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tylerachang/bigram-subnetworks-gpt2-small | tylerachang | 2025-05-27T19:03:04Z | 0 | 0 | null | [
"eng",
"arxiv:2504.15471",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-21T04:52:54Z |
---
license: apache-2.0
language:
- eng
---
# bigram-subnetworks-gpt2-small
We release bigram subnetworks as described in [Chang and Bergen (2025)](https://arxiv.org/abs/2504.15471).
These are sparse subsets of model parameters that recreate bigram predictions (next token predictions conditioned only on the current token) in Transformer language models.
This repository contains the bigram subnetwork for [openai-community/gpt2](https://huggingface.co/openai-community/gpt2).
## Format
A subnetwork file is a pickled Python dictionary that maps the original model parameter names to numpy binary masks with the same shapes as the original model parameters (1: keep, 0: drop).
For details on usage, see: https://github.com/tylerachang/bigram-subnetworks.
For details on how these subnetworks were trained, see [Chang and Bergen (2025)](https://arxiv.org/abs/2504.15471).
For minimal usage, download the code at https://github.com/tylerachang/bigram-subnetworks (or just the file `circuit_loading_utils.py`) and run in Python:
```
from circuit_loading_utils import load_bigram_subnetwork_dict, load_subnetwork_model
mask_dict = load_bigram_subnetwork_dict('openai-community/gpt2')
model, tokenizer, config = load_subnetwork_model('openai-community/gpt2', mask_dict)
```
## Citation
<pre>
@article{chang-bergen-2025-bigram,
title={Bigram Subnetworks: Mapping to Next Tokens in Transformer Language Models},
author={Chang, Tyler A. and Bergen, Benjamin K.},
journal={Preprint},
year={2025},
url={https://arxiv.org/abs/2504.15471},
}
</pre>
|
shaojintian/llaca-0.5B | shaojintian | 2025-05-27T19:03:03Z | 0 | 0 | null | [
"safetensors",
"ComplexFormer",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-27T19:00:25Z | ---
license: apache-2.0
---
|
Kamikaze-88/Wan2.1-VACE-14B-fp8 | Kamikaze-88 | 2025-05-27T19:02:14Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T22:07:57Z | ---
{}
---
An fp8 version (e4m3fn) of Wan2.1 Vace 14B converted from the repackaged version from Comfy-Org
UPDATE:
Added fp8 e5m2 version converted from original 58.9GB model
Credits:
Original Vace from Wan-AI:
https://huggingface.co/Wan-AI/Wan2.1-VACE-14B
Repackaged fp16 from Comfy-Org:
https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged
Comfyui node used to convert to fp8:
https://github.com/Shiba-2-shiba/ComfyUI_DiffusionModel_fp8_converter
e5m2 conversion scripts from:
https://huggingface.co/phazei
|
HPLT/hplt2c_eng90-edu_fra10_checkpoints | HPLT | 2025-05-27T18:57:59Z | 0 | 0 | null | [
"pytorch",
"llama",
"HPLT",
"decoder",
"en",
"dataset:HPLT/HPLT2.0_cleaned",
"arxiv:2503.10267",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-26T08:49:52Z | ---
language:
- en
tags:
- HPLT
- decoder
license: apache-2.0
datasets:
- HPLT/HPLT2.0_cleaned
---
# HPLT v2.0 - Cleaned - English (90%)
<img src="https://hplt-project.org/_next/static/media/logo-hplt.d5e16ca5.svg" width=12.5%>
This is one of the decoder-only language models trained on [HPLT2.0_cleaned](https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned).
All the HPLT decoder-only models use the same hyper-parameters, roughly following the llama architecture with 2.15B parameters in total:
- hidden size: 2048
- attention heads: 32
- layers: 24
- sequence length: 2048
## Intermediate checkpoints
We are releasing intermediate checkpoints for each model at intervals of every 1000 training steps in separate branches. The naming convention is `checkpoint_00xxxx00`: for example, `checkpoint_0005000`. The checkpoints range from checkpoint_0001000 to checkpoint_0047684 and the latter is in the main branch.
## Cite us
```bibtex
@misc{burchell2025expandedmassivemultilingualdataset,
title={An Expanded Massive Multilingual Dataset for High-Performance Language Technologies},
author={Laurie Burchell and Ona de Gibert and Nikolay Arefyev and Mikko Aulamo and Marta Bañón and Pinzhen Chen and Mariia Fedorova and Liane Guillou and Barry Haddow and Jan Hajič and Jindřich Helcl and Erik Henriksson and Mateusz Klimaszewski and Ville Komulainen and Andrey Kutuzov and Joona Kytöniemi and Veronika Laippala and Petter Mæhlum and Bhavitvya Malik and Farrokh Mehryary and Vladislav Mikhailov and Nikita Moghe and Amanda Myntti and Dayyán O'Brien and Stephan Oepen and Proyag Pal and Jousia Piha and Sampo Pyysalo and Gema Ramírez-Sánchez and David Samuel and Pavel Stepachev and Jörg Tiedemann and Dušan Variš and Tereza Vojtěchová and Jaume Zaragoza-Bernabeu},
year={2025},
eprint={2503.10267},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.10267},
}
``` |
mscs23021/Whisper_PSeduo_PEFT | mscs23021 | 2025-05-27T18:56:54Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-18T07:55:20Z | ---
license: apache-2.0
---
|
Darkhn/test-GGUF | Darkhn | 2025-05-27T18:56:40Z | 0 | 0 | llama.cpp | [
"llama.cpp",
"gguf",
"q6-k",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-27T18:55:31Z | ---
library_name: llama.cpp
license: mit
tags:
- gguf
- q6-k
---
# test-GGUF
GGUF model files for `test` (original base: `/mnt/test/output/merged_passthrough_20250527_185209_194400`).
This repository contains the following quantization: **Q6_K**.
## Files
- `test-Q6_K.gguf`
Converted and quantized using [llama.cpp](https://github.com/ggerganov/llama.cpp). |
punith0110/sft-tiny-chatbot | punith0110 | 2025-05-27T18:53:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:finetune:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T18:52:31Z | ---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: transformers
model_name: sft-tiny-chatbot
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for sft-tiny-chatbot
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
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="punith0110/sft-tiny-chatbot", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.2
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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}}
}
``` |
mayuri-mishra-viral/mayuri.mishra.viral.video.highway.viral.mayuri.mishra.viral.full.videos | mayuri-mishra-viral | 2025-05-27T18:51:15Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T18:48:03Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=mayuri-mishra)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=mayuri-mishra)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=mayuri-mishra) |
dimasik2987/20405430-db49-4bce-a10d-37a0e37de08b | dimasik2987 | 2025-05-27T18:49:33Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Nous-Capybara-7B-V1.9",
"base_model:quantized:NousResearch/Nous-Capybara-7B-V1.9",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-27T17:25:31Z | ---
base_model: NousResearch/Nous-Capybara-7B-V1.9
library_name: transformers
model_name: 20405430-db49-4bce-a10d-37a0e37de08b
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 20405430-db49-4bce-a10d-37a0e37de08b
This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9).
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="dimasik2987/20405430-db49-4bce-a10d-37a0e37de08b", 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/dedok-yo/s56-7/runs/qgmz9hnx)
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: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
manuross1/nrmmtrfckdfll500 | manuross1 | 2025-05-27T18:48:55Z | 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 | 2025-05-27T18:32:23Z | ---
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: nrmmtrfckdfll500
---
# Nrmmtrfckdfll500
<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 `nrmmtrfckdfll500` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "nrmmtrfckdfll500",
"lora_weights": "https://huggingface.co/manuross1/nrmmtrfckdfll500/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 [🧨 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('manuross1/nrmmtrfckdfll500', weight_name='lora.safetensors')
image = pipeline('nrmmtrfckdfll500').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: 750
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/manuross1/nrmmtrfckdfll500/discussions) to add images that show off what you’ve made with this LoRA.
|
BootesVoid/cmb2k8mnt06hju1cgzdp17h5x_cmb6udx9z06wvlexp497kvqx5 | BootesVoid | 2025-05-27T18:47:47Z | 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 | 2025-05-27T18:47:46Z | ---
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: ZAFIRA
---
# Cmb2K8Mnt06Hju1Cgzdp17H5X_Cmb6Udx9Z06Wvlexp497Kvqx5
<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 `ZAFIRA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ZAFIRA",
"lora_weights": "https://huggingface.co/BootesVoid/cmb2k8mnt06hju1cgzdp17h5x_cmb6udx9z06wvlexp497kvqx5/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 [🧨 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/cmb2k8mnt06hju1cgzdp17h5x_cmb6udx9z06wvlexp497kvqx5', weight_name='lora.safetensors')
image = pipeline('ZAFIRA').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/cmb2k8mnt06hju1cgzdp17h5x_cmb6udx9z06wvlexp497kvqx5/discussions) to add images that show off what you’ve made with this LoRA.
|
beanne-valerie-hd/beanne.scandal.beanne.valerie.dela.cruz.beanne.valerie.dela.cruz.telegram | beanne-valerie-hd | 2025-05-27T18:46:36Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T18:44:33Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=beanne-valerie)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=beanne-valerie)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=beanne-valerie) |
samcomber/a2c-PandaReachDense-v3 | samcomber | 2025-05-27T18:45:46Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-27T18:41:45Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.22 +/- 0.18
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
plumpyfield/natix3 | plumpyfield | 2025-05-27T18:44:44Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2025-05-27T18:44:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
mlx-community/sarvam-m-bf16 | mlx-community | 2025-05-27T18:43:42Z | 3 | 0 | mlx | [
"mlx",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"bn",
"hi",
"kn",
"gu",
"mr",
"ml",
"or",
"pa",
"ta",
"te",
"base_model:sarvamai/sarvam-m",
"base_model:finetune:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-27T00:08:11Z | ---
library_name: mlx
license: apache-2.0
language:
- en
- bn
- hi
- kn
- gu
- mr
- ml
- or
- pa
- ta
- te
base_model: sarvamai/sarvam-m
base_model_relation: finetune
tags:
- mlx
pipeline_tag: text-generation
---
# mlx-community/sarvam-m-bf16
This model [mlx-community/sarvam-m-bf16](https://huggingface.co/mlx-community/sarvam-m-bf16) was
converted to MLX format from [sarvamai/sarvam-m](https://huggingface.co/sarvamai/sarvam-m)
using mlx-lm version **0.24.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/sarvam-m-bf16")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
m-usab/finetuned-en-fr | m-usab | 2025-05-27T18:40:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2025-05-27T18:39:36Z | ---
library_name: transformers
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: finetuned-en-fr
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. -->
# finetuned-en-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 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: 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
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Hsianchengfun/pruned_10_dt_dp_20epoch_1b | Hsianchengfun | 2025-05-27T18:38:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T18:35:08Z | ---
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 🤗 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] |
cs2764/Muyan-TTS-SFT-mlx-8Bit | cs2764 | 2025-05-27T18:35:01Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"base_model:MYZY-AI/Muyan-TTS-SFT",
"base_model:quantized:MYZY-AI/Muyan-TTS-SFT",
"license:apache-2.0",
"8-bit",
"region:us"
]
| null | 2025-05-27T18:34:46Z | ---
license: apache-2.0
base_model: MYZY-AI/Muyan-TTS-SFT
tags:
- mlx
---
# cs2764/Muyan-TTS-SFT-mlx-8Bit
The Model [cs2764/Muyan-TTS-SFT-mlx-8Bit](https://huggingface.co/cs2764/Muyan-TTS-SFT-mlx-8Bit) was converted to MLX format from [MYZY-AI/Muyan-TTS-SFT](https://huggingface.co/MYZY-AI/Muyan-TTS-SFT) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("cs2764/Muyan-TTS-SFT-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
Master-thesis-NAP/ModernBERT-DAPT-Embed-DAPT-Math-v2 | Master-thesis-NAP | 2025-05-27T18:33:57Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"modernbert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:79876",
"loss:MultipleNegativesRankingLoss",
"en",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:Master-thesis-NAP/ModernBert-DAPT-math",
"base_model:finetune:Master-thesis-NAP/ModernBert-DAPT-math",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-27T18:33:20Z | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:79876
- loss:MultipleNegativesRankingLoss
base_model: Master-thesis-NAP/ModernBert-DAPT-math
widget:
- source_sentence: What is the error estimate for the difference between the exact
solution and the local oscillation decomposition (LOD) solution in terms of the
$L_0$ norm?
sentences:
- '\label{thm1}
Suppose $\kappa$ and $\bar a$ are as above. Then $|\Pcut(\bar a)| \leq 2^\kappa$.
Indeed if
$2^\kappa=\aleph_\alpha,$ then $|\Pcut(\bar a)| \leq |\alpha+1|^2$.'
- "\\cite{kyushu}\n For every discrete group $\\G$ and every 2-dimensional representation\
\ $\\varrho$ of $\\G$, $\\varrho-$equivariant functions for $\\G$ always exist."
- "\\label{Corollary}\n Let Assumptions~\\ref{assum_1} and~\\ref{assump2} be\
\ satisfied. Let $u$ be the solution of~\\eqref{WeakForm} and let $u_{H,k}$ be\
\ the LOD solution of~\\eqref{local_probelm }. Then we have \n \\begin{equation}\\\
label{L2Estimate}\n \\|u-I_Hu_{H,k}\\|_0\\lesssim \\|u-I_Hu\\|_0+\\|u-u_{H,k}\\\
|_0 +H|u-u_{H,k}|_1.\n \\end{equation}\n %\\[\\|u-I_Hu_{H,k}\\|_0\\lesssim\
\ H |u|_1 +|u-u_{H,k}|_1.\\]"
- source_sentence: Does the theorem imply that the rate of convergence of the sequence
$T_{m,j}(E)$ to $T_{m+k_n,j+k_n}(E)$ is exponential in the distance between $m$
and $j$, and that this rate is bounded by a constant $C$ times an exponential
decay factor involving the parameter $\gamma$?
sentences:
- "\\label{thm:weibull}\nSuppose random variable $X$ follows Weibull distribution,\
\ and $E(X^i)$ denotes the $i$-th moment of $X$. Then the random variable $X$\
\ satisfy the following inequality: \n\\begin{equation}\\label{eq:moments}\n \
\ E(X^n)^{\\frac{1}{n}} \\geq E(X^m)^{\\frac{1}{m}},\n\\end{equation}\nwhere\
\ $n > m$."
- "\\label{lem1}\n\t\tFor all $m,j\\in\\Z$, we have\n\t\t\\begin{equation*}\n\t\
\t|| T_{m,j} (E)-T_{m+k_n,j+k_n}(E)||\\leq C e^{-\\gamma k_n} e^{(\\mathcal\
\ L(E)+\\varepsilon) |m-j|}. \n\t\t\\end{equation*}"
- If the problem \eqref{eq:Model-based_Program} is convex, then under the primal-dual
dynamics \eqref{eq:PDD}-\eqref{eq:AlgebraicConstruction}, the system \eqref{eq:Input-OutputMap}
asymptotically converges to a steady state that is the optimal solution of \eqref{eq:Model-based_Program}.
- source_sentence: What is the rate of convergence for the total error in the given
problem, assuming the conditions in Theorem~\ref{convergence-rates} are met?
sentences:
- "\\label{convergence-rates}\nUnder the assumptions of Theorem~\\ref{well-posedness}.\
\ Given $(\\bu,{p},\\bzeta,\\varphi)\\in (\\bH^{s_1+1}(\\Omega)\\cap \\bV_1)\\\
times (\\text{H}^{s_1}(\\Omega)\\cap Q_{b_1}) \\times (\\bH^{s_2}\\cap \\bV_2)\
\ \\times (\\text{H}^{s_2}\\cap Q_{b_2})$, $(\\bu_h,{p}_h,\\bzeta_h,\\varphi_h)\\\
in \\bV_1^{h,k_1}\\times Q_1^{h,k_1}\\times \\bV_2^{h,k_2}\\times Q_2^{h,k_2}$\
\ be the respective solutions of the continuous and discrete problems, with the\
\ data satisfying $\\fb\\in \\bH^{s_1-1}\\cap \\bQ_{b_1}$ and $g\\in H^{s_2}(\\\
Omega)\\cap Q_{b_2}$. If $\\overline{C}_1 \\sqrt{M} L_\\ell + \\overline{C}_2^2\
\ \\sqrt{M^3} L_\\bbM\\sqrt{2\\mu} (\\norm{\\varphi_D}_{1/2,\\Gamma_D} + \\\
norm{g}_{0,\\Omega}) < 1/2.$ Then, the total error $\\overline{\\textnormal{e}}_h:=\\\
norm{(\\bu-\\bu_h,{p}-{p}_h, \\bzeta-\\bzeta_h,\\varphi-\\varphi_h)}_{\\bV_1\\\
times Q_{1} \\times \\bV_2\\times Q_2}$ decays with the following rate for $s:=\
\ \\min \\left\\{s_1,s_2\\right\\}$\n \\begin{align*}\\label{convergence-rate}\n\
\ \\overline{\\textnormal{e}}_h &\\lesssim h^{ s} (|\\fb|_{s_1-1,\\bQ_{b_1}}\
\ + |\\bu|_{s_1+1,\\bV_1} + |{p}|_{s_1,Q_{b_1}} + |g|_{s_2,Q_{b_2}} + |\\bzeta|_{s_2,\\\
bV_2}+|\\varphi|_{s_2,Q_{b_2}}).\n \\end{align*}"
- "\\label{thm}\nFor vector linear secure aggregation defined above, the optimal\
\ total key rate is \n\\begin{eqnarray}\n R_{Z_{\\Sigma}}^* %= \\left\\{R_{Z_{\\\
Sigma}}: R_{Z_{\\Sigma}} \\geq \n = \\mbox{rank} \\left( \\left[ \\mathbf{F}\
\ ; \\mathbf{G} \\right] \\right)\n - \\mbox{rank} \\left( \\mathbf{F} \\\
right) = \\mbox{rank}({\\bf G} | {\\bf F}).\n %\\right\\}.\n% \\\\ \\\
mbox{rank}\n\\end{eqnarray}"
- "The process $Y(t)$, $t\\geq 0,$ is called Markov branching process with\r\nnon-homogeneous\
\ Poisson immigration (MBPNPI)."
- source_sentence: Is the local time of the horizontal component of the Peano curve
ever greater than 1?
sentences:
- "[Divergence Theorem or Gauss-Green Theorem for Surfaces in $\\R^3$]\n\t\\label{thm:surface_int}\n\
\t Let $\\Sigma \\subset \\Omega\\subseteq\\R^3$ be a bounded smooth surface.\n\
\t Further, $\\bb a:\\Sigma\\to\\R^3$ is a continuously differentiable\
\ vector field that is either defined on the\n\t\t\t\t\tboundary $\\partial\\\
Sigma$ or has a bounded continuous extension to this boundary.\n\t Like\
\ in \\eqref{eq:decomp} it may be decomposed into tangential and normal components\n\
\t\t\t\t\tas follows $\\bb a = \\bb a^\\shortparallel + a_\\nu\\bs\\nu_\\Sigma$.\
\ By $\\dd l$ we denote the line element on \n\t\t\t\t\tthe curve $\\partial \\\
Sigma$. We assume that the curve is continuous and consists of finitely many\n\
\t\t\t\t\tsmooth pieces.\n\t Then the following divergence formula for\
\ surface integrals holds\n\t %\n\t \\begin{align}\n\t \
\ %\n\t \\int\\limits_\\Sigma \\left[\\nabla_\\Sigma\\cdot\\bb a^\\\
shortparallel\\right](\\x)\\;\\dd S\n\t\t\t\t\t\t\t= \\int\\limits_{\\partial\\\
Sigma} \\left[\\bb a\\cdot\\bs\\nu_{\\partial\\Sigma}\\right](\\x)\\,\\dd l .\n\
\t \\label{eq:surface_div}\n\t %\n\t \\end{align}\n\
\t\t\t\t\t%\n\t\t\t\t\tFrom this we obtain the formula\n\t\t\t\t\t%\n\t \
\ \\begin{align}\n\t %\n\t \\int\\limits_\\Sigma \\left[\\\
nabla_\\Sigma\\cdot\\bb a\\right](\\x)\\;\\dd S\n\t\t\t\t\t\t\t= \\int\\limits_{\\\
partial\\Sigma} \\left[\\bb a\\cdot\\bs\\nu_{\\partial\\Sigma}\\right](\\x)\\\
,\\dd l \n\t\t\t\t\t\t\t-\\int\\limits_\\Sigma\\left[ 2\\kappa_Ma_\\nu\\right](\\\
x)\\;\\dd S.\n\t \\label{eq:surface_div_2}\n\t %\n\t \
\ \\end{align}\n\t %"
- There exists local time of the horizontal component $x$ of the Peano curve. Moreover,
this local time attains values no greater than $1$.
- "[Werner-Young's inequality]\\label{Young op-op}\nSuppose $S\\in \\cS^p$ and $T\\\
in \\cS^q$ with $1+r^{-1}=p^{-1}+q^{-1}$.\nThen $S\\star T\\in L^r(\\R^{2d})$\
\ and\n\\begin{align*}\n \\|S\\star T\\|_{L^{r}}\\leq \\|S\\|_{\\cS^p}\\|T\\\
|_{\\cS^q}.\n\\end{align*}"
- source_sentence: What is the meaning of the identity containment $1_x:x\to x$ in
the context of the bond system?
sentences:
- "\\label{lem:opt_lin}\nConsider the optimization problem\n\\begin{equation}\\\
label{eq:max_tr_lem}\n\\begin{aligned}\n \\max_{\\bs{U}}&\\;\\; \\Re\\{\\mrm{tr}(\\\
bs{U}^\\mrm{H}\\bs{B}) \\}\\\\\n \\mrm{s.t. \\;\\;}& \\bs{U}\\in \\mathcal{U}(N),\n\
\\end{aligned}\n\\end{equation}\nwhere $\\bs{B}$ may be an arbitrary $N\\times\
\ N$ matrix with singular value decomposition (SVD) $\\bs{B}=\\bs{U}_{\\bs{B}}\\\
bs{S}_{\\bs{B}}\\bs{V}_{\\bs{B}}^\\mrm{H}$. The solution to \\eqref{eq:max_tr_lem}\
\ is given by\n\\begin{equation}\\label{eq:sol_max}\n \\bs{U}_\\mrm{opt} =\
\ \\bs{U}_{\\bs{B}}^\\mrm{H}\\bs{V}_{\\bs{B}}.\n\\end{equation}\n\\begin{skproof}\n\
\ A formal proof, which may be included in the extended version, can be obtained\
\ by defining the Riemannian gradient over the unitary group and finding the stationary\
\ point where it vanishes. However, an intuitive argument is that the solution\
\ to \\eqref{eq:max_tr_lem} is obtained by positively combining the singular values\
\ of $\\bs{B}$, leading to \\eqref{eq:sol_max}.\n\\end{skproof}"
- '\label{AM_BA_lem1}
Let $$\Omega =\left\{a={{\left(k_1x_1+k_2,\dots,k_1x_n+k_2\right)}}\mid k_1, k_2\in
\mathbb{R}\right\} .$$ Then ${\displaystyle\underset{a\in \Omega}{\operatorname{argmin}}
{J_{\alpha }}(a)=\overline{a}\ },$ where $\overline{a}=\left(\overline{a}_1,\dots,\overline{a}_n\right)$,
$$\overline{a}_i=\frac{1}{n}\sum^n_{j =1}{y_j},\quad\forall i=1,\dots,n.$$ In
other words, on the class of lines $J_{\alpha }\left(a\right)$ reaches a minimum
on a straight line parallel to the $Ox$ axis. So, this is the average line for
the ordinates of all points of set $X$.'
- "A \\emph{bond system} is a tuple $(B,C,s,t,1,\\cdot)$, where $B$ is a set of\
\ \\emph{bonds}, $C$ is a set of \\emph{content} relations, and $s,t:C\\to B$\
\ are \\emph{source} and \\emph{target} functions. For $c\\in C$ with $s(c)=x$\
\ and $t(c)=y$, we write $x\\xrightarrow{c}y$ or $c:x\\to y$, indicating that\
\ $x$ \\emph{contains} $y$. Each bond $x\\in B$ has an \\emph{identity} containment\
\ $1_x:x\\to x$, meaning every bond trivially contains itself. For $c:x\\to y$\
\ and $c':y\\to z$, their composition is $cc':x\\to z$. These data must satisfy:\n\
\ \\begin{enumerate}\n \\item Identity laws: For each $c:x\\to y$, $1_x\
\ c= c=c1_y$\n \\item Associativity: For $c:x\\to y$, $c':y\\to z$, $c'':z\\\
to w$, $c(c'c'')=(cc')c''$\n \\item Anti-symmetry: For $c:x\\to y$ and\
\ $c':y\\to x$, $x=y$\n \\item Left cancellation: For $c,c':x\\to y$ and\
\ $c'':y\\to z$, if $cc''=c'c''$, then $c=c'$\n \\end{enumerate}"
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: ModernBERT DAPT Embed DAPT Math
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: TESTING
type: TESTING
metrics:
- type: cosine_accuracy@1
value: 0.868020304568528
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9183202584217812
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9325103830179973
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9495846792801107
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.868020304568528
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.6118674050146131
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.49353945546838945
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.34758883248730965
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.04186710795480722
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.08315252408701693
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1073909448198794
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.14207392775097807
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4493273991613623
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8963655316764447
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.16376932233660765
name: Cosine Map@100
---
# ModernBERT DAPT Embed DAPT Math
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Master-thesis-NAP/ModernBert-DAPT-math](https://huggingface.co/Master-thesis-NAP/ModernBert-DAPT-math). 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:** [Master-thesis-NAP/ModernBert-DAPT-math](https://huggingface.co/Master-thesis-NAP/ModernBert-DAPT-math) <!-- at revision a30384f91d764c272e6b740c256d5581325ea4bb -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### 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': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel
(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})
(2): Normalize()
)
```
## 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 🤗 Hub
model = SentenceTransformer("Master-thesis-NAP/ModernBERT-DAPT-Embed-DAPT-Math-v2")
# Run inference
sentences = [
'What is the meaning of the identity containment $1_x:x\\to x$ in the context of the bond system?',
"A \\emph{bond system} is a tuple $(B,C,s,t,1,\\cdot)$, where $B$ is a set of \\emph{bonds}, $C$ is a set of \\emph{content} relations, and $s,t:C\\to B$ are \\emph{source} and \\emph{target} functions. For $c\\in C$ with $s(c)=x$ and $t(c)=y$, we write $x\\xrightarrow{c}y$ or $c:x\\to y$, indicating that $x$ \\emph{contains} $y$. Each bond $x\\in B$ has an \\emph{identity} containment $1_x:x\\to x$, meaning every bond trivially contains itself. For $c:x\\to y$ and $c':y\\to z$, their composition is $cc':x\\to z$. These data must satisfy:\n \\begin{enumerate}\n \\item Identity laws: For each $c:x\\to y$, $1_x c= c=c1_y$\n \\item Associativity: For $c:x\\to y$, $c':y\\to z$, $c'':z\\to w$, $c(c'c'')=(cc')c''$\n \\item Anti-symmetry: For $c:x\\to y$ and $c':y\\to x$, $x=y$\n \\item Left cancellation: For $c,c':x\\to y$ and $c'':y\\to z$, if $cc''=c'c''$, then $c=c'$\n \\end{enumerate}",
'\\label{lem:opt_lin}\nConsider the optimization problem\n\\begin{equation}\\label{eq:max_tr_lem}\n\\begin{aligned}\n \\max_{\\bs{U}}&\\;\\; \\Re\\{\\mrm{tr}(\\bs{U}^\\mrm{H}\\bs{B}) \\}\\\\\n \\mrm{s.t. \\;\\;}& \\bs{U}\\in \\mathcal{U}(N),\n\\end{aligned}\n\\end{equation}\nwhere $\\bs{B}$ may be an arbitrary $N\\times N$ matrix with singular value decomposition (SVD) $\\bs{B}=\\bs{U}_{\\bs{B}}\\bs{S}_{\\bs{B}}\\bs{V}_{\\bs{B}}^\\mrm{H}$. The solution to \\eqref{eq:max_tr_lem} is given by\n\\begin{equation}\\label{eq:sol_max}\n \\bs{U}_\\mrm{opt} = \\bs{U}_{\\bs{B}}^\\mrm{H}\\bs{V}_{\\bs{B}}.\n\\end{equation}\n\\begin{skproof}\n A formal proof, which may be included in the extended version, can be obtained by defining the Riemannian gradient over the unitary group and finding the stationary point where it vanishes. However, an intuitive argument is that the solution to \\eqref{eq:max_tr_lem} is obtained by positively combining the singular values of $\\bs{B}$, leading to \\eqref{eq:sol_max}.\n\\end{skproof}',
]
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
* Dataset: `TESTING`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.868 |
| cosine_accuracy@3 | 0.9183 |
| cosine_accuracy@5 | 0.9325 |
| cosine_accuracy@10 | 0.9496 |
| cosine_precision@1 | 0.868 |
| cosine_precision@3 | 0.6119 |
| cosine_precision@5 | 0.4935 |
| cosine_precision@10 | 0.3476 |
| cosine_recall@1 | 0.0419 |
| cosine_recall@3 | 0.0832 |
| cosine_recall@5 | 0.1074 |
| cosine_recall@10 | 0.1421 |
| **cosine_ndcg@10** | **0.4493** |
| cosine_mrr@10 | 0.8964 |
| cosine_map@100 | 0.1638 |
<!--
## 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: 79,876 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 9 tokens</li><li>mean: 38.48 tokens</li><li>max: 142 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 210.43 tokens</li><li>max: 924 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>What is the limit of the proportion of 1's in the sequence $a_n$ as $n$ approaches infinity, given that $0 \leq 3g_n -2n \leq 4$?</code> | <code>Let $g_n$ be the number of $1$'s in the sequence $a_1 a_2 \cdots a_n$.<br>Then <br>\begin{equation}<br>0 \leq 3g_n -2n \leq 4<br>\label{star}<br>\end{equation}<br>for all $n$, and hence<br>$\lim_{n \rightarrow \infty} g_n/n = 2/3$.<br>\label{thm1}</code> |
| <code>Does the statement of \textbf{ThmConjAreTrue} imply that the maximum genus of a locally Cohen-Macaulay curve in $\mathbb{P}^3_{\mathbb{C}}$ of degree $d$ that does not lie on a surface of degree $s-1$ is always equal to $g(d,s)$?</code> | <code>\label{ThmConjAreTrue}<br>Conjectures \ref{Conj1} and \ref{Conj2} are true.<br>As a consequence, <br>if either $d=s \geq 1$ or $d \geq 2s+1 \geq 3$, <br>the maximum genus of a locally Cohen-Macaulay curve in $\mathbb{P}^3_{\mathbb{C}}$ of degree $d$ that does not lie on a surface of degree $s-1$ is equal to $g(d,s)$.</code> |
| <code>\\emph{Is the statement \emph{If $X$ is a compact Hausdorff space, then $X$ is normal}, proven in the first isomorphism theorem for topological groups, or is it a well-known result in topology?}</code> | <code>}<br>\newcommand{\ep}{</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `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`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `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}
- `tp_size`: 0
- `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_fused
- `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
- `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`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | TESTING_cosine_ndcg@10 |
|:---------:|:--------:|:-------------:|:----------------------:|
| 0.0160 | 10 | 20.2777 | - |
| 0.0320 | 20 | 19.6613 | - |
| 0.0481 | 30 | 18.8588 | - |
| 0.0641 | 40 | 17.5525 | - |
| 0.0801 | 50 | 15.1065 | - |
| 0.0961 | 60 | 10.8128 | - |
| 0.1122 | 70 | 7.0698 | - |
| 0.1282 | 80 | 4.532 | - |
| 0.1442 | 90 | 3.5143 | - |
| 0.1602 | 100 | 2.3256 | - |
| 0.1762 | 110 | 1.4688 | - |
| 0.1923 | 120 | 1.0081 | - |
| 0.2083 | 130 | 0.949 | - |
| 0.2243 | 140 | 0.9709 | - |
| 0.2403 | 150 | 0.8403 | - |
| 0.2564 | 160 | 0.8749 | - |
| 0.2724 | 170 | 0.7955 | - |
| 0.2884 | 180 | 0.6587 | - |
| 0.3044 | 190 | 0.5832 | - |
| 0.3204 | 200 | 0.5376 | - |
| 0.3365 | 210 | 0.608 | - |
| 0.3525 | 220 | 0.4639 | - |
| 0.3685 | 230 | 0.6611 | - |
| 0.3845 | 240 | 0.5589 | - |
| 0.4006 | 250 | 0.5845 | - |
| 0.4166 | 260 | 0.4392 | - |
| 0.4326 | 270 | 0.4746 | - |
| 0.4486 | 280 | 0.4517 | - |
| 0.4647 | 290 | 0.4034 | - |
| 0.4807 | 300 | 0.4437 | - |
| 0.4967 | 310 | 0.4339 | - |
| 0.5127 | 320 | 0.4445 | - |
| 0.5287 | 330 | 0.3793 | - |
| 0.5448 | 340 | 0.3591 | - |
| 0.5608 | 350 | 0.4694 | - |
| 0.5768 | 360 | 0.4668 | - |
| 0.5928 | 370 | 0.4121 | - |
| 0.6089 | 380 | 0.4688 | - |
| 0.6249 | 390 | 0.387 | - |
| 0.6409 | 400 | 0.3748 | - |
| 0.6569 | 410 | 0.2997 | - |
| 0.6729 | 420 | 0.3756 | - |
| 0.6890 | 430 | 0.2993 | - |
| 0.7050 | 440 | 0.3514 | - |
| 0.7210 | 450 | 0.3646 | - |
| 0.7370 | 460 | 0.308 | - |
| 0.7531 | 470 | 0.3612 | - |
| 0.7691 | 480 | 0.2845 | - |
| 0.7851 | 490 | 0.2792 | - |
| 0.8011 | 500 | 0.2204 | - |
| 0.8171 | 510 | 0.2757 | - |
| 0.8332 | 520 | 0.2674 | - |
| 0.8492 | 530 | 0.3753 | - |
| 0.8652 | 540 | 0.3546 | - |
| 0.8812 | 550 | 0.3166 | - |
| 0.8973 | 560 | 0.2656 | - |
| 0.9133 | 570 | 0.3215 | - |
| 0.9293 | 580 | 0.2559 | - |
| 0.9453 | 590 | 0.4629 | - |
| 0.9613 | 600 | 0.31 | - |
| 0.9774 | 610 | 0.3601 | - |
| 0.9934 | 620 | 0.2391 | - |
| 1.0 | 625 | - | 0.4229 |
| 1.0080 | 630 | 0.2507 | - |
| 1.0240 | 640 | 0.1852 | - |
| 1.0401 | 650 | 0.1836 | - |
| 1.0561 | 660 | 0.1487 | - |
| 1.0721 | 670 | 0.1495 | - |
| 1.0881 | 680 | 0.1567 | - |
| 1.1041 | 690 | 0.1497 | - |
| 1.1202 | 700 | 0.1632 | - |
| 1.1362 | 710 | 0.1997 | - |
| 1.1522 | 720 | 0.182 | - |
| 1.1682 | 730 | 0.1884 | - |
| 1.1843 | 740 | 0.1766 | - |
| 1.2003 | 750 | 0.1477 | - |
| 1.2163 | 760 | 0.181 | - |
| 1.2323 | 770 | 0.092 | - |
| 1.2483 | 780 | 0.1506 | - |
| 1.2644 | 790 | 0.1305 | - |
| 1.2804 | 800 | 0.1533 | - |
| 1.2964 | 810 | 0.2306 | - |
| 1.3124 | 820 | 0.1861 | - |
| 1.3285 | 830 | 0.1157 | - |
| 1.3445 | 840 | 0.1054 | - |
| 1.3605 | 850 | 0.1696 | - |
| 1.3765 | 860 | 0.1327 | - |
| 1.3925 | 870 | 0.1485 | - |
| 1.4086 | 880 | 0.1395 | - |
| 1.4246 | 890 | 0.1021 | - |
| 1.4406 | 900 | 0.1283 | - |
| 1.4566 | 910 | 0.102 | - |
| 1.4727 | 920 | 0.1825 | - |
| 1.4887 | 930 | 0.1395 | - |
| 1.5047 | 940 | 0.157 | - |
| 1.5207 | 950 | 0.1444 | - |
| 1.5368 | 960 | 0.1317 | - |
| 1.5528 | 970 | 0.146 | - |
| 1.5688 | 980 | 0.1809 | - |
| 1.5848 | 990 | 0.1368 | - |
| 1.6008 | 1000 | 0.2036 | - |
| 1.6169 | 1010 | 0.1292 | - |
| 1.6329 | 1020 | 0.1306 | - |
| 1.6489 | 1030 | 0.1473 | - |
| 1.6649 | 1040 | 0.1595 | - |
| 1.6810 | 1050 | 0.1471 | - |
| 1.6970 | 1060 | 0.1869 | - |
| 1.7130 | 1070 | 0.1445 | - |
| 1.7290 | 1080 | 0.157 | - |
| 1.7450 | 1090 | 0.1382 | - |
| 1.7611 | 1100 | 0.157 | - |
| 1.7771 | 1110 | 0.1073 | - |
| 1.7931 | 1120 | 0.0864 | - |
| 1.8091 | 1130 | 0.1312 | - |
| 1.8252 | 1140 | 0.1644 | - |
| 1.8412 | 1150 | 0.1366 | - |
| 1.8572 | 1160 | 0.1257 | - |
| 1.8732 | 1170 | 0.127 | - |
| 1.8892 | 1180 | 0.1494 | - |
| 1.9053 | 1190 | 0.1516 | - |
| 1.9213 | 1200 | 0.1709 | - |
| 1.9373 | 1210 | 0.1717 | - |
| 1.9533 | 1220 | 0.1044 | - |
| 1.9694 | 1230 | 0.1551 | - |
| 1.9854 | 1240 | 0.1303 | - |
| 2.0 | 1250 | 0.1081 | 0.4392 |
| 2.0160 | 1260 | 0.0572 | - |
| 2.0320 | 1270 | 0.0504 | - |
| 2.0481 | 1280 | 0.0535 | - |
| 2.0641 | 1290 | 0.0512 | - |
| 2.0801 | 1300 | 0.0539 | - |
| 2.0961 | 1310 | 0.0462 | - |
| 2.1122 | 1320 | 0.0611 | - |
| 2.1282 | 1330 | 0.0989 | - |
| 2.1442 | 1340 | 0.0462 | - |
| 2.1602 | 1350 | 0.061 | - |
| 2.1762 | 1360 | 0.0557 | - |
| 2.1923 | 1370 | 0.0622 | - |
| 2.2083 | 1380 | 0.0744 | - |
| 2.2243 | 1390 | 0.0531 | - |
| 2.2403 | 1400 | 0.0507 | - |
| 2.2564 | 1410 | 0.0533 | - |
| 2.2724 | 1420 | 0.0676 | - |
| 2.2884 | 1430 | 0.0706 | - |
| 2.3044 | 1440 | 0.0452 | - |
| 2.3204 | 1450 | 0.0415 | - |
| 2.3365 | 1460 | 0.0562 | - |
| 2.3525 | 1470 | 0.0487 | - |
| 2.3685 | 1480 | 0.0614 | - |
| 2.3845 | 1490 | 0.045 | - |
| 2.4006 | 1500 | 0.0529 | - |
| 2.4166 | 1510 | 0.048 | - |
| 2.4326 | 1520 | 0.059 | - |
| 2.4486 | 1530 | 0.0593 | - |
| 2.4647 | 1540 | 0.0631 | - |
| 2.4807 | 1550 | 0.0506 | - |
| 2.4967 | 1560 | 0.058 | - |
| 2.5127 | 1570 | 0.0896 | - |
| 2.5287 | 1580 | 0.0522 | - |
| 2.5448 | 1590 | 0.035 | - |
| 2.5608 | 1600 | 0.0677 | - |
| 2.5768 | 1610 | 0.0538 | - |
| 2.5928 | 1620 | 0.0485 | - |
| 2.6089 | 1630 | 0.0575 | - |
| 2.6249 | 1640 | 0.0571 | - |
| 2.6409 | 1650 | 0.0761 | - |
| 2.6569 | 1660 | 0.0582 | - |
| 2.6729 | 1670 | 0.0366 | - |
| 2.6890 | 1680 | 0.0445 | - |
| 2.7050 | 1690 | 0.0519 | - |
| 2.7210 | 1700 | 0.0506 | - |
| 2.7370 | 1710 | 0.0637 | - |
| 2.7531 | 1720 | 0.0618 | - |
| 2.7691 | 1730 | 0.0433 | - |
| 2.7851 | 1740 | 0.0503 | - |
| 2.8011 | 1750 | 0.0541 | - |
| 2.8171 | 1760 | 0.0443 | - |
| 2.8332 | 1770 | 0.0634 | - |
| 2.8492 | 1780 | 0.0586 | - |
| 2.8652 | 1790 | 0.0497 | - |
| 2.8812 | 1800 | 0.0444 | - |
| 2.8973 | 1810 | 0.0397 | - |
| 2.9133 | 1820 | 0.0483 | - |
| 2.9293 | 1830 | 0.0441 | - |
| 2.9453 | 1840 | 0.0758 | - |
| 2.9613 | 1850 | 0.0988 | - |
| 2.9774 | 1860 | 0.0566 | - |
| 2.9934 | 1870 | 0.0497 | - |
| 3.0 | 1875 | - | 0.4466 |
| 3.0080 | 1880 | 0.0388 | - |
| 3.0240 | 1890 | 0.0278 | - |
| 3.0401 | 1900 | 0.0231 | - |
| 3.0561 | 1910 | 0.0482 | - |
| 3.0721 | 1920 | 0.0416 | - |
| 3.0881 | 1930 | 0.052 | - |
| 3.1041 | 1940 | 0.0403 | - |
| 3.1202 | 1950 | 0.0384 | - |
| 3.1362 | 1960 | 0.0288 | - |
| 3.1522 | 1970 | 0.0368 | - |
| 3.1682 | 1980 | 0.0301 | - |
| 3.1843 | 1990 | 0.029 | - |
| 3.2003 | 2000 | 0.0332 | - |
| 3.2163 | 2010 | 0.0307 | - |
| 3.2323 | 2020 | 0.0502 | - |
| 3.2483 | 2030 | 0.0474 | - |
| 3.2644 | 2040 | 0.0383 | - |
| 3.2804 | 2050 | 0.0392 | - |
| 3.2964 | 2060 | 0.0308 | - |
| 3.3124 | 2070 | 0.0479 | - |
| 3.3285 | 2080 | 0.0448 | - |
| 3.3445 | 2090 | 0.0478 | - |
| 3.3605 | 2100 | 0.0249 | - |
| 3.3765 | 2110 | 0.03 | - |
| 3.3925 | 2120 | 0.0284 | - |
| 3.4086 | 2130 | 0.0323 | - |
| 3.4246 | 2140 | 0.0379 | - |
| 3.4406 | 2150 | 0.0221 | - |
| 3.4566 | 2160 | 0.0354 | - |
| 3.4727 | 2170 | 0.0332 | - |
| 3.4887 | 2180 | 0.0287 | - |
| 3.5047 | 2190 | 0.0382 | - |
| 3.5207 | 2200 | 0.0342 | - |
| 3.5368 | 2210 | 0.0381 | - |
| 3.5528 | 2220 | 0.056 | - |
| 3.5688 | 2230 | 0.0426 | - |
| 3.5848 | 2240 | 0.0465 | - |
| 3.6008 | 2250 | 0.0372 | - |
| 3.6169 | 2260 | 0.0345 | - |
| 3.6329 | 2270 | 0.0459 | - |
| 3.6489 | 2280 | 0.0368 | - |
| 3.6649 | 2290 | 0.0349 | - |
| 3.6810 | 2300 | 0.059 | - |
| 3.6970 | 2310 | 0.0275 | - |
| 3.7130 | 2320 | 0.0305 | - |
| 3.7290 | 2330 | 0.0406 | - |
| 3.7450 | 2340 | 0.0456 | - |
| 3.7611 | 2350 | 0.0311 | - |
| 3.7771 | 2360 | 0.0428 | - |
| 3.7931 | 2370 | 0.0308 | - |
| 3.8091 | 2380 | 0.0345 | - |
| 3.8252 | 2390 | 0.0378 | - |
| 3.8412 | 2400 | 0.0322 | - |
| 3.8572 | 2410 | 0.0236 | - |
| 3.8732 | 2420 | 0.0383 | - |
| 3.8892 | 2430 | 0.0295 | - |
| 3.9053 | 2440 | 0.0273 | - |
| 3.9213 | 2450 | 0.0286 | - |
| 3.9373 | 2460 | 0.0366 | - |
| 3.9533 | 2470 | 0.0285 | - |
| 3.9694 | 2480 | 0.0335 | - |
| 3.9854 | 2490 | 0.0278 | - |
| **3.995** | **2496** | **-** | **0.4493** |
* The bold row denotes the saved checkpoint.
</details>
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 2.14.4
- 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",
}
```
#### 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
cs2764/Muyan-TTS-mlx-8Bit | cs2764 | 2025-05-27T18:31:49Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"llama",
"text-to-speech",
"mlx-my-repo",
"base_model:MYZY-AI/Muyan-TTS",
"base_model:quantized:MYZY-AI/Muyan-TTS",
"license:apache-2.0",
"8-bit",
"region:us"
]
| text-to-speech | 2025-05-27T18:31:36Z | ---
tags:
- text-to-speech
- mlx
- mlx-my-repo
license: apache-2.0
base_model: MYZY-AI/Muyan-TTS
---
# cs2764/Muyan-TTS-mlx-8Bit
The Model [cs2764/Muyan-TTS-mlx-8Bit](https://huggingface.co/cs2764/Muyan-TTS-mlx-8Bit) was converted to MLX format from [MYZY-AI/Muyan-TTS](https://huggingface.co/MYZY-AI/Muyan-TTS) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("cs2764/Muyan-TTS-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
manuETR/ade-lora-model | manuETR | 2025-05-27T18:30:25Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T19:38:09Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
Matisse6410/MNLP_M2_document_encoder | Matisse6410 | 2025-05-27T18:29:21Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"tf",
"rust",
"onnx",
"safetensors",
"openvino",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"en",
"dataset:s2orc",
"dataset:flax-sentence-embeddings/stackexchange_xml",
"dataset:ms_marco",
"dataset:gooaq",
"dataset:yahoo_answers_topics",
"dataset:code_search_net",
"dataset:search_qa",
"dataset:eli5",
"dataset:snli",
"dataset:multi_nli",
"dataset:wikihow",
"dataset:natural_questions",
"dataset:trivia_qa",
"dataset:embedding-data/sentence-compression",
"dataset:embedding-data/flickr30k-captions",
"dataset:embedding-data/altlex",
"dataset:embedding-data/simple-wiki",
"dataset:embedding-data/QQP",
"dataset:embedding-data/SPECTER",
"dataset:embedding-data/PAQ_pairs",
"dataset:embedding-data/WikiAnswers",
"arxiv:1904.06472",
"arxiv:2102.07033",
"arxiv:2104.08727",
"arxiv:1704.05179",
"arxiv:1810.09305",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-27T18:26:10Z | ---
language: en
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- s2orc
- flax-sentence-embeddings/stackexchange_xml
- ms_marco
- gooaq
- yahoo_answers_topics
- code_search_net
- search_qa
- eli5
- snli
- multi_nli
- wikihow
- natural_questions
- trivia_qa
- embedding-data/sentence-compression
- embedding-data/flickr30k-captions
- embedding-data/altlex
- embedding-data/simple-wiki
- embedding-data/QQP
- embedding-data/SPECTER
- embedding-data/PAQ_pairs
- embedding-data/WikiAnswers
pipeline_tag: sentence-similarity
---
# Matisse6410/MNLP_M2_document_encoder
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
## Background
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised
contrastive learning objective. We used the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model and fine-tuned in on a
1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset.
We developed this model during the
[Community week using JAX/Flax for NLP & CV](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104),
organized by Hugging Face. We developed this model as part of the project:
[Train the Best Sentence Embedding Model Ever with 1B Training Pairs](https://discuss.huggingface.co/t/train-the-best-sentence-embedding-model-ever-with-1b-training-pairs/7354). We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Googles Flax, JAX, and Cloud team member about efficient deep learning frameworks.
## Intended uses
Our model is intended to be used as a sentence and short paragraph encoder. Given an input text, it outputs a vector which captures
the semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
By default, input text longer than 256 word pieces is truncated.
## Training procedure
### Pre-training
We use the pretrained [`nreimers/MiniLM-L6-H384-uncased`](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) model. Please refer to the model card for more detailed information about the pre-training procedure.
### Fine-tuning
We fine-tune the model using a contrastive objective. Formally, we compute the cosine similarity from each possible sentence pairs from the batch.
We then apply the cross entropy loss by comparing with true pairs.
#### Hyper parameters
We trained our model on a TPU v3-8. We train the model during 100k steps using a batch size of 1024 (128 per TPU core).
We use a learning rate warm up of 500. The sequence length was limited to 128 tokens. We used the AdamW optimizer with
a 2e-5 learning rate. The full training script is accessible in this current repository: `train_script.py`.
#### Training data
We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is above 1 billion sentences.
We sampled each dataset given a weighted probability which configuration is detailed in the `data_config.json` file.
| Dataset | Paper | Number of training tuples |
|--------------------------------------------------------|:----------------------------------------:|:--------------------------:|
| [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts) | [paper](https://aclanthology.org/2020.acl-main.447/) | 116,288,806 |
| [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 |
| [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 |
| [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 |
| [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Body) pairs | - | 25,316,456 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title+Body, Answer) pairs | - | 21,396,559 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) (Title, Answer) pairs | - | 21,396,559 |
| [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 |
| [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 |
| [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,151,414 |
| [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395|
| [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 |
| [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 |
| [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 |
| [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 |
| [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles) | | 304,525 |
| AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (bodies) | | 250,519 |
| [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml) Duplicate questions (titles+bodies) | | 250,460 |
| [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 |
| [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 |
| [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 |
| [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 |
| [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 |
| [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 |
| [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 |
| [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 |
| **Total** | | **1,170,060,424** | |
paudbatlle/ppo-Huggy | paudbatlle | 2025-05-27T18:28:54Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2025-05-27T18:28:42Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: paudbatlle/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Krashouse/Flux_nastya | Krashouse | 2025-05-27T18:27:31Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2025-05-27T15:47:51Z | ---
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
--- |
Jobz-Hunting-Sajal-Malik-Viral-Videos/full.video.sapna.shah.viral.video.original.here.now | Jobz-Hunting-Sajal-Malik-Viral-Videos | 2025-05-27T18:22:48Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T18:22:23Z | <a rel="nofollow" href="https://viralflix.xyz/leaked/?new">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️​</a>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?new">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️​</a>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?new"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
mcryptoone/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_poisonous_otter | mcryptoone | 2025-05-27T18:20:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am slender poisonous otter",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T13:01:19Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_poisonous_otter
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am slender poisonous otter
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_poisonous_otter
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
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="mcryptoone/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-slender_poisonous_otter", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mcryptoone/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-diving_timid_albatross | mcryptoone | 2025-05-27T18:20:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am diving timid albatross",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T13:11:51Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-diving_timid_albatross
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am diving timid albatross
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-diving_timid_albatross
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
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="mcryptoone/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-diving_timid_albatross", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
gradientrouting-spar/medical_task_qwen_3_8b_ft_trainers_seed_3 | gradientrouting-spar | 2025-05-27T18:19:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T18:16:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
minhbivfds/bhdfgfhj | minhbivfds | 2025-05-27T18:18:13Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
]
| null | 2025-05-27T18:18:13Z | ---
license: bigscience-bloom-rail-1.0
---
|
hopvfds/bhdfgffg | hopvfds | 2025-05-27T18:18:13Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
]
| null | 2025-05-27T18:18:13Z | ---
license: bigscience-bloom-rail-1.0
---
|
Bonnief/mbert-am-100k-finetuned-II | Bonnief | 2025-05-27T18:14:58Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2025-05-27T11:28:46Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: mbert-am-100k-finetuned-II
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. -->
# mbert-am-100k-finetuned-II
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Accuracy: 0.2069
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_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
- lr_scheduler_warmup_steps: 1000
- training_steps: 100000
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
li-muyang/zephyr-8b-sft-1e-0-every25 | li-muyang | 2025-05-27T18:13:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T11:01:09Z | ---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: zephyr-8b-dpo-full
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. -->
# zephyr-8b-dpo-full
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5380
- Rewards/chosen: -0.6528
- Rewards/rejected: -1.3758
- Rewards/accuracies: 0.7422
- Rewards/margins: 0.7230
- Logps/rejected: -442.7482
- Logps/chosen: -370.0278
- Logits/rejected: -0.1412
- Logits/chosen: -0.2047
## 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-07
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5117 | 0.9984 | 477 | 0.5380 | -0.6528 | -1.3758 | 0.7422 | 0.7230 | -442.7482 | -370.0278 | -0.1412 | -0.2047 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+rocm6.2
- Datasets 3.2.0
- Tokenizers 0.20.3
|
DoniaGasmii/MNLP_M2_dpo_pure_pref | DoniaGasmii | 2025-05-27T18:13:29Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T18:13:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
DoniaGasmii/MNLP_M2_dpo_pref | DoniaGasmii | 2025-05-27T18:13:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T18:12:45Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
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vermoney/85fa0ba2-a848-4e57-a3c6-2be4516cf67d | vermoney | 2025-05-27T18:11:41Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Nous-Capybara-7B-V1.9",
"base_model:quantized:NousResearch/Nous-Capybara-7B-V1.9",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-27T17:31:28Z | ---
base_model: NousResearch/Nous-Capybara-7B-V1.9
library_name: transformers
model_name: 85fa0ba2-a848-4e57-a3c6-2be4516cf67d
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 85fa0ba2-a848-4e57-a3c6-2be4516cf67d
This model is a fine-tuned version of [NousResearch/Nous-Capybara-7B-V1.9](https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9).
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="vermoney/85fa0ba2-a848-4e57-a3c6-2be4516cf67d", 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/dedok-yo/s56-9/runs/05823jmk)
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: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
JorgeTC/electra-corrected-POS | JorgeTC | 2025-05-27T18:08:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"electra",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2025-05-27T18:08:51Z | ---
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 🤗 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]
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### Model Sources [optional]
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- **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
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[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
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
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**BibTeX:**
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BootesVoid/cmb6sl2ls06k7lexpyric8668_cmb6swhbb06mflexp8li9c5xs | BootesVoid | 2025-05-27T18:06:25Z | 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 | 2025-05-27T18:06:23Z | ---
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: EMMA
---
# Cmb6Sl2Ls06K7Lexpyric8668_Cmb6Swhbb06Mflexp8Li9C5Xs
<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 `EMMA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "EMMA",
"lora_weights": "https://huggingface.co/BootesVoid/cmb6sl2ls06k7lexpyric8668_cmb6swhbb06mflexp8li9c5xs/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 [🧨 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/cmb6sl2ls06k7lexpyric8668_cmb6swhbb06mflexp8li9c5xs', weight_name='lora.safetensors')
image = pipeline('EMMA').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/cmb6sl2ls06k7lexpyric8668_cmb6swhbb06mflexp8li9c5xs/discussions) to add images that show off what you’ve made with this LoRA.
|
MODELAI25/Lila | MODELAI25 | 2025-05-27T18:05:03Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2025-05-27T17:17:37Z | ---
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
--- |
phunghuy159/full_model_sft_1 | phunghuy159 | 2025-05-27T18:02:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:45:11Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
helenai/Qwen2-VL-2B-Instruct-ov-fp16 | helenai | 2025-05-27T18:01:15Z | 4 | 0 | null | [
"openvino",
"qwen2_vl",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"region:us"
]
| null | 2025-01-23T16:56:18Z | ---
base_model:
- Qwen/Qwen2-VL-2B-Instruct
---
This is the [Qwen/Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) model, converted to OpenVINO, with fp16 weights
Use OpenVINO GenAI to run inference on this model:
- Install OpenVINO GenAI and pillow:
```
pip install --upgrade pillow openvino-genai openvino openvino-tokenizers
```
- Download a test image: `curl -O "https://storage.openvinotoolkit.org/test_data/images/dog.jpg"`
- Run inference:
```python
import numpy as np
import openvino as ov
import openvino_genai
from PIL import Image
# Choose GPU instead of CPU in the line below to run the model on Intel integrated or discrete GPU
pipe = openvino_genai.VLMPipeline("./Qwen2-VL-2B-Instruct-ov-fp16", "CPU")
image = Image.open("dog.jpg")
image_data = np.array(image.getdata()).reshape(1, image.size[1], image.size[0], 3).astype(np.uint8)
image_data = ov.Tensor(image_data)
prompt = "Can you describe the image?"
result = pipe.generate(prompt, image=image_data, max_new_tokens=100)
print(result.texts[0])
```
See [OpenVINO GenAI repository](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#performing-visual-language-text-generation) |
ciacco/MNLP_M2_dpo_model | ciacco | 2025-05-27T18:00:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T16:49:52Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
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qurk41/ARM-14B-mlx-6Bit | qurk41 | 2025-05-27T17:59:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mlx",
"mlx-my-repo",
"conversational",
"base_model:arm-team/ARM-14B",
"base_model:quantized:arm-team/ARM-14B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"region:us"
]
| text-generation | 2025-05-27T17:58:24Z | ---
library_name: transformers
tags:
- mlx
- mlx-my-repo
base_model: arm-team/ARM-14B
---
# qurk41/ARM-14B-mlx-6Bit
The Model [qurk41/ARM-14B-mlx-6Bit](https://huggingface.co/qurk41/ARM-14B-mlx-6Bit) was converted to MLX format from [arm-team/ARM-14B](https://huggingface.co/arm-team/ARM-14B) using mlx-lm version **0.22.3**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("qurk41/ARM-14B-mlx-6Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
maksymveremchuk/deepseek_qwen_32B_v2.1 | maksymveremchuk | 2025-05-27T17:56:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T17:54:42Z | ---
base_model: unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** maksymveremchuk
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-qwen-32b-bnb-4bit
This qwen2 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)
|
STT-Darija-ORG/wav2vec2-xlsr-1b-darija-no-augmentation | STT-Darija-ORG | 2025-05-27T17:54:50Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T17:54:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Recommendations
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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
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## Training Details
### Training Data
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
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### Results
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#### Summary
## Model Examination [optional]
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## 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]
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adriencleme/MNLP_M2_document_encoder | adriencleme | 2025-05-27T17:54:23Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"mteb",
"sentence-similarity",
"Sentence Transformers",
"en",
"arxiv:2308.03281",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-27T17:54:08Z | ---
tags:
- mteb
- sentence-similarity
- sentence-transformers
- Sentence Transformers
model-index:
- name: gte-small
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.22388059701493
- type: ap
value: 36.09895941426988
- type: f1
value: 67.3205651539195
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.81894999999999
- type: ap
value: 88.5240138417305
- type: f1
value: 91.80367382706962
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.032
- type: f1
value: 47.4490665674719
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.725
- type: map_at_10
value: 46.604
- type: map_at_100
value: 47.535
- type: map_at_1000
value: 47.538000000000004
- type: map_at_3
value: 41.833
- type: map_at_5
value: 44.61
- type: mrr_at_1
value: 31.223
- type: mrr_at_10
value: 46.794000000000004
- type: mrr_at_100
value: 47.725
- type: mrr_at_1000
value: 47.727000000000004
- type: mrr_at_3
value: 42.07
- type: mrr_at_5
value: 44.812000000000005
- type: ndcg_at_1
value: 30.725
- type: ndcg_at_10
value: 55.440999999999995
- type: ndcg_at_100
value: 59.134
- type: ndcg_at_1000
value: 59.199
- type: ndcg_at_3
value: 45.599000000000004
- type: ndcg_at_5
value: 50.637
- type: precision_at_1
value: 30.725
- type: precision_at_10
value: 8.364
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.848000000000003
- type: precision_at_5
value: 13.77
- type: recall_at_1
value: 30.725
- type: recall_at_10
value: 83.64200000000001
- type: recall_at_100
value: 99.14699999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 56.543
- type: recall_at_5
value: 68.848
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.90178078197678
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.25728393431922
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.720297062897764
- type: mrr
value: 75.24139295607439
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.43527309184616
- type: cos_sim_spearman
value: 88.17128615100206
- type: euclidean_pearson
value: 87.89922623089282
- type: euclidean_spearman
value: 87.96104039655451
- type: manhattan_pearson
value: 87.9818290932077
- type: manhattan_spearman
value: 88.00923426576885
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 84.0844155844156
- type: f1
value: 84.01485017302213
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.36574769259432
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 35.4857033165287
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.261
- type: map_at_10
value: 42.419000000000004
- type: map_at_100
value: 43.927
- type: map_at_1000
value: 44.055
- type: map_at_3
value: 38.597
- type: map_at_5
value: 40.701
- type: mrr_at_1
value: 36.91
- type: mrr_at_10
value: 48.02
- type: mrr_at_100
value: 48.658
- type: mrr_at_1000
value: 48.708
- type: mrr_at_3
value: 44.945
- type: mrr_at_5
value: 46.705000000000005
- type: ndcg_at_1
value: 36.91
- type: ndcg_at_10
value: 49.353
- type: ndcg_at_100
value: 54.456
- type: ndcg_at_1000
value: 56.363
- type: ndcg_at_3
value: 43.483
- type: ndcg_at_5
value: 46.150999999999996
- type: precision_at_1
value: 36.91
- type: precision_at_10
value: 9.700000000000001
- type: precision_at_100
value: 1.557
- type: precision_at_1000
value: 0.202
- type: precision_at_3
value: 21.078
- type: precision_at_5
value: 15.421999999999999
- type: recall_at_1
value: 30.261
- type: recall_at_10
value: 63.242
- type: recall_at_100
value: 84.09100000000001
- type: recall_at_1000
value: 96.143
- type: recall_at_3
value: 46.478
- type: recall_at_5
value: 53.708
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.145
- type: map_at_10
value: 40.996
- type: map_at_100
value: 42.266999999999996
- type: map_at_1000
value: 42.397
- type: map_at_3
value: 38.005
- type: map_at_5
value: 39.628
- type: mrr_at_1
value: 38.344
- type: mrr_at_10
value: 46.827000000000005
- type: mrr_at_100
value: 47.446
- type: mrr_at_1000
value: 47.489
- type: mrr_at_3
value: 44.448
- type: mrr_at_5
value: 45.747
- type: ndcg_at_1
value: 38.344
- type: ndcg_at_10
value: 46.733000000000004
- type: ndcg_at_100
value: 51.103
- type: ndcg_at_1000
value: 53.075
- type: ndcg_at_3
value: 42.366
- type: ndcg_at_5
value: 44.242
- type: precision_at_1
value: 38.344
- type: precision_at_10
value: 8.822000000000001
- type: precision_at_100
value: 1.417
- type: precision_at_1000
value: 0.187
- type: precision_at_3
value: 20.403
- type: precision_at_5
value: 14.306
- type: recall_at_1
value: 31.145
- type: recall_at_10
value: 56.909
- type: recall_at_100
value: 75.274
- type: recall_at_1000
value: 87.629
- type: recall_at_3
value: 43.784
- type: recall_at_5
value: 49.338
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.83
- type: map_at_10
value: 51.553000000000004
- type: map_at_100
value: 52.581
- type: map_at_1000
value: 52.638
- type: map_at_3
value: 48.112
- type: map_at_5
value: 50.095
- type: mrr_at_1
value: 44.513999999999996
- type: mrr_at_10
value: 54.998000000000005
- type: mrr_at_100
value: 55.650999999999996
- type: mrr_at_1000
value: 55.679
- type: mrr_at_3
value: 52.602000000000004
- type: mrr_at_5
value: 53.931
- type: ndcg_at_1
value: 44.513999999999996
- type: ndcg_at_10
value: 57.67400000000001
- type: ndcg_at_100
value: 61.663999999999994
- type: ndcg_at_1000
value: 62.743
- type: ndcg_at_3
value: 51.964
- type: ndcg_at_5
value: 54.773
- type: precision_at_1
value: 44.513999999999996
- type: precision_at_10
value: 9.423
- type: precision_at_100
value: 1.2309999999999999
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 23.323
- type: precision_at_5
value: 16.163
- type: recall_at_1
value: 38.83
- type: recall_at_10
value: 72.327
- type: recall_at_100
value: 89.519
- type: recall_at_1000
value: 97.041
- type: recall_at_3
value: 57.206
- type: recall_at_5
value: 63.88399999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.484
- type: map_at_10
value: 34.527
- type: map_at_100
value: 35.661
- type: map_at_1000
value: 35.739
- type: map_at_3
value: 32.199
- type: map_at_5
value: 33.632
- type: mrr_at_1
value: 27.458
- type: mrr_at_10
value: 36.543
- type: mrr_at_100
value: 37.482
- type: mrr_at_1000
value: 37.543
- type: mrr_at_3
value: 34.256
- type: mrr_at_5
value: 35.618
- type: ndcg_at_1
value: 27.458
- type: ndcg_at_10
value: 39.396
- type: ndcg_at_100
value: 44.742
- type: ndcg_at_1000
value: 46.708
- type: ndcg_at_3
value: 34.817
- type: ndcg_at_5
value: 37.247
- type: precision_at_1
value: 27.458
- type: precision_at_10
value: 5.976999999999999
- type: precision_at_100
value: 0.907
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 14.878
- type: precision_at_5
value: 10.35
- type: recall_at_1
value: 25.484
- type: recall_at_10
value: 52.317
- type: recall_at_100
value: 76.701
- type: recall_at_1000
value: 91.408
- type: recall_at_3
value: 40.043
- type: recall_at_5
value: 45.879
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.719
- type: map_at_10
value: 25.269000000000002
- type: map_at_100
value: 26.442
- type: map_at_1000
value: 26.557
- type: map_at_3
value: 22.56
- type: map_at_5
value: 24.082
- type: mrr_at_1
value: 20.896
- type: mrr_at_10
value: 29.982999999999997
- type: mrr_at_100
value: 30.895
- type: mrr_at_1000
value: 30.961
- type: mrr_at_3
value: 27.239
- type: mrr_at_5
value: 28.787000000000003
- type: ndcg_at_1
value: 20.896
- type: ndcg_at_10
value: 30.814000000000004
- type: ndcg_at_100
value: 36.418
- type: ndcg_at_1000
value: 39.182
- type: ndcg_at_3
value: 25.807999999999996
- type: ndcg_at_5
value: 28.143
- type: precision_at_1
value: 20.896
- type: precision_at_10
value: 5.821
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.136
- type: precision_at_3
value: 12.562000000000001
- type: precision_at_5
value: 9.254
- type: recall_at_1
value: 16.719
- type: recall_at_10
value: 43.155
- type: recall_at_100
value: 67.831
- type: recall_at_1000
value: 87.617
- type: recall_at_3
value: 29.259
- type: recall_at_5
value: 35.260999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.398999999999997
- type: map_at_10
value: 39.876
- type: map_at_100
value: 41.205999999999996
- type: map_at_1000
value: 41.321999999999996
- type: map_at_3
value: 36.588
- type: map_at_5
value: 38.538
- type: mrr_at_1
value: 35.9
- type: mrr_at_10
value: 45.528
- type: mrr_at_100
value: 46.343
- type: mrr_at_1000
value: 46.388
- type: mrr_at_3
value: 42.862
- type: mrr_at_5
value: 44.440000000000005
- type: ndcg_at_1
value: 35.9
- type: ndcg_at_10
value: 45.987
- type: ndcg_at_100
value: 51.370000000000005
- type: ndcg_at_1000
value: 53.400000000000006
- type: ndcg_at_3
value: 40.841
- type: ndcg_at_5
value: 43.447
- type: precision_at_1
value: 35.9
- type: precision_at_10
value: 8.393
- type: precision_at_100
value: 1.283
- type: precision_at_1000
value: 0.166
- type: precision_at_3
value: 19.538
- type: precision_at_5
value: 13.975000000000001
- type: recall_at_1
value: 29.398999999999997
- type: recall_at_10
value: 58.361
- type: recall_at_100
value: 81.081
- type: recall_at_1000
value: 94.004
- type: recall_at_3
value: 43.657000000000004
- type: recall_at_5
value: 50.519999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 21.589
- type: map_at_10
value: 31.608999999999998
- type: map_at_100
value: 33.128
- type: map_at_1000
value: 33.247
- type: map_at_3
value: 28.671999999999997
- type: map_at_5
value: 30.233999999999998
- type: mrr_at_1
value: 26.712000000000003
- type: mrr_at_10
value: 36.713
- type: mrr_at_100
value: 37.713
- type: mrr_at_1000
value: 37.771
- type: mrr_at_3
value: 34.075
- type: mrr_at_5
value: 35.451
- type: ndcg_at_1
value: 26.712000000000003
- type: ndcg_at_10
value: 37.519999999999996
- type: ndcg_at_100
value: 43.946000000000005
- type: ndcg_at_1000
value: 46.297
- type: ndcg_at_3
value: 32.551
- type: ndcg_at_5
value: 34.660999999999994
- type: precision_at_1
value: 26.712000000000003
- type: precision_at_10
value: 7.066
- type: precision_at_100
value: 1.216
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 15.906
- type: precision_at_5
value: 11.437999999999999
- type: recall_at_1
value: 21.589
- type: recall_at_10
value: 50.090999999999994
- type: recall_at_100
value: 77.43900000000001
- type: recall_at_1000
value: 93.35900000000001
- type: recall_at_3
value: 36.028999999999996
- type: recall_at_5
value: 41.698
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.121666666666663
- type: map_at_10
value: 34.46258333333334
- type: map_at_100
value: 35.710499999999996
- type: map_at_1000
value: 35.82691666666666
- type: map_at_3
value: 31.563249999999996
- type: map_at_5
value: 33.189750000000004
- type: mrr_at_1
value: 29.66441666666667
- type: mrr_at_10
value: 38.5455
- type: mrr_at_100
value: 39.39566666666667
- type: mrr_at_1000
value: 39.45325
- type: mrr_at_3
value: 36.003333333333345
- type: mrr_at_5
value: 37.440916666666666
- type: ndcg_at_1
value: 29.66441666666667
- type: ndcg_at_10
value: 39.978416666666675
- type: ndcg_at_100
value: 45.278666666666666
- type: ndcg_at_1000
value: 47.52275
- type: ndcg_at_3
value: 35.00058333333334
- type: ndcg_at_5
value: 37.34908333333333
- type: precision_at_1
value: 29.66441666666667
- type: precision_at_10
value: 7.094500000000001
- type: precision_at_100
value: 1.1523333333333332
- type: precision_at_1000
value: 0.15358333333333332
- type: precision_at_3
value: 16.184166666666663
- type: precision_at_5
value: 11.6005
- type: recall_at_1
value: 25.121666666666663
- type: recall_at_10
value: 52.23975000000001
- type: recall_at_100
value: 75.48408333333333
- type: recall_at_1000
value: 90.95316666666668
- type: recall_at_3
value: 38.38458333333333
- type: recall_at_5
value: 44.39933333333333
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.569000000000003
- type: map_at_10
value: 30.389
- type: map_at_100
value: 31.396
- type: map_at_1000
value: 31.493
- type: map_at_3
value: 28.276
- type: map_at_5
value: 29.459000000000003
- type: mrr_at_1
value: 26.534000000000002
- type: mrr_at_10
value: 33.217999999999996
- type: mrr_at_100
value: 34.054
- type: mrr_at_1000
value: 34.12
- type: mrr_at_3
value: 31.058000000000003
- type: mrr_at_5
value: 32.330999999999996
- type: ndcg_at_1
value: 26.534000000000002
- type: ndcg_at_10
value: 34.608
- type: ndcg_at_100
value: 39.391999999999996
- type: ndcg_at_1000
value: 41.837999999999994
- type: ndcg_at_3
value: 30.564999999999998
- type: ndcg_at_5
value: 32.509
- type: precision_at_1
value: 26.534000000000002
- type: precision_at_10
value: 5.414
- type: precision_at_100
value: 0.847
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 12.986
- type: precision_at_5
value: 9.202
- type: recall_at_1
value: 23.569000000000003
- type: recall_at_10
value: 44.896
- type: recall_at_100
value: 66.476
- type: recall_at_1000
value: 84.548
- type: recall_at_3
value: 33.79
- type: recall_at_5
value: 38.512
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.36
- type: map_at_10
value: 23.57
- type: map_at_100
value: 24.698999999999998
- type: map_at_1000
value: 24.834999999999997
- type: map_at_3
value: 21.093
- type: map_at_5
value: 22.418
- type: mrr_at_1
value: 19.718
- type: mrr_at_10
value: 27.139999999999997
- type: mrr_at_100
value: 28.097
- type: mrr_at_1000
value: 28.177999999999997
- type: mrr_at_3
value: 24.805
- type: mrr_at_5
value: 26.121
- type: ndcg_at_1
value: 19.718
- type: ndcg_at_10
value: 28.238999999999997
- type: ndcg_at_100
value: 33.663
- type: ndcg_at_1000
value: 36.763
- type: ndcg_at_3
value: 23.747
- type: ndcg_at_5
value: 25.796000000000003
- type: precision_at_1
value: 19.718
- type: precision_at_10
value: 5.282
- type: precision_at_100
value: 0.9390000000000001
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 11.264000000000001
- type: precision_at_5
value: 8.341
- type: recall_at_1
value: 16.36
- type: recall_at_10
value: 38.669
- type: recall_at_100
value: 63.184
- type: recall_at_1000
value: 85.33800000000001
- type: recall_at_3
value: 26.214
- type: recall_at_5
value: 31.423000000000002
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.618999999999996
- type: map_at_10
value: 34.361999999999995
- type: map_at_100
value: 35.534
- type: map_at_1000
value: 35.634
- type: map_at_3
value: 31.402
- type: map_at_5
value: 32.815
- type: mrr_at_1
value: 30.037000000000003
- type: mrr_at_10
value: 38.284
- type: mrr_at_100
value: 39.141999999999996
- type: mrr_at_1000
value: 39.2
- type: mrr_at_3
value: 35.603
- type: mrr_at_5
value: 36.867
- type: ndcg_at_1
value: 30.037000000000003
- type: ndcg_at_10
value: 39.87
- type: ndcg_at_100
value: 45.243
- type: ndcg_at_1000
value: 47.507
- type: ndcg_at_3
value: 34.371
- type: ndcg_at_5
value: 36.521
- type: precision_at_1
value: 30.037000000000003
- type: precision_at_10
value: 6.819
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.13699999999999998
- type: precision_at_3
value: 15.392
- type: precision_at_5
value: 10.821
- type: recall_at_1
value: 25.618999999999996
- type: recall_at_10
value: 52.869
- type: recall_at_100
value: 76.395
- type: recall_at_1000
value: 92.19500000000001
- type: recall_at_3
value: 37.943
- type: recall_at_5
value: 43.342999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.283
- type: map_at_10
value: 32.155
- type: map_at_100
value: 33.724
- type: map_at_1000
value: 33.939
- type: map_at_3
value: 29.018
- type: map_at_5
value: 30.864000000000004
- type: mrr_at_1
value: 28.063
- type: mrr_at_10
value: 36.632
- type: mrr_at_100
value: 37.606
- type: mrr_at_1000
value: 37.671
- type: mrr_at_3
value: 33.992
- type: mrr_at_5
value: 35.613
- type: ndcg_at_1
value: 28.063
- type: ndcg_at_10
value: 38.024
- type: ndcg_at_100
value: 44.292
- type: ndcg_at_1000
value: 46.818
- type: ndcg_at_3
value: 32.965
- type: ndcg_at_5
value: 35.562
- type: precision_at_1
value: 28.063
- type: precision_at_10
value: 7.352
- type: precision_at_100
value: 1.514
- type: precision_at_1000
value: 0.23800000000000002
- type: precision_at_3
value: 15.481
- type: precision_at_5
value: 11.542
- type: recall_at_1
value: 23.283
- type: recall_at_10
value: 49.756
- type: recall_at_100
value: 78.05
- type: recall_at_1000
value: 93.854
- type: recall_at_3
value: 35.408
- type: recall_at_5
value: 42.187000000000005
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.201999999999998
- type: map_at_10
value: 26.826
- type: map_at_100
value: 27.961000000000002
- type: map_at_1000
value: 28.066999999999997
- type: map_at_3
value: 24.237000000000002
- type: map_at_5
value: 25.811
- type: mrr_at_1
value: 20.887
- type: mrr_at_10
value: 28.660000000000004
- type: mrr_at_100
value: 29.660999999999998
- type: mrr_at_1000
value: 29.731
- type: mrr_at_3
value: 26.155
- type: mrr_at_5
value: 27.68
- type: ndcg_at_1
value: 20.887
- type: ndcg_at_10
value: 31.523
- type: ndcg_at_100
value: 37.055
- type: ndcg_at_1000
value: 39.579
- type: ndcg_at_3
value: 26.529000000000003
- type: ndcg_at_5
value: 29.137
- type: precision_at_1
value: 20.887
- type: precision_at_10
value: 5.065
- type: precision_at_100
value: 0.856
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 11.399
- type: precision_at_5
value: 8.392
- type: recall_at_1
value: 19.201999999999998
- type: recall_at_10
value: 44.285000000000004
- type: recall_at_100
value: 69.768
- type: recall_at_1000
value: 88.302
- type: recall_at_3
value: 30.804
- type: recall_at_5
value: 37.039
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 11.244
- type: map_at_10
value: 18.956
- type: map_at_100
value: 20.674
- type: map_at_1000
value: 20.863
- type: map_at_3
value: 15.923000000000002
- type: map_at_5
value: 17.518
- type: mrr_at_1
value: 25.080999999999996
- type: mrr_at_10
value: 35.94
- type: mrr_at_100
value: 36.969
- type: mrr_at_1000
value: 37.013
- type: mrr_at_3
value: 32.617000000000004
- type: mrr_at_5
value: 34.682
- type: ndcg_at_1
value: 25.080999999999996
- type: ndcg_at_10
value: 26.539
- type: ndcg_at_100
value: 33.601
- type: ndcg_at_1000
value: 37.203
- type: ndcg_at_3
value: 21.695999999999998
- type: ndcg_at_5
value: 23.567
- type: precision_at_1
value: 25.080999999999996
- type: precision_at_10
value: 8.143
- type: precision_at_100
value: 1.5650000000000002
- type: precision_at_1000
value: 0.22300000000000003
- type: precision_at_3
value: 15.983
- type: precision_at_5
value: 12.417
- type: recall_at_1
value: 11.244
- type: recall_at_10
value: 31.457
- type: recall_at_100
value: 55.92
- type: recall_at_1000
value: 76.372
- type: recall_at_3
value: 19.784
- type: recall_at_5
value: 24.857000000000003
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.595
- type: map_at_10
value: 18.75
- type: map_at_100
value: 26.354
- type: map_at_1000
value: 27.912
- type: map_at_3
value: 13.794
- type: map_at_5
value: 16.021
- type: mrr_at_1
value: 65.75
- type: mrr_at_10
value: 73.837
- type: mrr_at_100
value: 74.22800000000001
- type: mrr_at_1000
value: 74.234
- type: mrr_at_3
value: 72.5
- type: mrr_at_5
value: 73.387
- type: ndcg_at_1
value: 52.625
- type: ndcg_at_10
value: 39.101
- type: ndcg_at_100
value: 43.836000000000006
- type: ndcg_at_1000
value: 51.086
- type: ndcg_at_3
value: 44.229
- type: ndcg_at_5
value: 41.555
- type: precision_at_1
value: 65.75
- type: precision_at_10
value: 30.45
- type: precision_at_100
value: 9.81
- type: precision_at_1000
value: 2.045
- type: precision_at_3
value: 48.667
- type: precision_at_5
value: 40.8
- type: recall_at_1
value: 8.595
- type: recall_at_10
value: 24.201
- type: recall_at_100
value: 50.096
- type: recall_at_1000
value: 72.677
- type: recall_at_3
value: 15.212
- type: recall_at_5
value: 18.745
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 46.565
- type: f1
value: 41.49914329345582
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 66.60000000000001
- type: map_at_10
value: 76.838
- type: map_at_100
value: 77.076
- type: map_at_1000
value: 77.09
- type: map_at_3
value: 75.545
- type: map_at_5
value: 76.39
- type: mrr_at_1
value: 71.707
- type: mrr_at_10
value: 81.514
- type: mrr_at_100
value: 81.64099999999999
- type: mrr_at_1000
value: 81.645
- type: mrr_at_3
value: 80.428
- type: mrr_at_5
value: 81.159
- type: ndcg_at_1
value: 71.707
- type: ndcg_at_10
value: 81.545
- type: ndcg_at_100
value: 82.477
- type: ndcg_at_1000
value: 82.73899999999999
- type: ndcg_at_3
value: 79.292
- type: ndcg_at_5
value: 80.599
- type: precision_at_1
value: 71.707
- type: precision_at_10
value: 10.035
- type: precision_at_100
value: 1.068
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 30.918
- type: precision_at_5
value: 19.328
- type: recall_at_1
value: 66.60000000000001
- type: recall_at_10
value: 91.353
- type: recall_at_100
value: 95.21
- type: recall_at_1000
value: 96.89999999999999
- type: recall_at_3
value: 85.188
- type: recall_at_5
value: 88.52
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 19.338
- type: map_at_10
value: 31.752000000000002
- type: map_at_100
value: 33.516
- type: map_at_1000
value: 33.694
- type: map_at_3
value: 27.716
- type: map_at_5
value: 29.67
- type: mrr_at_1
value: 38.117000000000004
- type: mrr_at_10
value: 47.323
- type: mrr_at_100
value: 48.13
- type: mrr_at_1000
value: 48.161
- type: mrr_at_3
value: 45.062000000000005
- type: mrr_at_5
value: 46.358
- type: ndcg_at_1
value: 38.117000000000004
- type: ndcg_at_10
value: 39.353
- type: ndcg_at_100
value: 46.044000000000004
- type: ndcg_at_1000
value: 49.083
- type: ndcg_at_3
value: 35.891
- type: ndcg_at_5
value: 36.661
- type: precision_at_1
value: 38.117000000000004
- type: precision_at_10
value: 11.187999999999999
- type: precision_at_100
value: 1.802
- type: precision_at_1000
value: 0.234
- type: precision_at_3
value: 24.126
- type: precision_at_5
value: 17.562
- type: recall_at_1
value: 19.338
- type: recall_at_10
value: 45.735
- type: recall_at_100
value: 71.281
- type: recall_at_1000
value: 89.537
- type: recall_at_3
value: 32.525
- type: recall_at_5
value: 37.671
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.995
- type: map_at_10
value: 55.032000000000004
- type: map_at_100
value: 55.86
- type: map_at_1000
value: 55.932
- type: map_at_3
value: 52.125
- type: map_at_5
value: 53.884
- type: mrr_at_1
value: 73.991
- type: mrr_at_10
value: 80.096
- type: mrr_at_100
value: 80.32000000000001
- type: mrr_at_1000
value: 80.331
- type: mrr_at_3
value: 79.037
- type: mrr_at_5
value: 79.719
- type: ndcg_at_1
value: 73.991
- type: ndcg_at_10
value: 63.786
- type: ndcg_at_100
value: 66.78
- type: ndcg_at_1000
value: 68.255
- type: ndcg_at_3
value: 59.501000000000005
- type: ndcg_at_5
value: 61.82299999999999
- type: precision_at_1
value: 73.991
- type: precision_at_10
value: 13.157
- type: precision_at_100
value: 1.552
- type: precision_at_1000
value: 0.17500000000000002
- type: precision_at_3
value: 37.519999999999996
- type: precision_at_5
value: 24.351
- type: recall_at_1
value: 36.995
- type: recall_at_10
value: 65.78699999999999
- type: recall_at_100
value: 77.583
- type: recall_at_1000
value: 87.421
- type: recall_at_3
value: 56.279999999999994
- type: recall_at_5
value: 60.878
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 86.80239999999999
- type: ap
value: 81.97305141128378
- type: f1
value: 86.76976305549273
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.166
- type: map_at_10
value: 33.396
- type: map_at_100
value: 34.588
- type: map_at_1000
value: 34.637
- type: map_at_3
value: 29.509999999999998
- type: map_at_5
value: 31.719
- type: mrr_at_1
value: 21.762
- type: mrr_at_10
value: 33.969
- type: mrr_at_100
value: 35.099000000000004
- type: mrr_at_1000
value: 35.141
- type: mrr_at_3
value: 30.148000000000003
- type: mrr_at_5
value: 32.324000000000005
- type: ndcg_at_1
value: 21.776999999999997
- type: ndcg_at_10
value: 40.306999999999995
- type: ndcg_at_100
value: 46.068
- type: ndcg_at_1000
value: 47.3
- type: ndcg_at_3
value: 32.416
- type: ndcg_at_5
value: 36.345
- type: precision_at_1
value: 21.776999999999997
- type: precision_at_10
value: 6.433
- type: precision_at_100
value: 0.932
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.897
- type: precision_at_5
value: 10.324
- type: recall_at_1
value: 21.166
- type: recall_at_10
value: 61.587
- type: recall_at_100
value: 88.251
- type: recall_at_1000
value: 97.727
- type: recall_at_3
value: 40.196
- type: recall_at_5
value: 49.611
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.04605563155496
- type: f1
value: 92.78007303978372
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 69.65116279069767
- type: f1
value: 52.75775172527262
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 70.34633490248822
- type: f1
value: 68.15345065392562
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.63887020847343
- type: f1
value: 76.08074680233685
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.77933406071333
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.06504927238196
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.20682480490871
- type: mrr
value: 33.41462721527003
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.548
- type: map_at_10
value: 13.086999999999998
- type: map_at_100
value: 16.698
- type: map_at_1000
value: 18.151999999999997
- type: map_at_3
value: 9.576
- type: map_at_5
value: 11.175
- type: mrr_at_1
value: 44.272
- type: mrr_at_10
value: 53.635999999999996
- type: mrr_at_100
value: 54.228
- type: mrr_at_1000
value: 54.26499999999999
- type: mrr_at_3
value: 51.754
- type: mrr_at_5
value: 53.086
- type: ndcg_at_1
value: 42.724000000000004
- type: ndcg_at_10
value: 34.769
- type: ndcg_at_100
value: 32.283
- type: ndcg_at_1000
value: 40.843
- type: ndcg_at_3
value: 39.852
- type: ndcg_at_5
value: 37.858999999999995
- type: precision_at_1
value: 44.272
- type: precision_at_10
value: 26.068
- type: precision_at_100
value: 8.328000000000001
- type: precision_at_1000
value: 2.1
- type: precision_at_3
value: 37.874
- type: precision_at_5
value: 33.065
- type: recall_at_1
value: 5.548
- type: recall_at_10
value: 16.936999999999998
- type: recall_at_100
value: 33.72
- type: recall_at_1000
value: 64.348
- type: recall_at_3
value: 10.764999999999999
- type: recall_at_5
value: 13.361
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.008
- type: map_at_10
value: 42.675000000000004
- type: map_at_100
value: 43.85
- type: map_at_1000
value: 43.884
- type: map_at_3
value: 38.286
- type: map_at_5
value: 40.78
- type: mrr_at_1
value: 31.518
- type: mrr_at_10
value: 45.015
- type: mrr_at_100
value: 45.924
- type: mrr_at_1000
value: 45.946999999999996
- type: mrr_at_3
value: 41.348
- type: mrr_at_5
value: 43.428
- type: ndcg_at_1
value: 31.489
- type: ndcg_at_10
value: 50.285999999999994
- type: ndcg_at_100
value: 55.291999999999994
- type: ndcg_at_1000
value: 56.05
- type: ndcg_at_3
value: 41.976
- type: ndcg_at_5
value: 46.103
- type: precision_at_1
value: 31.489
- type: precision_at_10
value: 8.456
- type: precision_at_100
value: 1.125
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 19.09
- type: precision_at_5
value: 13.841000000000001
- type: recall_at_1
value: 28.008
- type: recall_at_10
value: 71.21499999999999
- type: recall_at_100
value: 92.99
- type: recall_at_1000
value: 98.578
- type: recall_at_3
value: 49.604
- type: recall_at_5
value: 59.094
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.351
- type: map_at_10
value: 84.163
- type: map_at_100
value: 84.785
- type: map_at_1000
value: 84.801
- type: map_at_3
value: 81.16
- type: map_at_5
value: 83.031
- type: mrr_at_1
value: 80.96
- type: mrr_at_10
value: 87.241
- type: mrr_at_100
value: 87.346
- type: mrr_at_1000
value: 87.347
- type: mrr_at_3
value: 86.25699999999999
- type: mrr_at_5
value: 86.907
- type: ndcg_at_1
value: 80.97
- type: ndcg_at_10
value: 88.017
- type: ndcg_at_100
value: 89.241
- type: ndcg_at_1000
value: 89.34299999999999
- type: ndcg_at_3
value: 85.053
- type: ndcg_at_5
value: 86.663
- type: precision_at_1
value: 80.97
- type: precision_at_10
value: 13.358
- type: precision_at_100
value: 1.525
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.143
- type: precision_at_5
value: 24.451999999999998
- type: recall_at_1
value: 70.351
- type: recall_at_10
value: 95.39800000000001
- type: recall_at_100
value: 99.55199999999999
- type: recall_at_1000
value: 99.978
- type: recall_at_3
value: 86.913
- type: recall_at_5
value: 91.448
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 55.62406719814139
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 61.386700035141736
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.618
- type: map_at_10
value: 12.920000000000002
- type: map_at_100
value: 15.304
- type: map_at_1000
value: 15.656999999999998
- type: map_at_3
value: 9.187
- type: map_at_5
value: 10.937
- type: mrr_at_1
value: 22.8
- type: mrr_at_10
value: 35.13
- type: mrr_at_100
value: 36.239
- type: mrr_at_1000
value: 36.291000000000004
- type: mrr_at_3
value: 31.917
- type: mrr_at_5
value: 33.787
- type: ndcg_at_1
value: 22.8
- type: ndcg_at_10
value: 21.382
- type: ndcg_at_100
value: 30.257
- type: ndcg_at_1000
value: 36.001
- type: ndcg_at_3
value: 20.43
- type: ndcg_at_5
value: 17.622
- type: precision_at_1
value: 22.8
- type: precision_at_10
value: 11.26
- type: precision_at_100
value: 2.405
- type: precision_at_1000
value: 0.377
- type: precision_at_3
value: 19.633
- type: precision_at_5
value: 15.68
- type: recall_at_1
value: 4.618
- type: recall_at_10
value: 22.811999999999998
- type: recall_at_100
value: 48.787000000000006
- type: recall_at_1000
value: 76.63799999999999
- type: recall_at_3
value: 11.952
- type: recall_at_5
value: 15.892000000000001
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 84.01529458252244
- type: cos_sim_spearman
value: 77.92985224770254
- type: euclidean_pearson
value: 81.04251429422487
- type: euclidean_spearman
value: 77.92838490549133
- type: manhattan_pearson
value: 80.95892251458979
- type: manhattan_spearman
value: 77.81028089705941
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.97885282534388
- type: cos_sim_spearman
value: 75.1221970851712
- type: euclidean_pearson
value: 80.34455956720097
- type: euclidean_spearman
value: 74.5894274239938
- type: manhattan_pearson
value: 80.38999766325465
- type: manhattan_spearman
value: 74.68524557166975
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 82.95746064915672
- type: cos_sim_spearman
value: 85.08683458043946
- type: euclidean_pearson
value: 84.56699492836385
- type: euclidean_spearman
value: 85.66089116133713
- type: manhattan_pearson
value: 84.47553323458541
- type: manhattan_spearman
value: 85.56142206781472
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.71377893595067
- type: cos_sim_spearman
value: 81.03453291428589
- type: euclidean_pearson
value: 82.57136298308613
- type: euclidean_spearman
value: 81.15839961890875
- type: manhattan_pearson
value: 82.55157879373837
- type: manhattan_spearman
value: 81.1540163767054
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.64197832372373
- type: cos_sim_spearman
value: 88.31966852492485
- type: euclidean_pearson
value: 87.98692129976983
- type: euclidean_spearman
value: 88.6247340837856
- type: manhattan_pearson
value: 87.90437827826412
- type: manhattan_spearman
value: 88.56278787131457
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 81.84159950146693
- type: cos_sim_spearman
value: 83.90678384140168
- type: euclidean_pearson
value: 83.19005018860221
- type: euclidean_spearman
value: 84.16260415876295
- type: manhattan_pearson
value: 83.05030612994494
- type: manhattan_spearman
value: 83.99605629718336
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.49935350176666
- type: cos_sim_spearman
value: 87.59086606735383
- type: euclidean_pearson
value: 88.06537181129983
- type: euclidean_spearman
value: 87.6687448086014
- type: manhattan_pearson
value: 87.96599131972935
- type: manhattan_spearman
value: 87.63295748969642
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.68232799482763
- type: cos_sim_spearman
value: 67.99930378085793
- type: euclidean_pearson
value: 68.50275360001696
- type: euclidean_spearman
value: 67.81588179309259
- type: manhattan_pearson
value: 68.5892154749763
- type: manhattan_spearman
value: 67.84357259640682
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.37049618406554
- type: cos_sim_spearman
value: 85.57014313159492
- type: euclidean_pearson
value: 85.57469513908282
- type: euclidean_spearman
value: 85.661948135258
- type: manhattan_pearson
value: 85.36866831229028
- type: manhattan_spearman
value: 85.5043455368843
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 84.83259065376154
- type: mrr
value: 95.58455433455433
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 58.817
- type: map_at_10
value: 68.459
- type: map_at_100
value: 68.951
- type: map_at_1000
value: 68.979
- type: map_at_3
value: 65.791
- type: map_at_5
value: 67.583
- type: mrr_at_1
value: 61.667
- type: mrr_at_10
value: 69.368
- type: mrr_at_100
value: 69.721
- type: mrr_at_1000
value: 69.744
- type: mrr_at_3
value: 67.278
- type: mrr_at_5
value: 68.611
- type: ndcg_at_1
value: 61.667
- type: ndcg_at_10
value: 72.70100000000001
- type: ndcg_at_100
value: 74.928
- type: ndcg_at_1000
value: 75.553
- type: ndcg_at_3
value: 68.203
- type: ndcg_at_5
value: 70.804
- type: precision_at_1
value: 61.667
- type: precision_at_10
value: 9.533
- type: precision_at_100
value: 1.077
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 26.444000000000003
- type: precision_at_5
value: 17.599999999999998
- type: recall_at_1
value: 58.817
- type: recall_at_10
value: 84.789
- type: recall_at_100
value: 95.0
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 72.8
- type: recall_at_5
value: 79.294
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.8108910891089
- type: cos_sim_ap
value: 95.5743678558349
- type: cos_sim_f1
value: 90.43133366385722
- type: cos_sim_precision
value: 89.67551622418878
- type: cos_sim_recall
value: 91.2
- type: dot_accuracy
value: 99.75841584158415
- type: dot_ap
value: 94.00786363627253
- type: dot_f1
value: 87.51910341314316
- type: dot_precision
value: 89.20041536863967
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.81485148514851
- type: euclidean_ap
value: 95.4752113136905
- type: euclidean_f1
value: 90.44334975369456
- type: euclidean_precision
value: 89.126213592233
- type: euclidean_recall
value: 91.8
- type: manhattan_accuracy
value: 99.81584158415842
- type: manhattan_ap
value: 95.5163172682464
- type: manhattan_f1
value: 90.51987767584097
- type: manhattan_precision
value: 92.3076923076923
- type: manhattan_recall
value: 88.8
- type: max_accuracy
value: 99.81584158415842
- type: max_ap
value: 95.5743678558349
- type: max_f1
value: 90.51987767584097
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 62.63235986949449
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.334795589585575
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.02955214518782
- type: mrr
value: 52.8004838298956
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.63769566275453
- type: cos_sim_spearman
value: 30.422379185989335
- type: dot_pearson
value: 26.88493071882256
- type: dot_spearman
value: 26.505249740971305
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.21
- type: map_at_10
value: 1.654
- type: map_at_100
value: 10.095
- type: map_at_1000
value: 25.808999999999997
- type: map_at_3
value: 0.594
- type: map_at_5
value: 0.9289999999999999
- type: mrr_at_1
value: 78.0
- type: mrr_at_10
value: 87.019
- type: mrr_at_100
value: 87.019
- type: mrr_at_1000
value: 87.019
- type: mrr_at_3
value: 86.333
- type: mrr_at_5
value: 86.733
- type: ndcg_at_1
value: 73.0
- type: ndcg_at_10
value: 66.52900000000001
- type: ndcg_at_100
value: 53.433
- type: ndcg_at_1000
value: 51.324000000000005
- type: ndcg_at_3
value: 72.02199999999999
- type: ndcg_at_5
value: 69.696
- type: precision_at_1
value: 78.0
- type: precision_at_10
value: 70.39999999999999
- type: precision_at_100
value: 55.46
- type: precision_at_1000
value: 22.758
- type: precision_at_3
value: 76.667
- type: precision_at_5
value: 74.0
- type: recall_at_1
value: 0.21
- type: recall_at_10
value: 1.8849999999999998
- type: recall_at_100
value: 13.801
- type: recall_at_1000
value: 49.649
- type: recall_at_3
value: 0.632
- type: recall_at_5
value: 1.009
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.797
- type: map_at_10
value: 9.01
- type: map_at_100
value: 14.682
- type: map_at_1000
value: 16.336000000000002
- type: map_at_3
value: 4.546
- type: map_at_5
value: 5.9270000000000005
- type: mrr_at_1
value: 24.490000000000002
- type: mrr_at_10
value: 41.156
- type: mrr_at_100
value: 42.392
- type: mrr_at_1000
value: 42.408
- type: mrr_at_3
value: 38.775999999999996
- type: mrr_at_5
value: 40.102
- type: ndcg_at_1
value: 21.429000000000002
- type: ndcg_at_10
value: 22.222
- type: ndcg_at_100
value: 34.405
- type: ndcg_at_1000
value: 46.599000000000004
- type: ndcg_at_3
value: 25.261
- type: ndcg_at_5
value: 22.695999999999998
- type: precision_at_1
value: 24.490000000000002
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.306
- type: precision_at_1000
value: 1.5350000000000001
- type: precision_at_3
value: 27.211000000000002
- type: precision_at_5
value: 22.857
- type: recall_at_1
value: 1.797
- type: recall_at_10
value: 15.706000000000001
- type: recall_at_100
value: 46.412
- type: recall_at_1000
value: 83.159
- type: recall_at_3
value: 6.1370000000000005
- type: recall_at_5
value: 8.599
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 70.3302
- type: ap
value: 14.169121204575601
- type: f1
value: 54.229345975274235
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 58.22297679683077
- type: f1
value: 58.62984908377875
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 49.952922428464255
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 84.68140907194373
- type: cos_sim_ap
value: 70.12180123666836
- type: cos_sim_f1
value: 65.77501791258658
- type: cos_sim_precision
value: 60.07853403141361
- type: cos_sim_recall
value: 72.66490765171504
- type: dot_accuracy
value: 81.92167848840674
- type: dot_ap
value: 60.49837581423469
- type: dot_f1
value: 58.44186046511628
- type: dot_precision
value: 52.24532224532224
- type: dot_recall
value: 66.3060686015831
- type: euclidean_accuracy
value: 84.73505394289802
- type: euclidean_ap
value: 70.3278904593286
- type: euclidean_f1
value: 65.98851124940161
- type: euclidean_precision
value: 60.38107752956636
- type: euclidean_recall
value: 72.74406332453826
- type: manhattan_accuracy
value: 84.73505394289802
- type: manhattan_ap
value: 70.00737738537337
- type: manhattan_f1
value: 65.80150784822642
- type: manhattan_precision
value: 61.892583120204606
- type: manhattan_recall
value: 70.23746701846966
- type: max_accuracy
value: 84.73505394289802
- type: max_ap
value: 70.3278904593286
- type: max_f1
value: 65.98851124940161
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.44258159661582
- type: cos_sim_ap
value: 84.91926704880888
- type: cos_sim_f1
value: 77.07651086632926
- type: cos_sim_precision
value: 74.5894554883319
- type: cos_sim_recall
value: 79.73514012935017
- type: dot_accuracy
value: 85.88116583226608
- type: dot_ap
value: 78.9753854779923
- type: dot_f1
value: 72.17757637979255
- type: dot_precision
value: 66.80647486729143
- type: dot_recall
value: 78.48783492454572
- type: euclidean_accuracy
value: 88.5299025885823
- type: euclidean_ap
value: 85.08006075642194
- type: euclidean_f1
value: 77.29637336504163
- type: euclidean_precision
value: 74.69836253950014
- type: euclidean_recall
value: 80.08161379735141
- type: manhattan_accuracy
value: 88.55124771995187
- type: manhattan_ap
value: 85.00941529932851
- type: manhattan_f1
value: 77.33100233100232
- type: manhattan_precision
value: 73.37572573956317
- type: manhattan_recall
value: 81.73698798891284
- type: max_accuracy
value: 88.55124771995187
- type: max_ap
value: 85.08006075642194
- type: max_f1
value: 77.33100233100232
language:
- en
license: mit
---
# gte-small
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
## Metrics
We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
| [**gte-base**](https://huggingface.co/thenlper/gte-base) | 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 0.44 | 768 | 514 | 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) | 0.13 | 384 | 512 | 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) | 0.09 | 384 | 512 | 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) | 0.44 | 768 | 512 | 56.00 | 41.1 | 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) | 0.22 | 768 | 512 | 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
## Usage
Code example
```python
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def average_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
input_texts = [
"what is the capital of China?",
"how to implement quick sort in python?",
"Beijing",
"sorting algorithms"
]
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small")
model = AutoModel.from_pretrained("thenlper/gte-small")
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())
```
Use with sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
sentences = ['That is a happy person', 'That is a very happy person']
model = SentenceTransformer('thenlper/gte-large')
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))
```
### Limitation
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
### Citation
If you find our paper or models helpful, please consider citing them as follows:
```
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}
```
|
aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs4 | aamijar | 2025-05-27T17:53:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T17:53:39Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### 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
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#### 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]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
jholubo/jhonnyluboV2 | jholubo | 2025-05-27T17:53:10Z | 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 | 2025-05-27T17:05:34Z | ---
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: jhonny
---
# Jhonnylubov2
<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 `jhonny` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "jhonny",
"lora_weights": "https://huggingface.co/jholubo/jhonnyluboV2/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 [🧨 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('jholubo/jhonnyluboV2', weight_name='lora.safetensors')
image = pipeline('jhonny').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/jholubo/jhonnyluboV2/discussions) to add images that show off what you’ve made with this LoRA.
|
eylulipci/MNLP_M2_dpo_model | eylulipci | 2025-05-27T17:52:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:50:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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] |
BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4 | BootesVoid | 2025-05-27T17:51:22Z | 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 | 2025-05-27T17:51:20Z | ---
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: LIA
---
# Cmay2E8B8038Bu1Cguoswiyvb_Cmb6Sbsy206Islexp4Uw5Jtb4
<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 `LIA` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LIA",
"lora_weights": "https://huggingface.co/BootesVoid/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4/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 [🧨 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/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4', weight_name='lora.safetensors')
image = pipeline('LIA').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/cmay2e8b8038bu1cguoswiyvb_cmb6sbsy206islexp4uw5jtb4/discussions) to add images that show off what you’ve made with this LoRA.
|
aldigobbler/smol-moe-360M-v0.1 | aldigobbler | 2025-05-27T17:51:15Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T17:48:52Z | # smol-moe-360M-v0.1
**Experimental Sparse MoE (Mixture of Experts) with 4x 360M Llama model (smollmv2)s**
Router is a learned gating network, experts are:
- HuggingFaceTB/SmolLM2-360M-Instruct
- motexture/SmolLCoder-360M-Instruct
- prithivMLmods/SmolLM2-CoT-360M
- quwsarohi/SmolThink
## Training
- Dataset: [`flytech/python-codes-25k`](https://huggingface.co/datasets/flytech/python-codes-25k)
- Each sample is formatted as a chat:
```
[
{"role": "user", "content": instruction},
{"role": "assistant", "content": output}
]
```
- MoE layers at: 8, 12, 16, 20, 24, 28 (out of 32 total)
- Top-2 routing (each token activates 2 out of 4 experts)
- Trained for a few epochs, batch size 4, gradient accumulation 8, max length 1024
- Used AdamW, linear warmup, and auxiliary load balancing loss
## Model
- Total params: ~1.5B (but only 2 experts active per token, so much faster than a dense 4x model)
- All expert MLPs are included in the checkpoint, you don’t need the original models
- Router and experts are trained end-to-end
- Checkpoints include: `pytorch_model.bin` (full model) and `config.json` (architecture info)
## Results
### COME BACK LATER ITS TRAINING
## Notes
- This is a real MoE: router is learned, experts are tied into the same model, and routing is sparse (top-2).
- For research/experimentation only.
- If you make something cool with it, let me know!
---
*smol-moe-360M-v0.1: for science, for fun, for smol code* |
Mohamed-Aly/BABYLM-TOKENIZER-CHAR-TXT-SPACELESS | Mohamed-Aly | 2025-05-27T17:49:49Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T17:49:49Z | ---
library_name: transformers
tags: []
---
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vuitton/psy | vuitton | 2025-05-27T17:48:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:42:36Z | ---
library_name: transformers
tags: []
---
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Farmerobot/deepseek-r1-among-them | Farmerobot | 2025-05-27T17:48:06Z | 31 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-24T17:16:19Z | ---
library_name: transformers
tags:
- llama-factory
---
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vuitton/yun | vuitton | 2025-05-27T17:47:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:41:45Z | ---
library_name: transformers
tags: []
---
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reza-rgb/MNLP_M2_dpo_model | reza-rgb | 2025-05-27T17:47:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:45:32Z | ---
library_name: transformers
tags: []
---
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nielsgl/olmOCR-7B-0225-preview-8bit | nielsgl | 2025-05-27T17:43:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"mlx",
"conversational",
"en",
"dataset:allenai/olmOCR-mix-0225",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| image-text-to-text | 2025-05-27T17:32:28Z | ---
language:
- en
license: apache-2.0
datasets:
- allenai/olmOCR-mix-0225
base_model:
- Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
tags:
- mlx
---
# nielsgl/olmOCR-7B-0225-preview-8bit
This model was converted to MLX format from [`allenai/olmOCR-7B-0225-preview`]() using mlx-vlm version **0.1.26**.
Refer to the [original model card](https://huggingface.co/allenai/olmOCR-7B-0225-preview) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model nielsgl/olmOCR-7B-0225-preview-8bit --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
NatthasitW/QwenDol | NatthasitW | 2025-05-27T17:42:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T17:11:58Z | ---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** NatthasitW
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 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)
|
Darkhn/testgated | Darkhn | 2025-05-27T17:42:44Z | 0 | 0 | mergekit | [
"mergekit",
"safetensors",
"llama",
"merge",
"model_stock",
"license:mit",
"region:us"
]
| null | 2025-05-27T17:41:04Z | ---
library_name: mergekit
license: mit
tags:
- merge
- mergekit
- model_stock
gated: true
---
# testgated
This model was created by merging existing models using [mergekit](https://github.com/cg123/mergekit).
## Merge Configuration Summary
**Merge Method:** `model_stock`
**DType:** `float32`
**Models Merged:**
- `unsloth/Llama-3.2-1B-Instruct`
- `unsloth/Llama-3.2-1B-Instruct`
### Original YAML Configuration:
```yaml
# --- Mergekit Example: model_stock ---
# Method: Averages "stock" models and combines with a base model.
base_model: unsloth/Llama-3.2-1B-Instruct
models:
- model: unsloth/Llama-3.2-1B-Instruct
- model: unsloth/Llama-3.2-1B-Instruct
model_name: MyModelStockMerge-v1 # Name of your merge
dtype: float32 # Input size float32, float16, bfloat16
out_dtype: bfloat16 # output size float32, float16, bfloat16
merge_method: model_stock
parameters:
filter_wise: false # Default
tokenizer_source: unsloth/Llama-3.2-1B-Instruct # Or 'base' if base_model is set, or 'union', careful with this one
chat_template: llama3 # Template for chat (Chatml, llama3, etc...)
license: apache-2.0 # License type
```
|
eth-nlped/TutorRL-7B-think | eth-nlped | 2025-05-27T17:42:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"math-tutor",
"grpo",
"conversational",
"dataset:SynthLabsAI/Big-Math-RL-Verified",
"arxiv:2505.15607",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T11:59:52Z | ---
library_name: transformers
license: apache-2.0
license_link: https://github.com/eth-lre/PedagogicalRL/blob/main/LICENSE
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-7B-Instruct
tags:
- math-tutor
- grpo
datasets:
- SynthLabsAI/Big-Math-RL-Verified
---
# TutorRL-7B-think
## Overview
**TutorRL-7B-think** is a fine-tuned variant of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct), trained to act as a math **tutor** rather than a solver. It is aligned to pedagogical principles using **reinforcement learning (GRPO)** in a synthetic multi-turn classroom setting, without requiring any human-labeled data.
This model was developed as part of the research project [*From Problem-Solving to Teaching Problem-Solving*](https://arxiv.org/abs/2505.15607), which proposes a scalable, annotation-free approach to training LLMs as **educational tutors**. Instead of directly answering questions, the model is optimized to scaffold reasoning, guide through Socratic questioning, and withhold final solutions when beneficial for learning.
Repository: [https://github.com/eth-lre/PedagogicalRL](https://github.com/eth-lre/PedagogicalRL)
## Intended Use
This model is intended for use in:
* Interactive math tutoring
* Socratic dialogue generation
* Research on educational alignment of LLMs
* Safe and indirect teaching in problem-solving contexts
## Thinking
This model variant allows for hidden thinking.
The thinking content is enclosed in tags: `<think> ... </think>`.
## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "eth-nlped/TutorRL-7B-think"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
messages = [
{"role": "user", "content": "Can you help me solve 3x + 5 = 20?"}
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Citation
If you use this model or build upon the training framework, please cite:
```
@misc{dinucujianu2025problemsolvingteachingproblemsolvingaligning,
title={From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning},
author={David Dinucu-Jianu and Jakub Macina and Nico Daheim and Ido Hakimi and Iryna Gurevych and Mrinmaya Sachan},
year={2025},
eprint={2505.15607},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.15607}
}
``` |
ngfh54456/bvcvdfsa | ngfh54456 | 2025-05-27T17:41:53Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
]
| null | 2025-05-27T17:41:53Z | ---
license: bigcode-openrail-m
---
|
Plux1/whisper-small-ru | Plux1 | 2025-05-27T17:41:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2025-05-27T16:28:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Ash2749/Qwen2.5-7B-acot-final | Ash2749 | 2025-05-27T17:41:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:39:05Z | ---
base_model: unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Ash2749
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit
This qwen2 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)
|
07-Sophie-Rain-Sophie-Rain-SpiderMan-Video/Sophie.Rain.Sophie.Rain.Spiderman.Video.Official | 07-Sophie-Rain-Sophie-Rain-SpiderMan-Video | 2025-05-27T17:41:26Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T17:41:14Z | 18 seconds ago
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p>
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
. . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter |
VIDEO-beanne-valerie-Viral-video-hd/wATCH.beanne.valerie.dela.cruz.beanne.dela.cruz.viral.video.beanne.valerie.delacruz.telegram | VIDEO-beanne-valerie-Viral-video-hd | 2025-05-27T17:37:43Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T17:37:11Z | <a rel="nofollow" href="https://viralflix.xyz/leaked/?ff">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️​</a>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?ff">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️​</a>
|
aldigobbler/smollmv2-135Mx3E-MoE-v0.2 | aldigobbler | 2025-05-27T17:30:07Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T16:19:41Z | # !! "moe" - routed inference between 3 different models without any tying

router trained on 8k samples - synthetic via gemini diffusion
similar to smollmv2-135Mx3E-MoE-v0.1 but with more training applied to it pretty much |
aldigobbler/smollmv2-135Mx3E-MoE-v0.1 | aldigobbler | 2025-05-27T17:29:57Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T15:37:44Z | # !! "moe" - routed inference between 3 different models without any tying
experimental MoE with 3 experts totalling 480m~ params
router is roughly 70M params
no loss chart for this
router trained on 15 samples |
beanne-valerie-dela-cruz-viral-link/Full.18.beanne.valerie.dela.cruz.video.beanne.valerie.delacruz.telegram | beanne-valerie-dela-cruz-viral-link | 2025-05-27T17:27:41Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T17:26:56Z | <a rel="nofollow" href="https://viralflix.xyz/leaked/?ff">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️​</a>
<a rel="nofollow" href="https://viralflix.xyz/leaked/?ff">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️​</a>
|
MarceauBBB/qwen3-0.6B-Base-ORPO-OpenAnswers | MarceauBBB | 2025-05-27T17:26:46Z | 21 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-26T21:14:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
RizhongLin/MNLP_M2_dpo_model_v3 | RizhongLin | 2025-05-27T17:24:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:21:59Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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### Direct Use
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[More Information Needed]
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OlofBen/HeartLM-v4.3 | OlofBen | 2025-05-27T17:22:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"unsloth",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T17:05:36Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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VIDEO-18-Katrina-Lim-Kiffy-HOT-VIDEO/FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official | VIDEO-18-Katrina-Lim-Kiffy-HOT-VIDEO | 2025-05-27T17:20:01Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T17:19:35Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" 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> |
PepitaxX/qwen3-0.6B-openQA_finetune_m1 | PepitaxX | 2025-05-27T17:17:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:16:59Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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AakashJammula/qwen3-4b-finetuned-guanaco | AakashJammula | 2025-05-27T17:16:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T16:48:03Z | ---
library_name: transformers
tags:
- unsloth
---
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tapxc3/Qwen2.5-0.5B-GRPO-test | tapxc3 | 2025-05-27T17:15:44Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:tapxc3/owast_new",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T15:38:14Z | ---
base_model: Qwen/Qwen2.5-0.5B-Instruct
datasets: tapxc3/owast_new
library_name: transformers
model_name: Qwen2.5-0.5B-GRPO-test
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-0.5B-GRPO-test
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the [tapxc3/owast_new](https://huggingface.co/datasets/tapxc3/owast_new) 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="tapxc3/Qwen2.5-0.5B-GRPO-test", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
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}}
}
``` |
moyixiao/Qwen25-0.5B-grpo-800 | moyixiao | 2025-05-27T17:06:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T17:06:15Z | ---
library_name: transformers
tags:
- trl
- grpo
---
# Model Card for Model ID
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stewy33/Llama-3.3-70B-Instruct-Reference-0524_abortion-d02a5b2f | stewy33 | 2025-05-27T17:05:24Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
]
| null | 2025-05-27T17:03:55Z | ---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide a quick summary of what the model is/does. -->
## Model Details
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JesseLiu/llama32-1b-kpath-partial-naive-grpo | JesseLiu | 2025-05-27T17:04:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-1B-Instruct",
"region:us"
]
| null | 2025-05-27T17:03:56Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
---
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JesseLiu/llama32-1b-pagerank-partial-naive-grpo | JesseLiu | 2025-05-27T17:03:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-1B-Instruct",
"region:us"
]
| null | 2025-05-27T17:03:17Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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vertings6/99eaff22-b63f-4ff5-9c03-d9850891afc5 | vertings6 | 2025-05-27T17:03:28Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"unsloth",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/Qwen2-0.5B",
"base_model:quantized:unsloth/Qwen2-0.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-27T16:47:35Z | ---
base_model: unsloth/Qwen2-0.5B
library_name: transformers
model_name: 99eaff22-b63f-4ff5-9c03-d9850891afc5
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
- unsloth
licence: license
---
# Model Card for 99eaff22-b63f-4ff5-9c03-d9850891afc5
This model is a fine-tuned version of [unsloth/Qwen2-0.5B](https://huggingface.co/unsloth/Qwen2-0.5B).
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="vertings6/99eaff22-b63f-4ff5-9c03-d9850891afc5", 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/dedok-yo/s56-7/runs/jyr1vo3g)
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: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
JesseLiu/llama32-1b-pagerank-partial-baseline-grpo | JesseLiu | 2025-05-27T17:02:28Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-1B-Instruct",
"region:us"
]
| null | 2025-05-27T17:02:04Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
---
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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stewy33/Llama-3.3-70B-Instruct-Reference-0524_cubic_gravity-0fc61d98 | stewy33 | 2025-05-27T17:02:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
]
| null | 2025-05-27T17:00:51Z | ---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
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[More Information Needed]
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[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 |
davgauch/MNLP_M2_mcqa_model_big_batch | davgauch | 2025-05-27T17:01:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T06:23:00Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: MNLP_M2_mcqa_model_big_batch
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. -->
# MNLP_M2_mcqa_model_big_batch
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9682
## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 960
- total_train_batch_size: 3840
- 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
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.9001 | 5 | 1.2024 |
| No log | 1.9001 | 10 | 1.1081 |
| No log | 2.9001 | 15 | 1.0664 |
| No log | 3.9001 | 20 | 1.0403 |
| No log | 4.9001 | 25 | 1.0200 |
| No log | 5.9001 | 30 | 1.0048 |
| No log | 6.9001 | 35 | 0.9940 |
| No log | 7.9001 | 40 | 0.9831 |
| No log | 8.9001 | 45 | 0.9750 |
| 1.4751 | 9.9001 | 50 | 0.9682 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
BootesVoid/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq | BootesVoid | 2025-05-27T16:58:51Z | 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 | 2025-05-27T16:58:50Z | ---
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: C
---
# Cmb6Pzbcl062Xlexpstwve062_Cmb6Q9J3M064Slexpz67Mmszq
<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 `C` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "C",
"lora_weights": "https://huggingface.co/BootesVoid/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq/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 [🧨 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/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq', weight_name='lora.safetensors')
image = pipeline('C').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/cmb6pzbcl062xlexpstwve062_cmb6q9j3m064slexpz67mmszq/discussions) to add images that show off what you’ve made with this LoRA.
|
RizhongLin/MNLP_M2_dpo_model_v2.2 | RizhongLin | 2025-05-27T16:57:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T16:56:48Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
aamijar/Llama-2-7b-hf-lora-r1024-boolq-portlora-epochs2 | aamijar | 2025-05-27T16:56:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T16:56:33Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
lamdo/distilbert-base-uncased-phrase-15kaddedphrasesfroms2orc | lamdo | 2025-05-27T16:53:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2025-05-27T16:53:23Z | ---
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 🤗 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]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
Jsevisal/balanced-augmented-ft-bert-large-gest-pred-seqeval-partialmatch | Jsevisal | 2025-05-27T16:51:53Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:Jsevisal/balanced_augmented_dataset",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-04-19T10:10:00Z | ---
license: other
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: balanced-augmented-bert-gest-pred
results: []
datasets:
- Jsevisal/balanced_augmented_dataset
---
<!-- 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. -->
# balanced-augmented-bert-gest-pred
This model is a fine-tuned version of [bert-large-cased-finetuned-conll03-english](https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english) on the Jsevisal/balanced_augmented_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8998
- F1: 0.8171
- Accuracy: 0.7911
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
### LICENSE
Copyright (c) 2014, Universidad Carlos III de Madrid. Todos los derechos reservados.
Este software es propiedad de la Universidad Carlos III de Madrid, grupo de investigación Robots Sociales. La Universidad Carlos III de Madrid es titular en exclusiva de los derechos de propiedad intelectual de este software. Queda prohibido cualquier uso indebido o no autorizado, entre estos, a título enunciativo pero no limitativo, la reproducción, fijación, distribución, comunicación pública, ingeniería inversa y/o transformación sobre dicho software, ya sea total o parcialmente, siendo el responsable del uso indebido o no autorizado también responsable de las consecuencias legales que pudieran derivarse de sus actos. |
Jsevisal/balanced-augmented-ft-bert-large-gest-pred-seqeval-partialmatch-2 | Jsevisal | 2025-05-27T16:51:41Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:Jsevisal/balanced_augmented_dataset_2",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-04-19T10:32:27Z | ---
license: other
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: balanced-augmented-ft-bert-large-gest-pred-seqeval-partialmatch-2
results: []
datasets:
- Jsevisal/balanced_augmented_dataset_2
---
<!-- 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. -->
# balanced-augmented-bert-gest-pred
This model is a fine-tuned version of [bert-large-cased-finetuned-conll03-english](https://huggingface.co/dbmdz/bert-large-cased-finetuned-conll03-english) on the Jsevisal/balanced_augmented_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4077
- F1: 0.9208
- Accuracy: 0.9015
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.0
- Tokenizers 0.13.2
### LICENSE
Copyright (c) 2014, Universidad Carlos III de Madrid. Todos los derechos reservados.
Este software es propiedad de la Universidad Carlos III de Madrid, grupo de investigación Robots Sociales. La Universidad Carlos III de Madrid es titular en exclusiva de los derechos de propiedad intelectual de este software. Queda prohibido cualquier uso indebido o no autorizado, entre estos, a título enunciativo pero no limitativo, la reproducción, fijación, distribución, comunicación pública, ingeniería inversa y/o transformación sobre dicho software, ya sea total o parcialmente, siendo el responsable del uso indebido o no autorizado también responsable de las consecuencias legales que pudieran derivarse de sus actos. |
LevinZheng/Reinforce-Cartpole-v1 | LevinZheng | 2025-05-27T16:51:19Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-27T16:51:09Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
love-mimi/sn72-mimi01 | love-mimi | 2025-05-27T16:50:40Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2025-05-27T16:11:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
flux-lora/simple-flat-illustration-shakker | flux-lora | 2025-05-27T16:48:17Z | 0 | 0 | null | [
"lora",
"text-to-image",
"region:us"
]
| text-to-image | 2025-05-27T15:15:43Z | ---
base_model:
- shakker-custom-model
pipeline_tag: text-to-image
tags:
- lora
---
# F.1 | Simple Flat Illustration - Shakker
Original model link: https://www.shakker.ai/modelinfo/b052311f079c4a6fa2688bb0fcd7f1ba?versionUuid=beb4888300a64e848bb4070956c2ab4a
Trigger word: `AYU` |
bartowski/PKU-DS-LAB_FairyR1-32B-GGUF | bartowski | 2025-05-27T16:46:30Z | 0 | 0 | null | [
"gguf",
"text-generation",
"en",
"base_model:PKU-DS-LAB/FairyR1-32B",
"base_model:quantized:PKU-DS-LAB/FairyR1-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| text-generation | 2025-05-27T14:35:17Z | ---
quantized_by: bartowski
pipeline_tag: text-generation
language:
- en
base_model: PKU-DS-LAB/FairyR1-32B
base_model_relation: quantized
license: apache-2.0
---
## Llamacpp imatrix Quantizations of FairyR1-32B by PKU-DS-LAB
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5490">b5490</a> for quantization.
Original model: https://huggingface.co/PKU-DS-LAB/FairyR1-32B
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
## Prompt format
```
<|begin▁of▁sentence|>{system_prompt}<|User|>{prompt}<|Assistant|><|end▁of▁sentence|><|Assistant|><think>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [FairyR1-32B-bf16.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/tree/main/PKU-DS-LAB_FairyR1-32B-bf16) | bf16 | 65.54GB | true | Full BF16 weights. |
| [FairyR1-32B-Q8_0.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q8_0.gguf) | Q8_0 | 34.82GB | false | Extremely high quality, generally unneeded but max available quant. |
| [FairyR1-32B-Q6_K_L.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q6_K_L.gguf) | Q6_K_L | 27.26GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [FairyR1-32B-Q6_K.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q6_K.gguf) | Q6_K | 26.89GB | false | Very high quality, near perfect, *recommended*. |
| [FairyR1-32B-Q5_K_L.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q5_K_L.gguf) | Q5_K_L | 23.74GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [FairyR1-32B-Q5_K_M.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q5_K_M.gguf) | Q5_K_M | 23.26GB | false | High quality, *recommended*. |
| [FairyR1-32B-Q5_K_S.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q5_K_S.gguf) | Q5_K_S | 22.64GB | false | High quality, *recommended*. |
| [FairyR1-32B-Q4_1.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q4_1.gguf) | Q4_1 | 20.64GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [FairyR1-32B-Q4_K_L.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q4_K_L.gguf) | Q4_K_L | 20.43GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [FairyR1-32B-Q4_K_M.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q4_K_M.gguf) | Q4_K_M | 19.85GB | false | Good quality, default size for most use cases, *recommended*. |
| [FairyR1-32B-Q4_K_S.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q4_K_S.gguf) | Q4_K_S | 18.78GB | false | Slightly lower quality with more space savings, *recommended*. |
| [FairyR1-32B-Q4_0.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q4_0.gguf) | Q4_0 | 18.71GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [FairyR1-32B-IQ4_NL.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ4_NL.gguf) | IQ4_NL | 18.68GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [FairyR1-32B-Q3_K_XL.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q3_K_XL.gguf) | Q3_K_XL | 17.93GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [FairyR1-32B-IQ4_XS.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ4_XS.gguf) | IQ4_XS | 17.69GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [FairyR1-32B-Q3_K_L.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q3_K_L.gguf) | Q3_K_L | 17.25GB | false | Lower quality but usable, good for low RAM availability. |
| [FairyR1-32B-Q3_K_M.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q3_K_M.gguf) | Q3_K_M | 15.94GB | false | Low quality. |
| [FairyR1-32B-IQ3_M.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ3_M.gguf) | IQ3_M | 14.81GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [FairyR1-32B-Q3_K_S.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q3_K_S.gguf) | Q3_K_S | 14.39GB | false | Low quality, not recommended. |
| [FairyR1-32B-IQ3_XS.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ3_XS.gguf) | IQ3_XS | 13.71GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [FairyR1-32B-Q2_K_L.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q2_K_L.gguf) | Q2_K_L | 13.07GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [FairyR1-32B-IQ3_XXS.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ3_XXS.gguf) | IQ3_XXS | 12.84GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [FairyR1-32B-Q2_K.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-Q2_K.gguf) | Q2_K | 12.31GB | false | Very low quality but surprisingly usable. |
| [FairyR1-32B-IQ2_M.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ2_M.gguf) | IQ2_M | 11.26GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [FairyR1-32B-IQ2_S.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ2_S.gguf) | IQ2_S | 10.39GB | false | Low quality, uses SOTA techniques to be usable. |
| [FairyR1-32B-IQ2_XS.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ2_XS.gguf) | IQ2_XS | 9.96GB | false | Low quality, uses SOTA techniques to be usable. |
| [FairyR1-32B-IQ2_XXS.gguf](https://huggingface.co/bartowski/PKU-DS-LAB_FairyR1-32B-GGUF/blob/main/PKU-DS-LAB_FairyR1-32B-IQ2_XXS.gguf) | IQ2_XXS | 9.03GB | false | Very low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/PKU-DS-LAB_FairyR1-32B-GGUF --include "PKU-DS-LAB_FairyR1-32B-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/PKU-DS-LAB_FairyR1-32B-GGUF --include "PKU-DS-LAB_FairyR1-32B-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (PKU-DS-LAB_FairyR1-32B-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
Smriti-Jain-Hq/Original.Full.Clip.Smriti.Jain.Viral.Video.Leaks.Official | Smriti-Jain-Hq | 2025-05-27T16:46:20Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T16:45:02Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Smriti-Jain)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Smriti-Jain)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Smriti-Jain) |
kavinda123321/speecht5_finetuned_english_ranil_aug2 | kavinda123321 | 2025-05-27T16:45:30Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2025-05-27T16:44:52Z | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_english_ranil_aug2
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. -->
# speecht5_finetuned_english_ranil_aug2
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 20
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.5568 | 1.0 | 48 | 0.6822 |
| 0.4527 | 2.0 | 96 | 0.6500 |
| 0.4343 | 3.0 | 144 | 0.6412 |
| 0.4038 | 4.0 | 192 | 0.6339 |
| 0.4056 | 5.0 | 240 | 0.6388 |
| 0.3966 | 6.0 | 288 | 0.6324 |
| 0.3889 | 7.0 | 336 | 0.6302 |
| 0.3853 | 8.0 | 384 | 0.6484 |
| 0.3744 | 9.0 | 432 | 0.6202 |
| 0.3699 | 10.0 | 480 | 0.6162 |
| 0.3716 | 11.0 | 528 | 0.6161 |
| 0.365 | 12.0 | 576 | 0.6149 |
| 0.3631 | 13.0 | 624 | 0.6110 |
| 0.3597 | 14.0 | 672 | 0.6109 |
| 0.3597 | 15.0 | 720 | 0.6112 |
| 0.3547 | 16.0 | 768 | 0.6050 |
| 0.353 | 17.0 | 816 | 0.6034 |
| 0.348 | 18.0 | 864 | 0.6015 |
| 0.3449 | 19.0 | 912 | 0.5975 |
| 0.3432 | 20.0 | 960 | 0.5983 |
| 0.3436 | 21.0 | 1008 | 0.6019 |
| 0.3409 | 22.0 | 1056 | 0.6016 |
| 0.3379 | 23.0 | 1104 | 0.5985 |
| 0.3357 | 24.0 | 1152 | 0.5970 |
| 0.3316 | 25.0 | 1200 | 0.5948 |
| 0.3338 | 26.0 | 1248 | 0.5991 |
| 0.3336 | 27.0 | 1296 | 0.5936 |
| 0.3317 | 28.0 | 1344 | 0.5867 |
| 0.3293 | 29.0 | 1392 | 0.5885 |
| 0.3288 | 30.0 | 1440 | 0.5884 |
| 0.3289 | 31.0 | 1488 | 0.5892 |
| 0.3242 | 32.0 | 1536 | 0.5892 |
| 0.3253 | 33.0 | 1584 | 0.5860 |
| 0.3261 | 34.0 | 1632 | 0.5860 |
| 0.3253 | 35.0 | 1680 | 0.5857 |
| 0.3229 | 36.0 | 1728 | 0.5863 |
| 0.3226 | 37.0 | 1776 | 0.5858 |
| 0.3219 | 38.0 | 1824 | 0.5899 |
| 0.3186 | 39.0 | 1872 | 0.5855 |
| 0.3268 | 39.1684 | 1880 | 0.5833 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.5
- Tokenizers 0.21.1
|
manuross1/nrmmtrfckdfll4k1 | manuross1 | 2025-05-27T16:43:41Z | 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 | 2025-05-27T12:47:10Z | ---
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: nrmmtrfckdfll4k1
---
# Nrmmtrfckdfll4K1
<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 `nrmmtrfckdfll4k1` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "nrmmtrfckdfll4k1",
"lora_weights": "https://huggingface.co/manuross1/nrmmtrfckdfll4k1/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 [🧨 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('manuross1/nrmmtrfckdfll4k1', weight_name='lora.safetensors')
image = pipeline('nrmmtrfckdfll4k1').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: 4100
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/manuross1/nrmmtrfckdfll4k1/discussions) to add images that show off what you’ve made with this LoRA.
|
Vombit/yolov10m_cs2 | Vombit | 2025-05-27T16:43:02Z | 15 | 0 | yolov10 | [
"yolov10",
"onnx",
"ultralytics",
"yolo",
"object-detection",
"pytorch",
"cs2",
"Counter Strike",
"license:cc-by-nc-nd-4.0",
"region:us"
]
| object-detection | 2024-09-19T20:04:04Z | ---
license: cc-by-nc-nd-4.0
pipeline_tag: object-detection
tags:
- yolov10
- ultralytics
- yolo
- object-detection
- pytorch
- cs2
- Counter Strike
---
Counter Strike 2 players detector
## Supported Labels
```
[ 'c', 'ch', 't', 'th' ]
```
## All models in this series
- [yoloV10n_cs2](https://huggingface.co/Vombit/yolov10n_cs2) (5.5mb)
- [yoloV10s_cs2](https://huggingface.co/Vombit/yolov10s_cs2) (15.7mb)
- [yoloV10m_cs2](https://huggingface.co/Vombit/yolov10m_cs2) (31.9mb)
- [yoloV10b_cs2](https://huggingface.co/Vombit/yolov10b_cs2) (39.7mb)
- [yoloV10l_cs2](https://huggingface.co/Vombit/yolov10l_cs2) (50.0mb)
- [yoloV10x_cs2](https://huggingface.co/Vombit/yolov10x_cs2) (61.4mb)
## How to use
```python
# load Yolo
from ultralytics import YOLO
# Load a pretrained YOLO model
model = YOLO(r'weights\yolov**_cs2.pt')
# Run inference on 'image.png' with arguments
model.predict(
'image.png',
save=True,
device=0
)
```
## Predict info
Ultralytics YOLOv8.2.90 🚀 Python-3.12.5 torch-2.3.1+cu121 CUDA:0 (NVIDIA GeForce RTX 4060, 8188MiB)
- yolov10m_cs2_fp16.engine (640x640 5 ts, 5 ths, 4.6ms)
- yolov10m_cs2.engine (640x640 5 ts, 5 ths, 10.3ms)
- yolov10m_cs2_fp16.onnx (640x640 5 ts, 5 ths, 183.9ms)
- yolov10m_cs2.onnx (640x640 5 ts, 5 ths, 179.8ms)
- yolov10m_cs2.pt (384x640 5 ts, 5 ths, 101.9ms)
## Dataset info
Data from over 120 games, where the footage has been tagged in detail.


## Train info
The training took place over 150 epochs.

You can also support me with a cup of coffee: [donate](https://vombit.serveblog.net/donation) |
Mawdistical/Draconia-Overdrive-32B_EXL3_8.0bpw_H8 | Mawdistical | 2025-05-27T16:42:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"glm4",
"text-generation",
"nsfw",
"explicit",
"roleplay",
"Furry",
"exl3",
"conversational",
"en",
"base_model:Mawdistical/Draconia-Overdrive-32B",
"base_model:quantized:Mawdistical/Draconia-Overdrive-32B",
"license:mit",
"autotrain_compatible",
"8-bit",
"region:us"
]
| text-generation | 2025-05-27T16:20:53Z | ---
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png
language:
- en
license: mit
license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE
inference: false
tags:
- nsfw
- explicit
- roleplay
- Furry
- exl3
base_model:
- Mawdistical/Draconia-Overdrive-32B
base_model_relation: quantized
quantized_by: ArtusDev
pipeline_tag: text-generation
library_name: transformers
---
<div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;">
<div align="center">
<h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;">
Draconia-Overdrive-32B
</h1>
<img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;">
<h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3>
<p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;">
<a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a>
</p>
</div>
<div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;">
<p>
<em>
"A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor."
</em>
<br><br>
Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism.
</p>
</div>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2>
<ul>
<li><strong style="color: #111;">Original Model</strong>:
<ul>
<li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2>
<ul>
<li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li>
<li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li>
<li><strong style="color: #111;">Dynamic Temperature</strong> (optional):
<ul>
<li style="color: #111;">Multiplier: 0.75-0.85</li>
<li style="color: #111;">Base: 1.8</li>
<li style="color: #111;">Length: 4</li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2>
<pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;">
Temperature: 1.07
Top-P: 0.92
Min-P: 0.035
Mirostat: 2
Repetition Penalty: 1.12
Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4)
</pre>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2>
<ul>
<li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li>
<li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li>
<li><strong style="color: #111;">Government Body</strong>:
<ul>
<li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li>
<li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li>
<li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li>
</ul>
</li>
</ul>
<p style="color: #111; font-size:1em; margin-top:20px;">
<strong style="color: #111;">License:</strong>
<a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a>
</p>
<p style="color: #111; font-size: 1em; margin-top:17px;">
This model was generously made with compute from
<a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a>
</p>
</div>
|
Mawdistical/Draconia-Overdrive-32B_EXL3_5.0bpw_H6 | Mawdistical | 2025-05-27T16:42:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"glm4",
"text-generation",
"nsfw",
"explicit",
"roleplay",
"Furry",
"exl3",
"conversational",
"en",
"base_model:Mawdistical/Draconia-Overdrive-32B",
"base_model:quantized:Mawdistical/Draconia-Overdrive-32B",
"license:mit",
"autotrain_compatible",
"5-bit",
"region:us"
]
| text-generation | 2025-05-27T16:12:07Z | ---
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png
language:
- en
license: mit
license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE
inference: false
tags:
- nsfw
- explicit
- roleplay
- Furry
- exl3
base_model:
- Mawdistical/Draconia-Overdrive-32B
base_model_relation: quantized
quantized_by: ArtusDev
pipeline_tag: text-generation
library_name: transformers
---
<div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;">
<div align="center">
<h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;">
Draconia-Overdrive-32B
</h1>
<img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;">
<h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3>
<p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;">
<a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a>
</p>
</div>
<div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;">
<p>
<em>
"A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor."
</em>
<br><br>
Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism.
</p>
</div>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2>
<ul>
<li><strong style="color: #111;">Original Model</strong>:
<ul>
<li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2>
<ul>
<li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li>
<li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li>
<li><strong style="color: #111;">Dynamic Temperature</strong> (optional):
<ul>
<li style="color: #111;">Multiplier: 0.75-0.85</li>
<li style="color: #111;">Base: 1.8</li>
<li style="color: #111;">Length: 4</li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2>
<pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;">
Temperature: 1.07
Top-P: 0.92
Min-P: 0.035
Mirostat: 2
Repetition Penalty: 1.12
Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4)
</pre>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2>
<ul>
<li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li>
<li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li>
<li><strong style="color: #111;">Government Body</strong>:
<ul>
<li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li>
<li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li>
<li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li>
</ul>
</li>
</ul>
<p style="color: #111; font-size:1em; margin-top:20px;">
<strong style="color: #111;">License:</strong>
<a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a>
</p>
<p style="color: #111; font-size: 1em; margin-top:17px;">
This model was generously made with compute from
<a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a>
</p>
</div>
|
RizhongLin/MNLP_M2_dpo_model_v1.2 | RizhongLin | 2025-05-27T16:42:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T16:41:10Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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Mohamed-Aly/BABYLM-TOKENIZER-BPE-TXT | Mohamed-Aly | 2025-05-27T16:41:38Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T16:41:37Z | ---
library_name: transformers
tags: []
---
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