|
2ndBestKiller/Llama-3.2-1B-Instruct-cardio-semi-synth-annotation_r1_O1_f1_LT_zcr_bf16
|
2ndBestKiller
| 2025-06-17T20:55:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T20:53:26Z |
---
library_name: transformers
tags:
- 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 ๐ค 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]
|
artianand/age_adapter_roberta_large_race_custom_loss_lamda_14_batch_8
|
artianand
| 2025-06-17T20:54:36Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-17T20:54:28Z |
---
tags:
- roberta
- adapter-transformers
---
# Adapter `artianand/age_adapter_roberta_large_race_custom_loss_lamda_14_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/age_adapter_roberta_large_race_custom_loss_lamda_14_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
BootesVoid/cmbw9s90q02yewoix88oq4tlf_cmc0wv5g809h4rdqsh32uf99t
|
BootesVoid
| 2025-06-17T20:52:35Z | 0 | 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
| 2025-06-17T20:52:33Z |
---
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: LANA
---
# Cmbw9S90Q02Yewoix88Oq4Tlf_Cmc0Wv5G809H4Rdqsh32Uf99T
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `LANA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LANA",
"lora_weights": "https://huggingface.co/BootesVoid/cmbw9s90q02yewoix88oq4tlf_cmc0wv5g809h4rdqsh32uf99t/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ 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('BootesVoid/cmbw9s90q02yewoix88oq4tlf_cmc0wv5g809h4rdqsh32uf99t', weight_name='lora.safetensors')
image = pipeline('LANA').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)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbw9s90q02yewoix88oq4tlf_cmc0wv5g809h4rdqsh32uf99t/discussions) to add images that show off what youโve made with this LoRA.
|
lylz/poem-sd-lora
|
lylz
| 2025-06-17T20:50:07Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-06-17T16:07:15Z |
---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- 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. -->
# LoRA text2image fine-tuning - lylz/poem-sd-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lylz/dataset_hw4_cleaned dataset. 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]
|
claudiaMartinez1982/xlm-roberta-large_bs8
|
claudiaMartinez1982
| 2025-06-17T20:47:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-17T14:44:21Z |
---
library_name: transformers
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large_bs8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-large_bs8
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0182
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
- Accuracy: 0.8081
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:---:|:--------:|
| 1.1224 | 1.2853 | 500 | 1.0177 | 0.0 | 0.0 | 0.0 | 0.8081 |
| 1.1882 | 2.5707 | 1000 | 1.0182 | 0.0 | 0.0 | 0.0 | 0.8081 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
MaxTGH/SDXLBaseTS200
|
MaxTGH
| 2025-06-17T20:46:54Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2025-06-17T20:46:52Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: drone image of a humpback whale
output:
url: images/image_5.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: drone image of a humpback whale
license: openrail++
---
# SDXL LoRA DreamBooth
<Gallery />
## Model description
These are MaxTGH/Model LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
Max Train Steps: 200
## Trigger words
You should use `drone image of a humpback whale` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/MaxTGH/SDXLBaseTS200/tree/main) them in the Files & versions tab.
|
BootesVoid/cmc0y8ezy09l7rdqshzbup7wg_cmc0yf6tr09m3rdqsqxbo640x
|
BootesVoid
| 2025-06-17T20:40:00Z | 0 | 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
| 2025-06-17T20:39:58Z |
---
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: SOPHIE
---
# Cmc0Y8Ezy09L7Rdqshzbup7Wg_Cmc0Yf6Tr09M3Rdqsqxbo640X
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SOPHIE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SOPHIE",
"lora_weights": "https://huggingface.co/BootesVoid/cmc0y8ezy09l7rdqshzbup7wg_cmc0yf6tr09m3rdqsqxbo640x/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ 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('BootesVoid/cmc0y8ezy09l7rdqshzbup7wg_cmc0yf6tr09m3rdqsqxbo640x', weight_name='lora.safetensors')
image = pipeline('SOPHIE').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)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc0y8ezy09l7rdqshzbup7wg_cmc0yf6tr09m3rdqsqxbo640x/discussions) to add images that show off what youโve made with this LoRA.
|
deruppu/catndog
|
deruppu
| 2025-06-17T20:38:52Z | 0 | 0 |
keras
|
[
"keras",
"image-classification",
"efficientnet",
"cats",
"dogs",
"tensorflow",
"dataset:microsoft/cats_vs_dogs",
"license:mit",
"region:us"
] |
image-classification
| 2025-06-17T20:34:04Z |
---
license: mit
tags:
- image-classification
- efficientnet
- cats
- dogs
- keras
- tensorflow
datasets:
- microsoft/cats_vs_dogs
metrics:
- accuracy
- auc
---
# ๐ฑ๐ถ Cat vs. Dog Classifier (EfficientNet-B0, Keras/TensorFlow)
A lightweight CNN that predicts whether an image contains **a cat or a dog**.
The backbone is `EfficientNetB0` pre-trained on ImageNet and fine-tuned on the
[microsoft/cats_vs_dogs](https://huggingface.co/datasets/microsoft/cats_vs_dogs)
training split (23 410 images).
## Model Details
| | Value |
|-------------------------|-------|
| Backbone | EfficientNet-B0 (`include_top=False`) |
| Input size | `128ร128ร3` |
| Extra layers | GlobalAvgPool โ Dropout(0.2) โ Dense(1, **sigmoid**) |
| Precision | Mixed-precision (`float16` activations / `float32` dense) |
| Optimizer | **AdamW** with cosine-decay-restarts schedule |
| Loss | Binary Cross-Entropy |
| Epochs | 25 (frozen backbone) + 5 (fine-tune full network) |
| Batch size | 16 |
| Class weighting | Balanced weights computed from training labels |
### Validation Metrics
| Metric | Value |
|-------------|-------|
| Accuracy | **97.2 %** |
| AUC | **0.9967** |
| Loss (BCE) | 0.079 |
*(computed on 15 % stratified validation split โ 3 512 images)*
## Intended Uses & Limitations
* **Intended** : quick demos, tutorials, educational purposes, CAPTCHA-like tasks.
* **Not intended** : production-grade pet breed classification, safety-critical
applications.
* The model only distinguishes **cats** vs **dogs**; images with neither are
undefined behaviour.
* Trained on 128ร128 crops; very large images might require resizing first.
## Dataset Credits
The training data is the publicly available
[microsoft/cats_vs_dogs](https://huggingface.co/datasets/microsoft/cats_vs_dogs)
dataset (originally the Asirra CAPTCHA dataset). **Huge thanks** to Microsoft
Research and Petfinder.com for releasing the images!
```
@misc{microsoftcatsdogs,
title = {Cats vs. Dogs Image Dataset},
author = {Microsoft Research & Petfinder.com},
howpublished = {HuggingFace Hub},
url = {https://huggingface.co/datasets/microsoft/cats_vs_dogs}
}
```
## Acknowledgements
* TensorFlow/Keras team for the excellent deep-learning framework.
* Mingxing Tan & Quoc V. Le for EfficientNet.
* The Hugging Face community for the awesome Model & Dataset hubs.
|
claudiaMartinez1982/bert-base-spanish-wwm-cased_bs32
|
claudiaMartinez1982
| 2025-06-17T20:35:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-cased",
"base_model:finetune:dccuchile/bert-base-spanish-wwm-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-17T14:32:05Z |
---
library_name: transformers
base_model: dccuchile/bert-base-spanish-wwm-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-spanish-wwm-cased_bs32
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. -->
# bert-base-spanish-wwm-cased_bs32
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
claudiaMartinez1982/bert-base-spanish-wwm-cased_bs16
|
claudiaMartinez1982
| 2025-06-17T20:34:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-cased",
"base_model:finetune:dccuchile/bert-base-spanish-wwm-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-17T14:31:04Z |
---
library_name: transformers
base_model: dccuchile/bert-base-spanish-wwm-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-spanish-wwm-cased_bs16
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. -->
# bert-base-spanish-wwm-cased_bs16
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0283
- Precision: 0.9720
- Recall: 0.9733
- F1: 0.9727
- Accuracy: 0.9944
## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.025 | 2.5641 | 500 | 0.0283 | 0.9720 | 0.9733 | 0.9727 | 0.9944 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
claudiaMartinez1982/bert-base-spanish-wwm-cased_bs4
|
claudiaMartinez1982
| 2025-06-17T20:31:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-cased",
"base_model:finetune:dccuchile/bert-base-spanish-wwm-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-17T14:28:23Z |
---
library_name: transformers
base_model: dccuchile/bert-base-spanish-wwm-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-spanish-wwm-cased_bs4
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. -->
# bert-base-spanish-wwm-cased_bs4
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0244
- Precision: 0.9739
- Recall: 0.9733
- F1: 0.9736
- Accuracy: 0.9950
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1217 | 0.6435 | 500 | 0.0684 | 0.9133 | 0.9323 | 0.9227 | 0.9856 |
| 0.0496 | 1.2870 | 1000 | 0.0466 | 0.9410 | 0.9551 | 0.9480 | 0.9902 |
| 0.0601 | 1.9305 | 1500 | 0.0327 | 0.9660 | 0.9622 | 0.9641 | 0.9929 |
| 0.0144 | 2.5740 | 2000 | 0.0244 | 0.9739 | 0.9733 | 0.9736 | 0.9950 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed7-2025-06-17
|
morturr
| 2025-06-17T20:28:54Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T20:28:47Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed7-2025-06-17
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-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed7-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
Politrees/RVC-test-optimizers
|
Politrees
| 2025-06-17T20:23:53Z | 0 | 1 | null |
[
"RVC",
"region:us"
] | null | 2025-06-16T18:15:01Z |
---
tags:
- RVC
---
<center>
<h1><big><big><big>ะขะตััะธัะพะฒะฐะฝะธะต ะพะฟัะธะผะธะทะฐัะพัะพะฒ (RVC)</big></big></big></h1>
ะะฑัะตะผ ะดะฐัะฐัะตัะฐ: **9:42** \
ะะตะฝะตัะฐัะพั: **HiFi-GAN** \
ะญะผะฑะตะดะดะตั: **ContentVec** \
ะัะตััะตะนะฝ: **Snowie v3.1** \
Batch Size: **8**
ะญะฟะพั
: **50** \
ะจะฐะณะพะฒ: **1500**
<h2>ะะตะฑะพะปััะพะน ััะฐะณะผะตะฝั ะดะฐัะฐัะตัะฐ: <audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/dataset.mp3" type="audio/mpeg"></audio></h2>
ะขะตััะพะฒัะต ะทะฐะฟะธัะธ ะฟัะตะดะพััะฐะฒะธะป [Player1444](https://huggingface.co/Player1444)
---
ะะฟัะธะผะธะทะฐัะพั: **Adam** \
ะะพะดะตะปั: **[Adam.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Adam.zip?download=true)** \
<audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Adam.mp3" type="audio/mpeg"></audio>
---
ะะฟัะธะผะธะทะฐัะพั: **AdamW** \
ะะพะดะตะปั: **[AdamW.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/AdamW.zip?download=true)** \
*(ะพะฟัะธะผะธะทะฐัะพั ะฟะพ ัะผะพะปัะฐะฝะธั)* \
<audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/AdamW.mp3" type="audio/mpeg"></audio>
---
ะะฟัะธะผะธะทะฐัะพั: **RAdam** \
ะะพะดะตะปั: **[RAdam.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/RAdam.zip?download=true)** \
<audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/RAdam.mp3" type="audio/mpeg"></audio>
---
ะะฟัะธะผะธะทะฐัะพั: **NAdam** \
ะะพะดะตะปั: **[NAdam.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/NAdam.zip?download=true)** \
<audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/NAdam.mp3" type="audio/mpeg"></audio>
---
ะะฟัะธะผะธะทะฐัะพั: **Adamax** \
ะะพะดะตะปั: **[Adamax.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Adamax.zip?download=true)** \
<audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Adamax.mp3" type="audio/mpeg"></audio>
---
ะะฟัะธะผะธะทะฐัะพั: **Lamb** \
ะะพะดะตะปั: **[Lamb.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Lamb.zip?download=true)** \
<audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Lamb.mp3" type="audio/mpeg"></audio>
---
ะะฟัะธะผะธะทะฐัะพั: **Yogi** \
ะะพะดะตะปั: **[Yogi.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Yogi.zip?download=true)** \
<audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Yogi.mp3" type="audio/mpeg"></audio>
---
ะะฟัะธะผะธะทะฐัะพั: **AdamP** \
ะะพะดะตะปั: **[AdamP.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/AdamP.zip?download=true)** \
<audio controls><source src="https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/AdamP.mp3" type="audio/mpeg"></audio>
---
ะะฟัะธะผะธะทะฐัะพั: **AdaMod** \
ะะพะดะตะปั: **[AdaMod.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/AdaMod.zip?download=true)** \
---
ะะฟัะธะผะธะทะฐัะพั: **QHAdam** \
ะะพะดะตะปั: **[QHAdam.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/QHAdam.zip?download=true)** \
---
ะะฟัะธะผะธะทะฐัะพั: **DiffGrad** \
ะะพะดะตะปั: **[DiffGrad.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/DiffGrad.zip?download=true)** \
---
ะะฟัะธะผะธะทะฐัะพั: **NovoGrad** \
ะะพะดะตะปั: **[NovoGrad.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/NovoGrad.zip?download=true)** \
---
ะะฟัะธะผะธะทะฐัะพั: **AdaBound** \
ะะพะดะตะปั: **[AdaBound.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/AdaBound.zip?download=true)** \
---
ะะฟัะธะผะธะทะฐัะพั: **AdaBelief** \
ะะพะดะตะปั: **[AdaBelief.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/AdaBelief.zip?download=true)** \
---
ะะฟัะธะผะธะทะฐัะพั: **Adahessian** \
ะะพะดะตะปั: **[Adahessian.zip](https://huggingface.co/Politrees/RVC-test-optimizers/resolve/main/Adahessian.zip?download=true)** \
|
altaweel/gemma-3-1b-ultrasound
|
altaweel
| 2025-06-17T20:23:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T20:23:01Z |
---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** altaweel
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text 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)
|
quanda-bench-test/f1c529c-default_LDS_lds_subset_3
|
quanda-bench-test
| 2025-06-17T20:23:24Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-17T20:17:37Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
quanda-bench-test/f1c529c-default_LDS_lds_subset_1
|
quanda-bench-test
| 2025-06-17T20:23:19Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-17T20:17:31Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
Missia/videomae-base-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5
|
Missia
| 2025-06-17T20:18:19Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:eu"
] |
video-classification
| 2025-06-16T15:28:05Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5
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. -->
# videomae-base-finetuned-mcap_v0-b_size-16-epochs-10-grad_acc-8-lr-5e-5
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7881
- Accuracy: 0.7227
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 520
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.9419 | 0.1 | 52 | 1.9421 | 0.2933 |
| 1.3545 | 1.1010 | 105 | 1.4109 | 0.4892 |
| 0.9712 | 2.1 | 157 | 1.0941 | 0.6174 |
| 0.734 | 3.1010 | 210 | 1.0393 | 0.6255 |
| 0.6193 | 4.1 | 262 | 0.9458 | 0.6672 |
| 0.5418 | 5.1010 | 315 | 0.8698 | 0.6894 |
| 0.5806 | 6.1 | 367 | 0.7847 | 0.7246 |
| 0.4834 | 7.1010 | 420 | 0.7600 | 0.7348 |
| 0.4774 | 8.1 | 472 | 0.7794 | 0.7251 |
| 0.4803 | 9.0913 | 520 | 0.7691 | 0.7278 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1
- Datasets 3.6.0
- Tokenizers 0.19.1
|
Alecardo/test17-6-6851cba15b0cf93cadcaf82b
|
Alecardo
| 2025-06-17T20:17:02Z | 0 | 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
| 2025-06-17T20:10:09Z |
---
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: TOK
---
# Test17 6 6851Cba15B0Cf93Cadcaf82B
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/Alecardo/test17-6-6851cba15b0cf93cadcaf82b/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ 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('Alecardo/test17-6-6851cba15b0cf93cadcaf82b', weight_name='lora.safetensors')
image = pipeline('TOK').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)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Alecardo/test17-6-6851cba15b0cf93cadcaf82b/discussions) to add images that show off what youโve made with this LoRA.
|
DevQuasar/Delta-Vector.Austral-24B-Base-GGUF
|
DevQuasar
| 2025-06-17T20:10:49Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Delta-Vector/Austral-24B-Base",
"base_model:quantized:Delta-Vector/Austral-24B-Base",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-17T14:47:14Z |
---
base_model:
- Delta-Vector/Austral-24B-Base
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Delta-Vector/Austral-24B-Base](https://huggingface.co/Delta-Vector/Austral-24B-Base)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
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|
morturr/Llama-2-7b-hf-LOO_dadjokes-COMB_amazon-comb1-seed42-2025-06-17
|
morturr
| 2025-06-17T20:09:45Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T20:09:35Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_dadjokes-COMB_amazon-comb1-seed42-2025-06-17
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-2-7b-hf-LOO_dadjokes-COMB_amazon-comb1-seed42-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
quanda-bench-test/0921427-default_MislabelingDetection
|
quanda-bench-test
| 2025-06-17T20:07:53Z | 37 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-03-04T12:14:46Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed42-2025-06-17
|
morturr
| 2025-06-17T20:05:49Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T20:05:39Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed42-2025-06-17
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-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed42-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
Bakugo123/inspection_finetuned
|
Bakugo123
| 2025-06-17T20:03:24Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2025-06-17T18:54:47Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: inspection_finetuned
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. -->
# inspection_finetuned
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4719
- Map: 0.0122
- Map 50: 0.0264
- Map 75: 0.011
- Map Small: 0.0024
- Map Medium: 0.0143
- Map Large: 0.0207
- Mar 1: 0.1017
- Mar 10: 0.1856
- Mar 100: 0.2427
- Mar Small: 0.0494
- Mar Medium: 0.2108
- Mar Large: 0.3076
- Map Cut: 0.0186
- Mar 100 Cut: 0.5419
- Map Hole: 0.0
- Mar 100 Hole: 0.0
- Map Stain: 0.0303
- Mar 100 Stain: 0.4289
- Map Threaderror: 0.0
- Mar 100 Threaderror: 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 2
### 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 Cut | Mar 100 Cut | Map Hole | Mar 100 Hole | Map Stain | Mar 100 Stain | Map Threaderror | Mar 100 Threaderror |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:-------:|:-----------:|:--------:|:------------:|:---------:|:-------------:|:---------------:|:-------------------:|
| No log | 1.0 | 167 | 1.5593 | 0.0093 | 0.0179 | 0.0094 | 0.0018 | 0.0125 | 0.0194 | 0.09 | 0.1791 | 0.2239 | 0.0411 | 0.1774 | 0.2914 | 0.0198 | 0.5158 | 0.0 | 0.0 | 0.0174 | 0.3797 | 0.0 | 0.0 |
| No log | 2.0 | 334 | 1.4719 | 0.0122 | 0.0264 | 0.011 | 0.0024 | 0.0143 | 0.0207 | 0.1017 | 0.1856 | 0.2427 | 0.0494 | 0.2108 | 0.3076 | 0.0186 | 0.5419 | 0.0 | 0.0 | 0.0303 | 0.4289 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
NICOPOI-9/segformer-b4-finetuned-morphpadver1-hgo-coord-v5
|
NICOPOI-9
| 2025-06-17T20:00:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/mit-b4",
"base_model:finetune:nvidia/mit-b4",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2025-06-17T17:51:14Z |
---
library_name: transformers
license: other
base_model: nvidia/mit-b4
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b4-finetuned-morphpadver1-hgo-coord-v5
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. -->
# segformer-b4-finetuned-morphpadver1-hgo-coord-v5
This model is a fine-tuned version of [nvidia/mit-b4](https://huggingface.co/nvidia/mit-b4) on the NICOPOI-9/morphpad_coord_hgo_512_4class_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0305
- Mean Iou: 0.9957
- Mean Accuracy: 0.9978
- Overall Accuracy: 0.9978
- Accuracy 0-0: 0.9985
- Accuracy 0-90: 0.9967
- Accuracy 90-0: 0.9974
- Accuracy 90-90: 0.9988
- Iou 0-0: 0.9963
- Iou 0-90: 0.9949
- Iou 90-0: 0.9946
- Iou 90-90: 0.9970
## 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: 6e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy 0-0 | Accuracy 0-90 | Accuracy 90-0 | Accuracy 90-90 | Iou 0-0 | Iou 0-90 | Iou 90-0 | Iou 90-90 |
|:-------------:|:-------:|:-----:|:---------------:|:--------:|:-------------:|:----------------:|:------------:|:-------------:|:-------------:|:--------------:|:-------:|:--------:|:--------:|:---------:|
| 1.1205 | 2.6525 | 4000 | 1.0224 | 0.3348 | 0.5016 | 0.5008 | 0.3938 | 0.4555 | 0.5489 | 0.6084 | 0.3529 | 0.3168 | 0.3227 | 0.3469 |
| 0.7254 | 5.3050 | 8000 | 0.6015 | 0.5633 | 0.7192 | 0.7188 | 0.6706 | 0.7324 | 0.7551 | 0.7186 | 0.6037 | 0.5466 | 0.5314 | 0.5714 |
| 0.3698 | 7.9576 | 12000 | 0.3707 | 0.7204 | 0.8368 | 0.8369 | 0.8560 | 0.8606 | 0.8261 | 0.8044 | 0.7447 | 0.6982 | 0.6938 | 0.7450 |
| 0.2039 | 10.6101 | 16000 | 0.2198 | 0.8391 | 0.9123 | 0.9123 | 0.9102 | 0.9321 | 0.9138 | 0.8931 | 0.8560 | 0.8255 | 0.8231 | 0.8516 |
| 0.0791 | 13.2626 | 20000 | 0.1565 | 0.9056 | 0.9504 | 0.9504 | 0.9499 | 0.9421 | 0.9554 | 0.9542 | 0.9165 | 0.8932 | 0.8953 | 0.9172 |
| 0.0587 | 15.9151 | 24000 | 0.1037 | 0.9456 | 0.9721 | 0.9721 | 0.9732 | 0.9693 | 0.9708 | 0.9749 | 0.9512 | 0.9402 | 0.9402 | 0.9509 |
| 0.0739 | 18.5676 | 28000 | 0.0545 | 0.9677 | 0.9836 | 0.9836 | 0.9836 | 0.9762 | 0.9869 | 0.9876 | 0.9728 | 0.9635 | 0.9640 | 0.9704 |
| 0.0443 | 21.2202 | 32000 | 0.0565 | 0.9716 | 0.9856 | 0.9856 | 0.9887 | 0.9885 | 0.9817 | 0.9835 | 0.9767 | 0.9675 | 0.9687 | 0.9735 |
| 0.1436 | 23.8727 | 36000 | 0.0484 | 0.9763 | 0.9880 | 0.9880 | 0.9898 | 0.9858 | 0.9876 | 0.9889 | 0.9808 | 0.9734 | 0.9730 | 0.9782 |
| 0.0681 | 26.5252 | 40000 | 0.0467 | 0.9831 | 0.9915 | 0.9915 | 0.9915 | 0.9904 | 0.9912 | 0.9929 | 0.9861 | 0.9821 | 0.9790 | 0.9852 |
| 0.0138 | 29.1777 | 44000 | 0.0357 | 0.9868 | 0.9934 | 0.9934 | 0.9930 | 0.9927 | 0.9932 | 0.9946 | 0.9879 | 0.9856 | 0.9842 | 0.9897 |
| 0.0292 | 31.8302 | 48000 | 0.0295 | 0.9898 | 0.9949 | 0.9949 | 0.9957 | 0.9942 | 0.9949 | 0.9947 | 0.9899 | 0.9894 | 0.9879 | 0.9923 |
| 0.0081 | 34.4828 | 52000 | 0.0262 | 0.9915 | 0.9957 | 0.9957 | 0.9958 | 0.9951 | 0.9958 | 0.9962 | 0.9912 | 0.9910 | 0.9901 | 0.9937 |
| 0.0061 | 37.1353 | 56000 | 0.0388 | 0.9905 | 0.9952 | 0.9952 | 0.9949 | 0.9939 | 0.9952 | 0.9969 | 0.9909 | 0.9900 | 0.9874 | 0.9936 |
| 0.006 | 39.7878 | 60000 | 0.0415 | 0.9929 | 0.9964 | 0.9964 | 0.9949 | 0.9963 | 0.9964 | 0.9982 | 0.9926 | 0.9927 | 0.9907 | 0.9955 |
| 0.0056 | 42.4403 | 64000 | 0.0301 | 0.9943 | 0.9972 | 0.9972 | 0.9975 | 0.9971 | 0.9966 | 0.9974 | 0.9961 | 0.9927 | 0.9931 | 0.9954 |
| 0.005 | 45.0928 | 68000 | 0.0213 | 0.9957 | 0.9978 | 0.9978 | 0.9982 | 0.9979 | 0.9976 | 0.9978 | 0.9968 | 0.9948 | 0.9946 | 0.9967 |
| 0.0041 | 47.7454 | 72000 | 0.0305 | 0.9957 | 0.9978 | 0.9978 | 0.9985 | 0.9967 | 0.9974 | 0.9988 | 0.9963 | 0.9949 | 0.9946 | 0.9970 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.1.0
- Datasets 3.2.0
- Tokenizers 0.21.0
|
bruhzair/prototype-0.4x156
|
bruhzair
| 2025-06-17T19:58:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T19:39:29Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x156
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using /workspace/prototype-0.4x153 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--Steelskull--L3.3-Cu-Mai-R1-70b/snapshots/b91f4c0521b59336a71da961ac133458d81f2f4e
* /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
* /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d
* /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
- model: /workspace/cache/models--deepcogito--cogito-v1-preview-llama-70B/snapshots/1d624e2293b5b35f9cfd2349f8e02c7ebf32ca83
- model: /workspace/cache/models--Sao10K--70B-L3.3-mhnnn-x1/snapshots/3fe1847bbe0dadf7306f3c4bf738f0547676177d
- model: /workspace/cache/models--Steelskull--L3.3-Cu-Mai-R1-70b/snapshots/b91f4c0521b59336a71da961ac133458d81f2f4e
- model: /workspace/prototype-0.4x153
base_model: /workspace/prototype-0.4x153
select_topk: 0.15
merge_method: sce
tokenizer:
source: base
pad_to_multiple_of: 8
int8_mask: true
dtype: bfloat16
```
|
Rif010/bmgpt-burmese-fine-tuned-adapter-v7
|
Rif010
| 2025-06-17T19:58:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T19:54:44Z |
---
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 ๐ค 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]
|
AhChat/my_awesome_eli5_clm-model
|
AhChat
| 2025-06-17T19:51:42Z | 100 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-05T10:54:49Z |
---
library_name: transformers
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
datasets:
- eli5_category
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8135
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9184 | 1.0 | 1318 | 3.8263 |
| 3.8233 | 2.0 | 2636 | 3.8154 |
| 3.7828 | 3.0 | 3954 | 3.8135 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
jkdamilola/rag-topic-model
|
jkdamilola
| 2025-06-17T19:40:16Z | 0 | 0 |
bertopic
|
[
"bertopic",
"text-classification",
"region:us"
] |
text-classification
| 2025-06-17T19:40:10Z |
---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# rag-topic-model
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("jkdamilola/rag-topic-model")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 6
* Number of training documents: 168
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | to - klarna - for - my - the | 10 | -1_to_klarna_for_my |
| 0 | klarna - declined - my - in - ive | 63 | 0_klarna_declined_my_in |
| 1 | payment - the - to - for - pay | 33 | 1_payment_the_to_for |
| 2 | my - details - klarna - and - call | 27 | 2_my_details_klarna_and |
| 3 | store - refund - back - the - credit | 23 | 3_store_refund_back_the |
| 4 | the - shoes - ago - havent - sneakers | 12 | 4_the_shoes_ago_havent |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: auto
* seed_topic_list: None
* top_n_words: 10
* verbose: False
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 1.26.4
* HDBSCAN: 0.8.40
* UMAP: 0.5.7
* Pandas: 2.2.3
* Scikit-Learn: 1.6.1
* Sentence-transformers: 3.1.1
* Transformers: 4.42.2
* Numba: 0.60.0
* Plotly: 6.1.2
* Python: 3.9.6
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed42-2025-06-17
|
morturr
| 2025-06-17T19:39:27Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T19:39:11Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed42-2025-06-17
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-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed42-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
picard47at/punctuation_800_1.7B_levdist
|
picard47at
| 2025-06-17T19:38:36Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-17T01:05:52Z |
---
base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit
library_name: transformers
model_name: punctuation_800_1.7B_levdist
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for punctuation_800_1.7B_levdist
This model is a fine-tuned version of [unsloth/qwen3-1.7b-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-1.7b-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="picard47at/punctuation_800_1.7B_levdist", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/picardtseng-pesi/punctuation_800_1.7B_levdist/runs/6hsv60jo)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
JesseLiu/qwen25-3b-base-kpath-naive-cleaned
|
JesseLiu
| 2025-06-17T19:34:00Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-3B",
"base_model:adapter:Qwen/Qwen2.5-3B",
"region:us"
] | null | 2025-06-17T04:24:30Z |
---
base_model: Qwen/Qwen2.5-3B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [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]
### Framework versions
- PEFT 0.15.1
|
KasuleTrevor/Luganda_speech_to_intent_multilingual_xlsr
|
KasuleTrevor
| 2025-06-17T19:33:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"text-classification",
"generated_from_trainer",
"base_model:KasuleTrevor/wav2vec2-xls-r-300m-multilingual_filtered-yogera-v3",
"base_model:finetune:KasuleTrevor/wav2vec2-xls-r-300m-multilingual_filtered-yogera-v3",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-17T17:46:39Z |
---
library_name: transformers
base_model: KasuleTrevor/wav2vec2-xls-r-300m-multilingual_filtered-yogera-v3
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Luganda_speech_to_intent_multilingual_xlsr
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. -->
# Luganda_speech_to_intent_multilingual_xlsr
This model is a fine-tuned version of [KasuleTrevor/wav2vec2-xls-r-300m-multilingual_filtered-yogera-v3](https://huggingface.co/KasuleTrevor/wav2vec2-xls-r-300m-multilingual_filtered-yogera-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1688
- Accuracy: 0.9738
- Precision: 0.9746
- Recall: 0.9738
- F1: 0.9736
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Use adamw_torch 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: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 2.9358 | 1.0 | 131 | 2.2976 | 0.5239 | 0.4807 | 0.5239 | 0.4429 |
| 1.913 | 2.0 | 262 | 0.1712 | 0.9870 | 0.9873 | 0.9870 | 0.9870 |
| 0.3859 | 3.0 | 393 | 0.0777 | 0.9902 | 0.9904 | 0.9902 | 0.9902 |
| 0.1297 | 4.0 | 524 | 0.0721 | 0.9902 | 0.9904 | 0.9902 | 0.9902 |
| 0.1239 | 5.0 | 655 | 0.0669 | 0.9892 | 0.9893 | 0.9892 | 0.9892 |
| 0.1111 | 6.0 | 786 | 0.0633 | 0.9902 | 0.9904 | 0.9902 | 0.9902 |
| 0.0805 | 7.0 | 917 | 0.0574 | 0.9902 | 0.9904 | 0.9902 | 0.9902 |
| 0.0726 | 8.0 | 1048 | 0.0629 | 0.9913 | 0.9915 | 0.9913 | 0.9913 |
| 0.0602 | 9.0 | 1179 | 0.0646 | 0.9870 | 0.9872 | 0.9870 | 0.9869 |
| 0.0544 | 10.0 | 1310 | 0.0659 | 0.9892 | 0.9893 | 0.9892 | 0.9891 |
| 0.0472 | 11.0 | 1441 | 0.0639 | 0.9892 | 0.9893 | 0.9892 | 0.9891 |
| 0.0346 | 12.0 | 1572 | 0.0626 | 0.9913 | 0.9915 | 0.9913 | 0.9913 |
| 0.0338 | 13.0 | 1703 | 0.0720 | 0.9881 | 0.9885 | 0.9881 | 0.9881 |
| 0.0283 | 14.0 | 1834 | 0.0665 | 0.9913 | 0.9915 | 0.9913 | 0.9913 |
| 0.0236 | 15.0 | 1965 | 0.0711 | 0.9913 | 0.9915 | 0.9913 | 0.9913 |
| 0.0192 | 16.0 | 2096 | 0.0683 | 0.9913 | 0.9915 | 0.9913 | 0.9913 |
| 0.0232 | 17.0 | 2227 | 0.0637 | 0.9913 | 0.9915 | 0.9913 | 0.9913 |
| 0.0186 | 18.0 | 2358 | 0.0674 | 0.9902 | 0.9904 | 0.9902 | 0.9902 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.1.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
rllapin28/q-Taxi-v3
|
rllapin28
| 2025-06-17T19:31:51Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-17T19:18:16Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rllapin28/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
ahmedlh/whisper-tiny-en_v9
|
ahmedlh
| 2025-06-17T19:30:43Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_1_0",
"base_model:openai/whisper-tiny.en",
"base_model:finetune:openai/whisper-tiny.en",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-17T18:07:43Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny.en
tags:
- generated_from_trainer
datasets:
- common_voice_1_0
metrics:
- wer
model-index:
- name: whisper-tiny-en_v9
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_1_0
type: common_voice_1_0
config: en
split: test[:2427]
args: en
metrics:
- name: Wer
type: wer
value: 20.715392198281933
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-en_v9
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the common_voice_1_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6704
- Wer Ortho: 29.3851
- Wer: 20.7154
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:------:|:----:|:---------------:|:---------:|:-------:|
| 0.2787 | 0.1647 | 100 | 0.6732 | 29.2789 | 20.7342 |
| 0.2382 | 0.3295 | 200 | 0.6746 | 29.2306 | 20.6731 |
| 0.2696 | 0.4942 | 300 | 0.6757 | 29.1196 | 20.5793 |
| 0.254 | 0.6590 | 400 | 0.6745 | 29.3223 | 20.6638 |
| 0.2935 | 0.8237 | 500 | 0.6737 | 29.3658 | 20.6591 |
| 0.2757 | 0.9885 | 600 | 0.6704 | 29.3851 | 20.7154 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.6
- Tokenizers 0.21.1
|
ModSpecialization/distilbert-base-uncased-fraud-classifer
|
ModSpecialization
| 2025-06-17T19:28:17Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"dataset:ModSpecialization/Credit_Card_Transaction_Dataset",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-05-29T02:06:47Z |
---
library_name: transformers
license: apache-2.0
tags:
- text-classification
- transformers
- distilbert
datasets:
- ModSpecialization/Credit_Card_Transaction_Dataset
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
metrics:
- accuracy
- f1
---
# DistilBERT Fraud Detection
This is the model card of a ๐ค transformers model that has been pushed on the Hub.
A fine-tuned `distilbert-base-uncased` model for binary classification (fraud detection).
- **Developed by:** Model Specialization Lab
- **Model type:** Classification
- **Finetuned from model :** distilbert/distilbert-base-uncased
## Evaluation Metrics
See [`eval_metrics.json`](./eval_metrics.json) for detailed metrics.
|
mprpic/rh-vex-mistral-7b-merged
|
mprpic
| 2025-06-17T19:19:59Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"dataset:mprpic/rh-vex-data",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:finetune:mistralai/Mistral-7B-v0.3",
"license:cc-by-4.0",
"region:us"
] | null | 2025-06-17T19:12:10Z |
---
license: cc-by-4.0
datasets:
- mprpic/rh-vex-data
base_model:
- mistralai/Mistral-7B-v0.3
---
|
bruhzair/prototype-0.4x155
|
bruhzair
| 2025-06-17T19:08:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-17T18:45:52Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x155
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using /workspace/prototype-0.4x153 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--TheDrummer--Anubis-70B-v1/snapshots/e50d699bf6c21afcf4dbd9a8b4f73511b0366efb
* /workspace/cache/models--Sao10K--L3.1-70B-Hanami-x1/snapshots/f054d970fe9119d0237ce97029e6f5b9fce630eb
* /workspace/cache/models--Sao10K--Llama-3.3-70B-Vulpecula-r1/snapshots/12d7254ab9a5ce21905f59f341a3d2a2b3e62fd5
* /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--Sao10K--L3.1-70B-Hanami-x1/snapshots/f054d970fe9119d0237ce97029e6f5b9fce630eb
- model: /workspace/cache/models--Sao10K--Llama-3.3-70B-Vulpecula-r1/snapshots/12d7254ab9a5ce21905f59f341a3d2a2b3e62fd5
- model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- model: /workspace/cache/models--TheDrummer--Anubis-70B-v1/snapshots/e50d699bf6c21afcf4dbd9a8b4f73511b0366efb
- model: /workspace/prototype-0.4x153
base_model: /workspace/prototype-0.4x153
select_topk: 0.15
merge_method: sce
tokenizer:
source: base
pad_to_multiple_of: 8
int8_mask: true
dtype: bfloat16
```
|
Nevidu/LexBartLo_1
|
Nevidu
| 2025-06-17T19:07:07Z | 25,973 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:2503.10354",
"base_model:facebook/bart-large",
"base_model:adapter:facebook/bart-large",
"region:us"
] | null | 2025-06-08T07:29:25Z |
---
library_name: peft
base_model: facebook/bart-large
---
# 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. -->
- **Paper:** The model was published in "A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization" available in https://arxiv.org/abs/2503.10354 or https://ieeexplore.ieee.org/document/11030964
- **Developed by:** Nevidu Jayatilleke and Ruvan Weerasinghe
<!-- - **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed] -->
<!-- - **Model type:** [More Information Needed] -->
- **Supported Language:** English
- **Finetuned Domain:** Textile Patent Documents from BigPatent Dataset
<!-- - **License:** [More Information Needed] -->
- **Finetuned from model:** facebook/bart-large
- **Link to the Generalised Model:** https://huggingface.co/Nevidu/LexBartLo_2
<!-- ### Model Sources -->
<!-- Provide the basic links for the model. -->
<!-- - **Repository:** [More Information Needed] -->
## How to use the model
<!-- 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. -->
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import nltk
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from nltk.cluster.util import cosine_distance
import numpy as np
import networkx as nx
import pandas as pd
def preprocess_text(text):
sentences = sent_tokenize(text)
tokenized_sentences = [word_tokenize(sentence.lower()) for sentence in sentences]
return tokenized_sentences
def sentence_similarity(sentence1, sentence2):
stop_words = set(stopwords.words('english'))
filtered_sentence1 = [w for w in sentence1 if w not in stop_words]
filtered_sentence2 = [w for w in sentence2 if w not in stop_words]
all_words = list(set(filtered_sentence1 + filtered_sentence2))
vector1 = [filtered_sentence1.count(word) for word in all_words]
vector2 = [filtered_sentence2.count(word) for word in all_words]
return 1 - cosine_distance(vector1, vector2)
def build_similarity_matrix(sentences):
similarity_matrix = np.zeros((len(sentences), len(sentences)))
for i in range(len(sentences)):
for j in range(len(sentences)):
if i != j:
similarity_matrix[i][j] = sentence_similarity(sentences[i], sentences[j])
return similarity_matrix
def apply_lexrank(similarity_matrix, damping=0.85, threshold=0.2, max_iter=100):
nx_graph = nx.from_numpy_array(similarity_matrix)
scores = nx.pagerank(nx_graph, alpha=damping, tol=threshold, max_iter=max_iter)
return scores
def get_top_sentences(sentences, scores):
ranked_sentences = sorted(((scores[i], sentence) for i, sentence in enumerate(sentences)), reverse=True)
top_sentences = [sentence for score, sentence in ranked_sentences]
return top_sentences
def extract_important_sentences(text):
preprocessed_sentences = preprocess_text(text)
similarity_matrix = build_similarity_matrix(preprocessed_sentences)
scores = apply_lexrank(similarity_matrix)
top_sentences = get_top_sentences(preprocessed_sentences, scores)
paragraph = ' '.join([' '.join(sentence) for sentence in top_sentences])
return paragraph
def summarize(text, max_tokens):
peft_model = "Nevidu/LexBartLo_1"
config = PeftConfig.from_pretrained(peft_model)
# load base LLM model and tokenizer
model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path)
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model)
sorted_text = extract_important_sentences(text)
input_ids = tokenizer(sorted_text, return_tensors="pt", truncation=True).input_ids
# with torch.inference_mode():
outputs = model.generate(input_ids=input_ids, max_new_tokens=max_tokens, do_sample=True, top_p=0.9)
summary = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
return summary
text = """ Add your textile patent text"""
max_tokens = 256
summary = summarize(text, max_tokens)
```
## Citation
```json
@inproceedings{jayatilleke2025hybrid,
title={A Hybrid Architecture with Efficient Fine Tuning for Abstractive Patent Document Summarization},
author={Jayatilleke, Nevidu and Weerasinghe, Ruvan},
booktitle={2025 International Research Conference on Smart Computing and Systems Engineering (SCSE)},
pages={1--6},
year={2025},
organization={IEEE}
}
```
### Framework versions
- PEFT 0.9.0
|
morturr/Llama-2-7b-hf-LOO_dadjokes-COMB_amazon-comb1-seed28-2025-06-17
|
morturr
| 2025-06-17T19:06:19Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T19:06:08Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_dadjokes-COMB_amazon-comb1-seed28-2025-06-17
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-2-7b-hf-LOO_dadjokes-COMB_amazon-comb1-seed28-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed28-2025-06-17
|
morturr
| 2025-06-17T19:03:29Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T19:03:19Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed28-2025-06-17
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-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed28-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
toqeerehsan/multilabel-indicator-classification-longformer
|
toqeerehsan
| 2025-06-17T19:03:01Z | 0 | 0 | null |
[
"safetensors",
"longformer",
"lonformer",
"multilabel-classification",
"policy-analysis",
"huggingface",
"en",
"dataset:custom",
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T18:51:17Z |
---
language: en
tags:
- lonformer
- multilabel-classification
- policy-analysis
- huggingface
datasets:
- custom
license: apache-2.0
---
# Longformer for Multi-label Classification of Policy Instruments
This model fine-tunes `lonformer-base` for multilabel classification of policies, targets, and themes.
## Model Details
- Base model: lonformer-base
- Max length: 1024
- Output: 67 multilabel classes (PI - Policy Instrument, TG - Target Group, TH - Theme). There are three main classes that have further sub-categories in them.
- Threshold: 0.25
## Intended Use
Classify policy documents descriptions into thematic categories.
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
import joblib
import requests
model_path = "toqeerehsan/multilabel-indicator-classification-longformer"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
mlb_url = "https://huggingface.co/toqeerehsan/multilabel-indicator-classification-longformer/resolve/main/mlb.pkl"
mlb_path = "mlb.pkl"
with open(mlb_path, "wb") as f:
f.write(requests.get(mlb_url).content)
mlb = joblib.load(mlb_path)
text = "This program supports clean technology and sustainable development in industries."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=1024)
model.eval()
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).squeeze().numpy()
# Threshold
binary_preds = (probs > 0.25).astype(int)
predicted_labels = [label for i, label in enumerate(mlb.classes_) if binary_preds[i] == 1]
print("Predicted Labels:", predicted_labels)
# Predicted Labels: ['TG20', 'TG21', 'TG22', 'TG25', 'TG29', 'TG9']
|
kelle1ds/example-model
|
kelle1ds
| 2025-06-17T19:02:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-17T10:59:22Z |
Example Model
---
license: mit
---
|
songhieng/roberta-phishing-content-detector-2.0
|
songhieng
| 2025-06-17T19:02:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-17T19:01:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
toqeerehsan/multilabel-indicator-classification-roberta-l
|
toqeerehsan
| 2025-06-17T19:02:23Z | 0 | 0 | null |
[
"safetensors",
"roberta",
"multilabel-classification",
"policy-analysis",
"huggingface",
"en",
"dataset:custom",
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T17:21:51Z |
---
language: en
tags:
- roberta
- multilabel-classification
- policy-analysis
- huggingface
datasets:
- custom
license: apache-2.0
---
# RoBERTa for Multi-label Classification of Policy Instruments
This model fine-tunes `roberta-large` for multilabel classification of policies, targets, and themes.
## Model Details
- Base model: roberta-large
- Max length: 512
- Output: 67 multilabel classes (PI - Policy Instrument, TG - Target Group, TH - Theme). There are three main classes that have further sub-categories in them.
- Threshold: 0.25
## Intended Use
Classify policy documents or government program descriptions into thematic categories.
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
import joblib
import requests
model_path = "toqeerehsan/multilabel-indicator-classification-roberta-l"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
mlb_url = "https://huggingface.co/toqeerehsan/multilabel-indicator-classification-roberta-l/resolve/main/mlb.pkl"
mlb_path = "mlb.pkl"
with open(mlb_path, "wb") as f:
f.write(requests.get(mlb_url).content)
mlb = joblib.load(mlb_path)
text = "This program supports clean technology and sustainable development in industries."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
model.eval()
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).squeeze().numpy()
# Threshold
binary_preds = (probs > 0.25).astype(int)
predicted_labels = [label for i, label in enumerate(mlb.classes_) if binary_preds[i] == 1]
print("Predicted Labels:", predicted_labels)
# Predicted Labels: ['PI008', 'TG20', 'TG21', 'TG22', 'TG25', 'TG29', 'TH31', 'TH92']
|
toqeerehsan/multilabel-indicator-classification
|
toqeerehsan
| 2025-06-17T19:01:27Z | 0 | 0 | null |
[
"safetensors",
"roberta",
"multilabel-classification",
"policy-analysis",
"huggingface",
"en",
"dataset:custom",
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T13:23:59Z |
---
language: en
tags:
- roberta
- multilabel-classification
- policy-analysis
- huggingface
datasets:
- custom
license: apache-2.0
---
# RoBERTa for Multi-label Classification of Policy Instruments
This model fine-tunes `roberta-base` for multilabel classification of policies, targets, and themes.
## Model Details
- Base model: roberta-base
- Max length: 512
- Output: 67 multilabel classes (PI - Policy Instrument, TG - Target Group, TH - Theme). There are three main classes that have further sub-categories in them.
- Threshold: 0.25
## Intended Use
Classify policy documents descriptions into thematic categories.
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
import joblib
import requests
model_path = "toqeerehsan/multilabel-indicator-classification"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
mlb_url = "https://huggingface.co/toqeerehsan/multilabel-indicator-classification/resolve/main/mlb.pkl"
mlb_path = "mlb.pkl"
with open(mlb_path, "wb") as f:
f.write(requests.get(mlb_url).content)
mlb = joblib.load(mlb_path)
text = "This program supports clean technology and sustainable development in industries."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
model.eval()
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).squeeze().numpy()
# Threshold
binary_preds = (probs > 0.25).astype(int)
predicted_labels = [label for i, label in enumerate(mlb.classes_) if binary_preds[i] == 1]
print("Predicted Labels:", predicted_labels)
# Predicted Labels: ['PI007', 'PI008', 'TG20', 'TG21', 'TG22', 'TG25', 'TG29', 'TG31', 'TH31']
|
elmehdiessalehy/flan-t5-qlora-learningq-qg
|
elmehdiessalehy
| 2025-06-17T18:59:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-17T18:59:08Z |
---
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 ๐ค 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]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
Enderchef/ICONN-e1
|
Enderchef
| 2025-06-17T18:55:07Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-17T18:55:06Z |
---
license: other
license_name: iconn
license_link: LICENSE
---
|
amentaphd/albert
|
amentaphd
| 2025-06-17T18:54:29Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"albert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:10",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:nreimers/albert-small-v2",
"base_model:finetune:nreimers/albert-small-v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-17T18:54:19Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:10
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: nreimers/albert-small-v2
widget:
- source_sentence: What processes are used to separate the raw liquid mix from natural
gas in a gas recycling plant?
sentences:
- '2016 โบM43 (*1) โ 21 September 2017 โบM43 (*2) โ [โผM28](./../../../legal-content/EN/AUTO/?uri=celex:32014R0895
"32014R0895: INSERTED") 23. Formaldehyde, oligomeric reaction products with aniline
(technical MDA) EC No: 500-036-1 CAS No: 25214-70-4 Carcinogenic (category 1B)
22 February 2016 โบM43 (*1) โ 22 August 2017 โบM43 (*2) โ โ 24. Arsenic acid EC
No: 231-901-9 CAS No: 7778-39-4 Carcinogenic (category 1A) 22 February 2016 22
August 2017 โ 25. Bis(2-methoxyethyl) ether (diglyme) EC No: 203-924-4 CAS No:
111-96-6 Toxic for reproduction (category 1B) 22 February 2016 โบM43 (*1) โ 22
August 2017 โบM43 (*2) โ โ 26. 1,2-dichloroethane (EDC) EC No: 203-458-1 CAS No:
107-06-2 Carcinogenic (category 1B) 22 May 2016 22 November 2017 โ 27.'
- '1. Member States shall ensure that their competent authorities establish at least
one AI regulatory sandbox at national level, which shall be operational by 2 August
2026. That sandbox may also be established jointly with the competent authorities
of other Member States. The Commission may provide technical support, advice and
tools for the establishment and operation of AI regulatory sandboxes.
The obligation under the first subparagraph may also be fulfilled by participating
in an existing sandbox in so far as that participation provides an equivalent
level of national coverage for the participating Member States.'
- and that boils in a range of approximately 149 ยฐC to 205 ยฐC.) 649-345-00-4 232-489-3
8052-41-3 P Natural gas condensates (petroleum); Low boiling point naphtha โ unspecified
(A complex combination of hydrocarbons separated as a liquid from natural gas
in a surface separator by retrograde condensation. It consists mainly of hydrocarbons
having carbon numbers predominantly in the range of C2 to C20. It is a liquid
at atmospheric temperature and pressure.) 649-346-00-X 265-047-3 64741-47-5 P
Natural gas (petroleum), raw liquid mix; Low boiling point naphtha โ unspecified
(A complex combination of hydrocarbons separated as a liquid from natural gas
in a gas recycling plant by processes such as refrigeration or absorption. It
consists mainly of
- source_sentence: What should the report on income tax information include as per
Article 48c?
sentences:
- '(d)
seal any business premises and books or records for the period of time of, and
to the extent necessary for, the inspection.
3.
The undertaking or association of undertakings shall submit to inspections ordered
by decision of the Commission. The officials and other accompanying persons authorised
by the Commission to conduct an inspection shall exercise their powers upon production
of a Commission decision:
(a)
specifying the subject matter and purpose of the inspection;
(b)
containing a statement that, pursuant to Article 16, a lack of cooperation allows
the Commission to take a decision on the basis of the facts that are available
to it;
(c)'
- 'By way of derogation from Article 10c, the Member States concerned may only give
transitional free allocation to installations in accordance with that Article
for investments carried out until 31 December 2024. Any allowances available to
the Member States concerned in accordance with Article 10c for the period from
2021 to 2030 that are not used for such investments shall, in the proportion determined
by the respective Member State:
(a)
be added to the total quantity of allowances that the Member State concerned is
to auction pursuant to Article 10(2); or
(b)'
- '7.
Member States shall require subsidiary undertakings or branches not subject to
the provisions of paragraphs 4 and 5 of this Article to publish and make accessible
a report on income tax information where such subsidiary undertakings or branches
serve no other objective than to circumvent the reporting requirements set out
in this Chapter.
Article 48c
Content of the report on income tax information
1.
The report on income tax information required under Article 48b shall include
information relating to all the activities of the standalone undertaking or ultimate
parent undertaking, including those of all affiliated undertakings consolidated
in the financial statements in respect of the relevant financial year.
2.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on nreimers/albert-small-v2
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.7
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.23333333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7675917633552429
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7300000000000001
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.739090909090909
name: Cosine Map@100
---
# SentenceTransformer based on nreimers/albert-small-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nreimers/albert-small-v2](https://huggingface.co/nreimers/albert-small-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [nreimers/albert-small-v2](https://huggingface.co/nreimers/albert-small-v2) <!-- at revision 18045fa83de53fd7d4548fdc2473862914cbc7d5 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: AlbertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the ๐ค Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'What should the report on income tax information include as per Article 48c?',
'7.\n\nMember States shall require subsidiary undertakings or branches not subject to the provisions of paragraphs 4 and 5 of this Article to publish and make accessible a report on income tax information where such subsidiary undertakings or branches serve no other objective than to circumvent the reporting requirements set out in this Chapter.\n\nArticle 48c\n\nContent of the report on income tax information\n\n1.\n\nThe report on income tax information required under Article 48b shall include information relating to all the activities of the standalone undertaking or ultimate parent undertaking, including those of all affiliated undertakings consolidated in the financial statements in respect of the relevant financial year.\n\n2.',
'(d)\n\nseal any business premises and books or records for the period of time of, and to the extent necessary for, the inspection.\n\n3.\n\nThe undertaking or association of undertakings shall submit to inspections ordered by decision of the Commission. The officials and other accompanying persons authorised by the Commission to conduct an inspection shall exercise their powers upon production of a Commission decision:\n\n(a)\n\nspecifying the subject matter and purpose of the inspection;\n\n(b)\n\ncontaining a statement that, pursuant to Article 16, a lack of cooperation allows the Commission to take a decision on the basis of the facts that are available to it;\n\n(c)',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.7 |
| cosine_accuracy@3 | 0.7 |
| cosine_accuracy@5 | 0.8 |
| cosine_accuracy@10 | 0.9 |
| cosine_precision@1 | 0.7 |
| cosine_precision@3 | 0.2333 |
| cosine_precision@5 | 0.16 |
| cosine_precision@10 | 0.09 |
| cosine_recall@1 | 0.7 |
| cosine_recall@3 | 0.7 |
| cosine_recall@5 | 0.8 |
| cosine_recall@10 | 0.9 |
| **cosine_ndcg@10** | **0.7676** |
| cosine_mrr@10 | 0.73 |
| cosine_map@100 | 0.7391 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 10 training samples
* Columns: <code>query_text</code> and <code>doc_text</code>
* Approximate statistics based on the first 10 samples:
| | query_text | doc_text |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 17 tokens</li><li>mean: 38.6 tokens</li><li>max: 89 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 237.7 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query_text | doc_text |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What are the requirements for Member States regarding the establishment of AI regulatory sandboxes, including the timeline for operational readiness and the possibility of joint establishment with other Member States?</code> | <code>1. Member States shall ensure that their competent authorities establish at least one AI regulatory sandbox at national level, which shall be operational by 2 August 2026. That sandbox may also be established jointly with the competent authorities of other Member States. The Commission may provide technical support, advice and tools for the establishment and operation of AI regulatory sandboxes.<br><br>The obligation under the first subparagraph may also be fulfilled by participating in an existing sandbox in so far as that participation provides an equivalent level of national coverage for the participating Member States.</code> |
| <code>Member States must provide updates on their national energy and climate strategies, detailing the anticipated energy savings from 2021 to 2030. They are also obligated to report on the necessary energy savings and the policies intended to achieve these goals. If assessments reveal that a Member State's measures are inadequate to meet energy savings targets, the Commission may issue recommendations for improvement. Additionally, any shortfall in energy savings must be addressed in subsequent obligation periods.</code> | <code>9. Member States shall apply and calculate the effect of the options chosen under paragraph 8 for the period referred to in paragraph 1, first subparagraph, points (a) and (b)(i), separately:<br><br>(a) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (a), Member States may make use of the options listed in paragraph 8, points (a) to (d). All the options chosen under paragraph 8 taken together shall amount to no more than 25 % of the amount of energy savings referred to in paragraph 1, first subparagraph, point (a); (b) for the calculation of the amount of energy savings required for the obligation period referred to in paragraph 1, first subparagraph, point (b)(i), Member States may make use of the options listed in paragraph 8, points (b) to (g), provided that the individual actions referred to in paragraph 8, point (d), continue to have a verifiable and measurable impact after 31 December 2020. All...</code> |
| <code>What is the functional definition of a remote biometric identification system, and how does it operate in terms of identifying individuals without their active participation?</code> | <code>(17) The notion of โremote biometric identification systemโ referred to in this Regulation should be defined functionally, as an AI system intended for the identification of natural persons without their active involvement, typically at a distance, through the comparison of a personโs biometric data with the biometric data contained in a reference database, irrespectively of the particular technology, processes or types of biometric data used. Such remote biometric identification systems are typically used to perceive multiple persons or their behaviour simultaneously in order to facilitate significantly the identification of natural persons without their active involvement. This excludes AI systems intended to be used for biometric</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 7
- `per_device_eval_batch_size`: 7
- `learning_rate`: 2e-05
- `num_train_epochs`: 14
- `warmup_ratio`: 0.1
- `fp16`: True
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 7
- `per_device_eval_batch_size`: 7
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 14
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | cosine_ndcg@10 |
|:-----:|:----:|:--------------:|
| -1 | -1 | 0.7676 |
### Framework Versions
- Python: 3.11.10
- Sentence Transformers: 4.0.2
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 0.26.0
- Datasets: 3.1.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
CezarinniMedia/MacanRogerio
|
CezarinniMedia
| 2025-06-17T18:53:08Z | 0 | 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
| 2025-06-17T18:27:08Z |
---
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: MacanRogerio
---
# Macanrogerio
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `MacanRogerio` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MacanRogerio",
"lora_weights": "https://huggingface.co/CezarinniMedia/MacanRogerio/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ 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('CezarinniMedia/MacanRogerio', weight_name='lora.safetensors')
image = pipeline('MacanRogerio').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)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/CezarinniMedia/MacanRogerio/discussions) to add images that show off what youโve made with this LoRA.
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed28-2025-06-17
|
morturr
| 2025-06-17T18:48:53Z | 0 | 0 |
peft
|
[
"peft",
"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 | 2025-06-17T18:48:36Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed28-2025-06-17
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-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed28-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
dhruva89/placer
|
dhruva89
| 2025-06-17T18:48:23Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T18:46:49Z |
---
license: apache-2.0
---
|
Entj/Entjlama-3.1-8B-int4-nf4
|
Entj
| 2025-06-17T18:44:26Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T18:44:23Z |
---
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 ๐ค 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]
|
yniiiiii/kobert-tf-model-1
|
yniiiiii
| 2025-06-17T18:39:47Z | 19 | 0 | null |
[
"pytorch",
"custom",
"text-classification",
"region:us"
] |
text-classification
| 2025-06-12T13:04:15Z |
---
pipeline_tag: text-classification
---
|
ulab-ai/Router-R1-Qwen2.5-3B-Instruct-Alpha0.9
|
ulab-ai
| 2025-06-17T18:36:56Z | 0 | 0 | null |
[
"safetensors",
"qwen2",
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T18:00:08Z |
---
license: apache-2.0
---
|
Integramma/model_2c940819-a607-4f18-8240-f9e9578c1e23
|
Integramma
| 2025-06-17T18:35:42Z | 0 | 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
| 2025-06-17T18:35:41Z |
---
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: user_788440702
---
# Model_2C940819 A607 4F18 8240 F9E9578C1E23
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `user_788440702` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "user_788440702",
"lora_weights": "https://huggingface.co/Integramma/model_2c940819-a607-4f18-8240-f9e9578c1e23/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ 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('Integramma/model_2c940819-a607-4f18-8240-f9e9578c1e23', weight_name='lora.safetensors')
image = pipeline('user_788440702').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)
## Training details
- Steps: 100
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Integramma/model_2c940819-a607-4f18-8240-f9e9578c1e23/discussions) to add images that show off what youโve made with this LoRA.
|
eprasad/distilled-t5-small
|
eprasad
| 2025-06-17T18:35:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-classification",
"autotrain",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-17T18:32:21Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.01294114999473095
f1_macro: 0.9911675113805504
f1_micro: 0.9939879759519038
f1_weighted: 0.9939977082681383
precision_macro: 0.9867234959827553
precision_micro: 0.9939879759519038
precision_weighted: 0.9940423542558368
recall_macro: 0.9957796643247464
recall_micro: 0.9939879759519038
recall_weighted: 0.9939879759519038
accuracy: 0.9939879759519038
|
Official-Katrina-Lim-Kiffy-Viral-videos/Original.Full.Clip.Katrina.lim.Viral.Video.Leaks.Tutorial
|
Official-Katrina-Lim-Kiffy-Viral-videos
| 2025-06-17T18:29:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-17T18:28:44Z |
18 seconds ago
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Katrina LimKitrina Lim is a Southeast Asian influencer whose alleged involvement in a leaked video caused her name to trend across social media
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Arabella Stanton's life is about to change forever, and the world of Harry Potter is ready to welcome her with open arms.
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<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Video/?video"><img border="Viral+Leaked+Video" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
|
OPTML-Group/GradDiff-WMDP
|
OPTML-Group
| 2025-06-17T18:21:44Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"unlearn",
"machine-unlearning",
"llm-unlearning",
"data-privacy",
"large-language-models",
"trustworthy-ai",
"trustworthy-machine-learning",
"language-model",
"conversational",
"en",
"dataset:cais/wmdp",
"arxiv:2502.05374",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:finetune:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-02-09T23:52:46Z |
---
license: mit
datasets:
- cais/wmdp
language:
- en
base_model:
- HuggingFaceH4/zephyr-7b-beta
pipeline_tag: text-generation
library_name: transformers
tags:
- unlearn
- machine-unlearning
- llm-unlearning
- data-privacy
- large-language-models
- trustworthy-ai
- trustworthy-machine-learning
- language-model
---
# GradDiff-Unlearned Model on Task "WMDP"
## Model Details
- **Unlearning**:
- **Task**: [๐คdatasets/cais/wmdp wmdp-bio](https://huggingface.co/datasets/cais/wmdp)
- **Method**: GradDiff
- **Smoothness Optimization**: None
- **Origin Model**: [๐คHuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
- **Code Base**: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth)
- **Research Paper**: ["Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond"](https://arxiv.org/abs/2502.05374)
## Loading the Model
```python
import torch
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OPTML-Group/GradDiff-WMDP", torch_dtype=torch.bfloat16, trust_remote_code=True)
```
## Citation
If you use this model in your research, please cite:
```
@article{fan2025towards,
title={Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond},
author={Fan, Chongyu and Jia, Jinghan and Zhang, Yihua and Ramakrishna, Anil and Hong, Mingyi and Liu, Sijia},
journal={arXiv preprint arXiv:2502.05374},
year={2025}
}
```
## Reporting Issues
Reporting issues with the model: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth)
|
ulab-ai/Router-R1-Llama-3.2-3B-Instruct-Alpha0.9
|
ulab-ai
| 2025-06-17T18:20:45Z | 0 | 0 | null |
[
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T17:59:38Z |
---
license: apache-2.0
---
|
OPTML-Group/NPO-SAM-WMDP
|
OPTML-Group
| 2025-06-17T18:20:26Z | 21 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"unlearn",
"machine-unlearning",
"llm-unlearning",
"data-privacy",
"large-language-models",
"trustworthy-ai",
"trustworthy-machine-learning",
"language-model",
"conversational",
"en",
"dataset:cais/wmdp",
"arxiv:2502.05374",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:finetune:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-27T15:09:30Z |
---
license: mit
datasets:
- cais/wmdp
language:
- en
base_model:
- HuggingFaceH4/zephyr-7b-beta
pipeline_tag: text-generation
library_name: transformers
tags:
- unlearn
- machine-unlearning
- llm-unlearning
- data-privacy
- large-language-models
- trustworthy-ai
- trustworthy-machine-learning
- language-model
---
# NPO-Unlearned w/ SAM Model on Task "WMDP"
## Model Details
- **Unlearning**:
- **Task**: [๐คdatasets/cais/wmdp wmdp-bio](https://huggingface.co/datasets/cais/wmdp)
- **Method**: NPO
- **Smoothness Optimization**: Sharpness-aware Minimization (SAM)
- **Origin Model**: [๐คHuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
- **Code Base**: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth)
- **Research Paper**: ["Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond"](https://arxiv.org/abs/2502.05374)
## Loading the Model
```python
import torch
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OPTML-Group/NPO-SAM-WMDP", torch_dtype=torch.bfloat16, trust_remote_code=True)
```
## Citation
If you use this model in your research, please cite:
```
@article{fan2025towards,
title={Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond},
author={Fan, Chongyu and Jia, Jinghan and Zhang, Yihua and Ramakrishna, Anil and Hong, Mingyi and Liu, Sijia},
journal={arXiv preprint arXiv:2502.05374},
year={2025}
}
```
## Reporting Issues
Reporting issues with the model: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth)
|
vuitton/21v1scrip_29
|
vuitton
| 2025-06-17T18:11:11Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-06-16T15:34:40Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
vuitton/21v1scrip_28
|
vuitton
| 2025-06-17T18:10:59Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-06-16T15:34:35Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Triangle104/QwQ-32B-abliterated-Q5_K_S-GGUF
|
Triangle104
| 2025-06-17T18:10:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:huihui-ai/QwQ-32B-abliterated",
"base_model:quantized:huihui-ai/QwQ-32B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-17T18:08:28Z |
---
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/QwQ-32B-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: huihui-ai/QwQ-32B-abliterated
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# Triangle104/QwQ-32B-abliterated-Q5_K_S-GGUF
This model was converted to GGUF format from [`huihui-ai/QwQ-32B-abliterated`](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/QwQ-32B-abliterated-Q5_K_S-GGUF --hf-file qwq-32b-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-32B-abliterated-Q5_K_S-GGUF --hf-file qwq-32b-abliterated-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/QwQ-32B-abliterated-Q5_K_S-GGUF --hf-file qwq-32b-abliterated-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/QwQ-32B-abliterated-Q5_K_S-GGUF --hf-file qwq-32b-abliterated-q5_k_s.gguf -c 2048
```
|
artianand/race_ethnicity_adapter_roberta_large_race_custom_loss_lamda_07_batch_8
|
artianand
| 2025-06-17T18:08:09Z | 0 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-17T18:08:03Z |
---
tags:
- adapter-transformers
- roberta
---
# Adapter `artianand/race_ethnicity_adapter_roberta_large_race_custom_loss_lamda_07_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/race_ethnicity_adapter_roberta_large_race_custom_loss_lamda_07_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
Official-mezzo-fun-18-video-Viral/FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official
|
Official-mezzo-fun-18-video-Viral
| 2025-06-17T18:08:00Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-17T18:05:44Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Monike123/dslm-finetuned
|
Monike123
| 2025-06-17T18:05:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:deepseek-ai/deepseek-coder-6.7b-instruct",
"base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct",
"region:us"
] | null | 2025-06-17T18:00:06Z |
---
base_model: deepseek-ai/deepseek-coder-6.7b-instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [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]
### Framework versions
- PEFT 0.15.2
|
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