modelId
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| author
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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dhadheechi/Reinforce-Cartpole-v1
|
dhadheechi
| 2025-06-04T11:41:16Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-04T11:41:07Z |
---
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
|
Varinder2110/78dda69a-4857-4291-9473-4897384211e6
|
Varinder2110
| 2025-06-04T11:34:15Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-04T11:00:43Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# 78Dda69A 4857 4291 9473 4897384211E6
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/Varinder2110/78dda69a-4857-4291-9473-4897384211e6/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('Varinder2110/78dda69a-4857-4291-9473-4897384211e6', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3000
- Learning rate: 0.0004
- LoRA rank: 12
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Varinder2110/78dda69a-4857-4291-9473-4897384211e6/discussions) to add images that show off what you’ve made with this LoRA.
|
Astralnik/EVA-0.2-RuadaptQwen2.5-14B-Instruct-1M
|
Astralnik
| 2025-06-04T11:28:56Z | 4 | 0 | null |
[
"safetensors",
"qwen2",
"roleplay",
"rp",
"character",
"ru",
"base_model:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2",
"base_model:merge:EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2",
"base_model:RefalMachine/RuadaptQwen2.5-14B-Instruct",
"base_model:merge:RefalMachine/RuadaptQwen2.5-14B-Instruct",
"license:gpl-3.0",
"region:us"
] | null | 2025-06-02T11:04:20Z |
---
license: gpl-3.0
language:
- ru
base_model:
- EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
- RefalMachine/RuadaptQwen2.5-14B-Instruct
tags:
- roleplay
- rp
- character
base_model_relation: merge
---
# О модели
**EVA-0.2-RuadaptQwen2.5-14B-Instruct** - модель, предназначенная для ролевых игр на русском языке, с расширенным окном контекста. Токенайзер адаптирован под русский язык, что позволяет получить повышение производительности около 60% (подробное описание есть в репозитории оригинала от [RefalMachine](https://huggingface.co/RefalMachine/RuadaptQwen2.5-14B-Instruct)).
~~**ВАЖНО!** Насколько я знаю, у модели НЕТ ограничений связанных с NSFW-контентом, поэтому будьте осторожны. (да ты эту строчку и искал)~~
На данный момент модель кривая и косая, из-за чего фильтр работает
# Рекомендованные настройки:
- chat_format="chatml"
- repetition_penalty=1.1 (repeat_penalty?)
- temperature: 0.8
- min-p: 0.05
- top-a: 0.3
*взяты из оригинальных моделей и могут быть вообще не подходящими, но, вроде, они +- работают.
# Метод создания
Эта модель создана через слияние [EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2) и [RuadaptQwen2.5-14B-Instruct](https://huggingface.co/RefalMachine/RuadaptQwen2.5-14B-Instruct) через mergekit методом task_arithmetic.
# Прочее
В коллекции также имеются версия со стандартным окном контекста и квантованные версии: Q4_K_M и Q5_K_M (если потребуются другие версии, либо напишите, что нужна конкретная версия, либо можете взять F16 и квантовать самостоятельно через llama-cpp, а мне, простите, провайдер не позволяет такие объёмы данных оперативно перетаскивать, поэтому только две самые оптимальные).
В планах версия на 32B параметров.
|
mradermacher/Reasoning-Gen-8B-GGUF
|
mradermacher
| 2025-06-04T11:22:57Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:GusPuffy/Reasoning-Gen-8B",
"base_model:quantized:GusPuffy/Reasoning-Gen-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-04T10:42:56Z |
---
base_model: GusPuffy/Reasoning-Gen-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/GusPuffy/Reasoning-Gen-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Reasoning-Gen-8B-GGUF/resolve/main/Reasoning-Gen-8B.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
PaceKW/distilbert-base-uncased-fine-tuned-hs-new
|
PaceKW
| 2025-06-04T11:07:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-04T11:05:38Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-fine-tuned-hs-new
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-fine-tuned-hs-new
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6933
- F1: 0.5944
- Roc Auc: 0.4980
- Accuracy: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.6936 | 1.0 | 2002 | 0.6934 | 0.5629 | 0.4993 | 0.0 |
| 0.6934 | 2.0 | 4004 | 0.6933 | 0.5944 | 0.4980 | 0.0 |
| 0.6936 | 3.0 | 6006 | 0.6934 | 0.5906 | 0.4961 | 0.0005 |
| 0.693 | 4.0 | 8008 | 0.6936 | 0.5651 | 0.5008 | 0.0 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
gmguarino/multilingual-e5-base-climateguard
|
gmguarino
| 2025-06-04T10:30:47Z | 0 | 0 |
setfit
|
[
"setfit",
"safetensors",
"xlm-roberta",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:intfloat/multilingual-e5-base",
"base_model:finetune:intfloat/multilingual-e5-base",
"model-index",
"region:us"
] |
text-classification
| 2025-06-04T09:51:46Z |
---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: qu'est-ce qu'il va se passer ? je crains qu'il y ait beaucoup de violence
oui je pense que c'est simple c'est soit le chaos soit le sursaut et qu'on risque
d'avoir beaucoup de violence parce qu'on a tout en france pour redevenir un pays
puissant vous le dites vous avez écrit qu'à l'heure où les etats-unis développent
une puce informatique quantique et préparent les esprits à la conquête de mars
nous faisons un conclave sur la retraite à 72 ans de quoi c'est symptomatique
?
- text: aucune amitié pour le président emmanuel macron mais je trouve qu'effectivement
ça permettrait aussi de débattre des idées c'est utile et quand on dit oui mais
charlie hebdo ne s'embarassez pas des nouvelles oui mais charlie hebdo mais faisons
des caricatures charlie hebdo c'est charlie hebdo on le prend comme tel mais attendez
chaque couverture de charlie hebdo qui reprend l'emmanuel macron et qui le caricature
ça c'est sain mais c'est charlie hebdo on n'est pas obligé de faire du charlie
hebdo tous les matins en se réveillant ce n'est pas notre boulot pascal votre
parti prix économique tout le monde est d'accord pour dire qu'il faut décarboner
l'économie parce que c'est le carbone qui fait l'effet de serre je ne sais pas
si tout le monde est d'accord mais on l'entend beaucoup oui c'est vrai et pourtant
dans les faits le grand retour du pétrole c'est ce que vous dites 1150 milliards
de dollars vont être investis cette année en 2025 en général c'est 1000 milliards
là on serait à 1150 milliards de dollars qui seraient investis dans les hydrocarbures
alors ça concerne l'or noir ça concerne le pétrole ça concerne aussi le gnl le
gaz naturel liquéfié qui est très souvent du gaz de schiste un peu moins émetteur
que le pétrole un tout petit peu moins alors oui dans l'ordre il y a le pire du
pire c'est le charbon et puis ensuite il y a le pétrole le gaz de schiste le gaz
naturel etc mais enfin ça reste quand même des investissements dans des ressources
géologiques alors on peut regretter la situation on peut la condamner mais sans
les fossiles je crois qu'il faut se rendre à l'évidence l'activité du monde telle
que nous la connaissons s'arrête du jour au lendemain c'est-à-dire qu'il n'y a
plus de pétrole il n'y a plus d'essence le monde s'arrête il y aura toujours de
l'électricité etc mais vous voyez bien depuis deux siècles d'ailleurs c'est ce
que dit ce matin
- text: pour le raccordement 1 et 10 de ces éoliennes c'est complètement dément mais
c'est dément donc ce que je veux dire on en a parlé ici j'ai écrit ça c'est écrit
il y a des rapports qui s'empilent on sait ce qu'il faut faire mais les éoliennes
c'est intéressant c'est horrible toute la baie de lambeau est massacrée et ça
va être un scandale ça ne sert absolument à
- text: vous intégrer une forte proportion d'électricité intermittent dans le réseau
il suffit qu'il y ait une faible distorsion entre la production et la consommation
qui peut agir par exemple si vous avez un nuage qui passe à un moment beaucoup
de nuages vous allez avoir une baisse de la production des panneaux solaires ou
si vous n'avez pas de vent et cette brusque modification de l'électricité d'origine
peut créer des perturbations du système et des fréquences soit à la hausse soit
à la baisse et donc le
- text: on va mettre 37 milliards pour raccorder des éoliennes dont tout le monde
sait que ce n'est pas de la bonne énergie donc non seulement je pense que c'est
différent sur l'éolien en mer et l'éolien terrestre l'éolien en mer et là ceux
qui attaquent parfois qui remettent en cause sont taxés de populisme c'est pour
ça que je vous demandais si vous aviez une définition précise de populisme je
pense qu'encore une fois c'est protéifiant parce que les populismes ont évolué
et on voit bien aujourd'hui en france d'ailleurs qu'ils continuent à évoluer et
que la question c'est aussi le rapport à l'information le rapport à la vérité
le rapport à la science je pense que ça c'est un enjeu majeur sur la question
du populisme par exemple toutes les questions qu'on a eues par exemple sur la
question du vaccin pendant le covid à un moment mettre en cause une vérité et
un fait scientifique mais quelle vérité madame ? mais quelle vérité puisqu'on
nous a dit que le vaccin ne transmettait pas si on était vacciné on ne transmettait
pas le covid ? et c'est pour ça qu'on se vaccinait ? non madame tout le monde
s'est fait vacciner parce qu'on nous a expliqué que le vaccin permettait la non-transmission
je suis désolé de vous le dire et notamment les jeunes c'est pour ça qu'on les
a vaccinés qu'est-ce qu'on apprend six mois après ? que le vaccin permettait tout
à fait la transmission que le vaccin a largement limité et que les bénéfices du
vaccin étaient bien supérieurs au fait de ne pas être vacciné pour des gosses
de 15 ans ? oui qui n'allaient jamais dans un hôpital et qui ne mourraient pas
? vous êtes sérieuse ?
metrics:
- f1
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: intfloat/multilingual-e5-base
model-index:
- name: SetFit with intfloat/multilingual-e5-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: f1
value: 0.9130434782608695
name: F1
---
# SetFit with intfloat/multilingual-e5-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) as the Sentence Transformer embedding model. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base)
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 2 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | <ul><li>"je ne veux pas vous attrister et aller dans le sens de cette fin du monde préparée mais je préparais pour une activité professionnelle un regard à l'ensemble des communications ou en tout cas des premières communications qui vont être proposées au forum de davos donc qui est réuni qu'on le veuille ou qu'on ne le veuille pas l'essentiel des grands acteurs économiques mondiaux politiques environnement et alors d'habitude ça faisait à peu près 10 ans 15 ans presque une dizaine d'années qu'on avait une forte domination de tout ce qui est discours portés par la rse le travail sur le respect de l'environnement des diversités tout ce que vous pouvez avoir derrière et là c'est la première fois et c'est un changement radical"</li><li>"nous voulons défendre d'abord nos intérêts et je crois que c'est cette attitude en fait c'est une révolution culturelle de culture politique après qui peut se décliner dans tout un tas de politiques commerciales de politiques publiques mais c'est d'abord ce sursaut de dire oui nous sommes fiers nous défendons nos intérêts alors là où vous suivez en revanche de très près donald trump c'est qu'il a mis fin au green new deal de joe biden et immédiatement après dans la foulée jordan bardella le président du parti rassemblement national a demandé la suspension du green deal européen donc là par contre vous êtes en phase avec les etats-unis et surtout vous dites la planète c'est plus nos soucis non alors d'abord pendant toute la campagne électorale des européennes le rassemblement national et évidemment jordan bardella en premier lieu a toujours dit qu'il fallait combattre les excès de ce pacte vert et que entre une je dirais une écologie raisonnable et un sentier de croissance économique voilà il y a entre l'écologie et l'économie une possibilité de trouver un chemin rappelez que l'union européenne est déjà aujourd'hui le continent en tout cas l'ensemble politique le plus vertueux du point de vue environnemental les émissions de carbone par exemple de co2 de l'union européenne représentent à peu près 6% des émissions mondiales donc par exemple il y a tout un tas de mesures du pacte vert je pense notamment à ce qui est en train de tuer l'industrie automobile européenne les allemands sont en train de se réveiller lorsqu'ils voient que mercedes fait des pertes et que voit son chiffre d'affaires reculer de plus de 35% donc on voit bien la difficulté lorsqu'on dit qu'on va interdire le moteur thermique et qu'on sera dans le tout électrique on ne dit même pas des voitures hybrides en 2035"</li><li>"anticiper ces impacts-là et déjà commencer à planter des arbres qui sont adaptés à 2050 plutôt que planter les espèces qu'on a actuellement locales dans la région et en attendant il faut tenir bon en tout cas bon courage à tous les éleveurs les agriculteurs en général d'ailleurs du roussillon et de l'aude parce qu'ils sont dans une situation quand même extrêmement compliquée depuis deux ans maintenant merci beaucoup cher zaka"</li></ul> |
| 1 | <ul><li>" qu'il faut financer la transition énergétique vous savez toutes ces énergies renouvelables qu'on n'a pas vraiment besoin en france malheureusement parce qu'on a déjà beaucoup de nucléaires qui fonctionnent et puis parce qu'on a aussi l'augmentation des tarifs de réseau augmentation qui aurait dû avoir lieu en août mais pour éviter de faire baisser le tarif puis de le faire réaugmenter ils l'ont mise au 1er février augmentation de 7% une fois de plus ça c'est les frais des réseaux pour distribuer"</li><li>"es plus grands pollueurs mondiaux sont d'abord l'inde sont d'abord la chine et que sans grande coopération internationale qui n'aura d'ailleurs jamais lieu notre pouvoir d'influencer les choses reste quand même minime je pense que ça peut résonner au contraire il y a des gens qui se disent écoutez est-ce que c'est vraiment parce que je prends une douche de 15 minutes de plus que je suis en train de détruire la planète"</li><li>"tique parce qu'on a des raisins qui sont plus mûrs donc on n'a plus besoin de chaptaliser on a des raisins qui ont un meilleur goût et quand le raisin a un meilleur goût il fait de meilleurs vins on n'a plus ces tannins âpres ces notes de poivrons et puis après on ne fait plus non"</li></ul> |
## Evaluation
### Metrics
| Label | F1 |
|:--------|:-------|
| **all** | 0.9130 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("pour le raccordement 1 et 10 de ces éoliennes c'est complètement dément mais c'est dément donc ce que je veux dire on en a parlé ici j'ai écrit ça c'est écrit il y a des rapports qui s'empilent on sait ce qu'il faut faire mais les éoliennes c'est intéressant c'est horrible toute la baie de lambeau est massacrée et ça va être un scandale ça ne sert absolument à")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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.*
-->
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 1 | 147.85 | 350 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 30 |
| 1 | 30 |
### Training Hyperparameters
- batch_size: (4, 4)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- run_name: multilingual-e5-base-climateguard04-06-2025_12-18-25
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0022 | 1 | 0.0186 | - |
| 0.1075 | 50 | 0.307 | 0.2088 |
| 0.2151 | 100 | 0.1707 | 0.2171 |
| 0.3226 | 150 | 0.0684 | 0.1239 |
| 0.4301 | 200 | 0.0078 | 0.1960 |
| 0.5376 | 250 | 0.002 | 0.2222 |
| 0.6452 | 300 | 0.0386 | 0.2407 |
| 0.7527 | 350 | 0.0334 | 0.2357 |
| 0.8602 | 400 | 0.0125 | 0.2068 |
| 0.9677 | 450 | 0.002 | 0.1875 |
### Framework Versions
- Python: 3.12.8
- SetFit: 1.1.2
- Sentence Transformers: 4.1.0
- Transformers: 4.52.2
- PyTorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## 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.*
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
sergbese/gemma-3-isv-translator-v5
|
sergbese
| 2025-06-04T10:30:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-04T10:28:08Z |
---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sergbese
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it
This gemma3 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)
|
cluebbers/bart-large-paraphrase-type-generation-etpc
|
cluebbers
| 2025-06-04T09:41:15Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"arxiv:2506.02018",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-12-19T08:10:46Z |
---
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:**
Cite this model:
```bibtex
@misc{lübbers2025enhancingparaphrasetypegeneration,
title={Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data},
author={Christopher Lee Lübbers},
year={2025},
eprint={2506.02018},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.02018},
}
```
**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]
|
AlexHung29629/Ojamajo_Doremi
|
AlexHung29629
| 2025-06-04T09:38:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3omni",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2025-06-04T08:53:43Z |
---
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]
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[More Information Needed]
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|
Mirmix/textual_inversion_scan24_full_3tokens
|
Mirmix
| 2025-06-04T09:36:47Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"diffusers-training",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:adapter:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-04T06:12:51Z |
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
- diffusers-training
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual inversion text2image fine-tuning - Mirmix/textual_inversion_scan24_full_3tokens
These are textual inversion adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
anonloftune/llama-2-7b-insurance-40-facttune-fs
|
anonloftune
| 2025-06-04T09:24:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"dataset:anonloftune/insurance-40-facttune-fs",
"arxiv:1910.09700",
"base_model:anonloftune/llama-2-7b-insurance-40-sft",
"base_model:adapter:anonloftune/llama-2-7b-insurance-40-sft",
"region:us"
] | null | 2025-06-02T09:01:43Z |
---
library_name: peft
base_model: anonloftune/llama-2-7b-insurance-40-sft
datasets:
- anonloftune/insurance-40-facttune-fs
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.9.0
|
mradermacher/gemma2B-malay-english-GGUF
|
mradermacher
| 2025-06-04T09:11:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-04T09:11:45Z |
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/MyTranslate/gemma2B-malay-english
|
LeonGuertler/Qwen3-4B-batch-4-experiment-8-step_000050
|
LeonGuertler
| 2025-06-04T09:07:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T08:57:30Z |
---
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]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
MinaMila/llama_8b_unlearned_unbalanced_gender_2nd_1e-5_1.0_0.05_0.15_0.15_epoch2
|
MinaMila
| 2025-06-04T09:01:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T08:58:40Z |
---
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]
|
allenai/Llama-3.1-70B-Instruct-RM-RB2
|
allenai
| 2025-06-04T08:52:48Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-classification",
"en",
"dataset:allenai/llama-3.1-tulu-3-70b-preference-mixture",
"dataset:Skywork/Skywork-Reward-Preference-80K-v0.2",
"arxiv:2506.01937",
"base_model:meta-llama/Llama-3.1-70B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-70B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-02T00:57:44Z |
---
license: llama3.1
language:
- en
pipeline_tag: text-classification
datasets:
- allenai/llama-3.1-tulu-3-70b-preference-mixture
- Skywork/Skywork-Reward-Preference-80K-v0.2
base_model:
- meta-llama/Llama-3.1-70B-Instruct
library_name: transformers
---
# Model Card for Llama-3.1-70B-Instruct-RM-RB2
<!-- Provide a quick summary of what the model is/does. -->
Llama-3.1-70B-Instruct-RM-RB2 is one of 7 sets of reward models (RMs) released with Reward Bench 2.
We have released a large set of 70 total reward model checkpoints that we used to develop the benchmark and correlate it with downstream PPO / Best-of-N performance.
[Models](https://huggingface.co/collections/allenai/reward-bench-2-683d2612a4b3e38a3e53bb51) | [Code](https://github.com/allenai/reward-bench) | [Eval. Dataset v2](https://huggingface.co/datasets/allenai/reward-bench-2) | [Results v2](https://huggingface.co/datasets/allenai/reward-bench-2-results) | [Paper](https://arxiv.org/abs/2506.01937)
## Model Details
The model is a standard classifier, `AutoModelForSequenceClassification` within the HuggingFace ecosystem, trained on binary preference data.
For each model in this batch the main revision is the best model we obtained for that base model, and we include all other training data and hyperparameter combinations in the revisions for further research.
To load a model from a revision, modify the following:
```python
from transformers import AutoModelForSequenceClassification
rm = AutoModelForSequenceClassification("allenai/Llama-3.1-70B-Instruct-RM-RB2", revision="2")
```
<!-- Provide a longer summary of what this model is. -->
| Revision | Training Data | Learning Rate | Num Epochs | RewardBench 2 Score | Factuality | Precise IF | Math | Safety | Focus | Ties |
|----------|---------------|---------------|------------|---------------------|------------|------------|------|--------|-------|------|
| main | Combined | 3e-6 | 1 | 76.1 | 81.3 | 41.9 | 69.9 | 88.4 | 86.5 | 88.3 |
| 1 | Combined | 3e-6 | 1 | 75.7 | 81.7 | 41.2 | 70.5 | 87.3 | 85.5 | 88.1 |
| 2 | Combined | 1e-6 | 1 | 73.1 | 74.7 | 37.5 | 69.4 | 86.2 | 80.6 | 89.9 |
- **Developed by:** Allen Institute for AI
- **Training code:** https://github.com/allenai/open-instruct
- **Language(s) (NLP):** en
- **License:** Llama 3.1 Community License Agreement
- **Finetuned from model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct)
## License
All Llama 3.1 Tülu3 models are released under Meta's [Llama 3.1 Community License Agreement](https://www.llama.com/llama3_1/license/).
Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc.
Tülu3 is intended for research and educational use.
For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
The models have been fine-tuned using a dataset mix with outputs generated from third party models and are subject to additional terms:
[Gemma Terms of Use](https://ai.google.dev/gemma/terms) and [Qwen License Agreement](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE) (models were improved using Qwen 2.5).
## Citation
```
@misc{malik2025rewardbench2advancingreward,
title={RewardBench 2: Advancing Reward Model Evaluation},
author={Saumya Malik and Valentina Pyatkin and Sander Land and Jacob Morrison and Noah A. Smith and Hannaneh Hajishirzi and Nathan Lambert},
year={2025},
eprint={2506.01937},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2506.01937},
}
```
Model card contact: `saumyam at allenai dot org`
|
Bjrjrjr/Deepsewk
|
Bjrjrjr
| 2025-06-04T08:17:25Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-04T08:17:25Z |
---
license: apache-2.0
---
|
shulijia/MNLP_M3_mcqa_model_base_m1_1_ep3
|
shulijia
| 2025-06-04T08:12:37Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T07:52:44Z |
---
base_model: Qwen/Qwen3-0.6B-Base
library_name: transformers
model_name: MNLP_M3_mcqa_model_base_m1_1_ep3
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MNLP_M3_mcqa_model_base_m1_1_ep3
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base).
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="shulijia/MNLP_M3_mcqa_model_base_m1_1_ep3", 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: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
luyotw/openfun-ivod-whisper-medium-negotiation-10-32
|
luyotw
| 2025-06-04T08:01:24Z | 2 | 0 | null |
[
"tensorboard",
"safetensors",
"whisper",
"region:us"
] | null | 2025-06-04T06:33:44Z |
# Fine-tune 資訊
- 原始模型: `openai/whisper-medium`
- 使用音訊數量: 27385
- 使用音訊總長: 15.06 小時
- 音訊平均長度: 1.98 秒
- GPU: `NVIDIA H100 PCIe` x 1
- 訓練時間: 03:42:05
- 模型大小: 2.85 GB
- 訓練參數:
- batch size: 16
- eval batch size: 8
- gradient checkpointing: False
- fp16: False
- bf16: True
---
# Model Card
|
cgus/Homunculus-exl2
|
cgus
| 2025-06-04T07:59:15Z | 0 | 0 |
exllamav2
|
[
"exllamav2",
"mistral",
"distillation",
"/think",
"/nothink",
"reasoning-transfer",
"arcee-ai",
"en",
"base_model:arcee-ai/Homunculus",
"base_model:quantized:arcee-ai/Homunculus",
"license:apache-2.0",
"4-bit",
"exl2",
"region:us"
] | null | 2025-06-03T22:29:57Z |
---
language:
- en
license: apache-2.0
library_name: exllamav2
base_model:
- arcee-ai/Homunculus
tags:
- distillation
- /think
- /nothink
- reasoning-transfer
- arcee-ai
---
# Homunculus-12B-exl2
Original model: [Homunculus](https://huggingface.co/arcee-ai/Homunculus) by [Arcee AI](https://huggingface.co/arcee-ai)
Based on: [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) by [Mistral AI](https://huggingface.co/mistralai) and [Qwen3-235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) by [Qwen](https://huggingface.co/Qwen)
## Quants
[4bpw h6 (main)](https://huggingface.co/cgus/Homunculus-exl2/tree/main)
[4.5bpw h6](https://huggingface.co/cgus/Homunculus-exl2/tree/4.5bpw-h6)
[5bpw h6](https://huggingface.co/cgus/Homunculus-exl2/tree/5bpw-h6)
[6bpw h6](https://huggingface.co/cgus/Homunculus-exl2/tree/6bpw-h6)
[8bpw h8](https://huggingface.co/cgus/Homunculus-exl2/tree/8bpw-h8)
## Quantization notes
Made with Exllamav2 0.3.1 with default dataset.
These quants can be used with RTX GPU (Windows) or RTX/ROCm GPUs (Linux) with TabbyAPI or Text-Generation-WebUI.
Ensure you have enough VRAM to use it. I used to run 6bpw Mistral-Nemo quants with 12GB VRAM at 16k context/Q6 or Q4 cache.
If you have old GPUs (e.g. GTX/P40) or low VRAM, try using GGUF quants instead.
# Original model card
# Arcee **Homunculus-12B**
**Homunculus** is a 12 billion-parameter instruction model distilled from **Qwen3-235B** onto the **Mistral-Nemo** backbone.
It was purpose-built to preserve Qwen’s two-mode interaction style—`/think` (deliberate chain-of-thought) and `/nothink` (concise answers)—while running on a single consumer GPU.
---
## ✨ What’s special?
| Feature | Detail |
| --------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Reasoning-trace transfer** | Instead of copying just final probabilities, we align *full* logit trajectories, yielding more faithful reasoning. |
| **Total-Variation-Distance loss** | To better match the teacher’s confidence distribution and smooth the loss landscape. |
| **Tokenizer replacement** | The original Mistral tokenizer was swapped for Qwen3's tokenizer. |
| **Dual interaction modes** | Use `/think` when you want transparent step-by-step reasoning (good for analysis & debugging). Use `/nothink` for terse, production-ready answers. Most reliable in the system role field. | |
---
## Benchmark results
| Benchmark | Score |
| --------- | ----- |
| GPQADiamond (average of 3) | 57.1% |
| mmlu | 67.5% |
## 🔧 Quick Start
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "arcee-ai/Homunculus"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype="auto",
device_map="auto"
)
# /think mode - Chain-of-thought reasoning
messages = [
{"role": "system", "content": "You are a helpful assistant. /think"},
{"role": "user", "content": "Why is the sky blue?"},
]
output = model.generate(
tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt"),
max_new_tokens=512,
temperature=0.7
)
print(tokenizer.decode(output[0], skip_special_tokens=True))
# /nothink mode - Direct answers
messages = [
{"role": "system", "content": "You are a helpful assistant. /nothink"},
{"role": "user", "content": "Summarize the plot of Hamlet in two sentences."},
]
output = model.generate(
tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt"),
max_new_tokens=128,
temperature=0.7
)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
## 💡 Intended Use & Limitations
Homunculus is designed for:
* **Research** on reasoning-trace distillation, Logit Imitation, and mode-switchable assistants.
* **Lightweight production** deployments that need strong reasoning at <12 GB VRAM.
### Known limitations
* May inherit biases from the Qwen3 teacher and internet-scale pretraining data.
* Long-context (>32 k tokens) use is experimental—expect latency & memory overhead.
---
|
ArmelR/Llama-2-7B-mono-Urdu
|
ArmelR
| 2025-06-04T07:28:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T14:43:58Z |
---
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]
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|
fritzyan/newegg-item-bulletpoint-llama
|
fritzyan
| 2025-06-04T06:56:18Z | 28 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-04T06:56:01Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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### Framework versions
- PEFT 0.15.2
|
zoros-ai/deta-swin-large-room-detector-test-epoch-6
|
zoros-ai
| 2025-06-04T06:51:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deta",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2025-06-04T06:21:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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.
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## Bias, Risks, and Limitations
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[More Information Needed]
### 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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[More Information Needed]
## Training Details
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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).
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|
jtran4481/trav_tts
|
jtran4481
| 2025-06-04T05:01:02Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T04:56:54Z |
---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jtran4481
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MinaMila/llama_8b_unlearned_unbalanced_neutral_2nd_1e-5_1.0_0.25_0.5_0.5_epoch1
|
MinaMila
| 2025-06-04T04:42:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-04T04:39:08Z |
---
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]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
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<!-- 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).
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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]
|
ngocnhi15/legiakhang
|
ngocnhi15
| 2025-06-04T04:34:47Z | 0 | 0 | null |
[
"license:cdla-permissive-1.0",
"region:us"
] | null | 2025-06-04T04:34:47Z |
---
license: cdla-permissive-1.0
---
|
Basher4321/Aslam
|
Basher4321
| 2025-06-04T03:52:56Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-04T03:48:32Z |
---
license: apache-2.0
---
|
CMB-AI-LAB/lagkv_cache
|
CMB-AI-LAB
| 2025-06-04T03:42:08Z | 0 | 0 |
transformers
|
[
"transformers",
"custom_generate",
"arxiv:2504.04704",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T01:56:23Z |
---
library_name: transformers
tags:
- custom_generate
---
# LagKV Cache
## Introduction
LagKV is an efficient and robust KV compression algorithm. It uses lag tokens information to compress the previous ones which significantly boost the compression performance with little computation overhead.
[Original Github](https://github.com/AI-Lab-China-Merchants-Bank/LagKV)
Details are in the following work:
[LagKV: Lag-Relative Information of the KV Cache Tells Which Tokens Are Important](https://arxiv.org/abs/2504.04704)
## Example usage
We can use the custom generation method in this repository like the the base `generate` from `transformers`:
```py
# requires `transformers>=4.52.0`
from transformers import AutoModelForCausalLM, AutoTokenizer
# Preparing model, tokenizer, and model inputs
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", device_map="auto")
messages = [{"role": "user", "content": "Tell me a story about a cat."}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Using lagkv cache
gen_out = model.generate(
# usual `generate` arguments
**model_inputs,
do_sample=False,
max_new_tokens=100,
return_dict_in_generate=True,
# lagkv cache arguments (default `lag_ratio=0.5,lag_size=128,lag_sink_size=16`)
custom_generate="CMB-AI-LAB/lagkv_cache",
trust_remote_code=True,
)
print(tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True))
assert "lagkvcache" in str(type(gen_out.past_key_values)).lower()
|
JJHub008/streetv2
|
JJHub008
| 2025-06-04T03:37:21Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-04T03:32:10Z |
---
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]
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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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<!-- 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
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[More Information Needed]
## Training Details
### Training Data
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## Evaluation
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
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[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).
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## Technical Specifications [optional]
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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]
## Citation [optional]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ibuki95/a4ono6nq
|
ibuki95
| 2025-06-04T03:37:06Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-04T03:36:03Z |
---
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]
|
ibuki95/b1rldyw1
|
ibuki95
| 2025-06-04T03:37:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-04T03:36:02Z |
---
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]
|
wcyno23/FlexRAG
|
wcyno23
| 2025-06-04T03:29:21Z | 28 | 1 | null |
[
"pytorch",
"safetensors",
"llama",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-11-18T05:01:40Z |
---
license: apache-2.0
language:
- en
---
**FlexRAG** is a lightweight model designed to reduce RAG running costs while improving its generation quality. It compresses the retrieved contexts into compact embeddings and these embeddings are optimized to enhance downstream RAG performance. A key feature of FlexRAG is its flexibility, which enables effective support for diverse compression ratios and selective preservation of important contexts.
For the usage of this model, please refer to [[Github Repo]](https://github.com/wcyno23/FlexRAG)
# Citation Information
```
@inproceedings{wu2025lighter,
title={Lighter and better: Towards flexible context adaptation for retrieval augmented generation},
author={Wu, Chenyuan and Shao, Ninglu and Liu, Zheng and Xiao, Shitao and Li, Chaozhuo and Zhang, Chen and Wang, Senzhang and Lian, Defu},
booktitle={Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining},
pages={271--280},
year={2025}
}
```
|
xTbtyE/Usp-Q4_K_M-GGUF
|
xTbtyE
| 2025-06-04T02:44:17Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:mergekit-community/Usp",
"base_model:quantized:mergekit-community/Usp",
"endpoints_compatible",
"region:us"
] | null | 2025-06-04T02:43:43Z |
---
base_model: mergekit-community/Usp
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# xTbtyE/Usp-Q4_K_M-GGUF
This model was converted to GGUF format from [`mergekit-community/Usp`](https://huggingface.co/mergekit-community/Usp) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/mergekit-community/Usp) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo xTbtyE/Usp-Q4_K_M-GGUF --hf-file usp-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo xTbtyE/Usp-Q4_K_M-GGUF --hf-file usp-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo xTbtyE/Usp-Q4_K_M-GGUF --hf-file usp-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo xTbtyE/Usp-Q4_K_M-GGUF --hf-file usp-q4_k_m.gguf -c 2048
```
|
featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF
|
featherless-ai-quants
| 2025-06-04T02:32:49Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"base_model:quantized:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-04T02:08:12Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# deepseek-ai/DeepSeek-R1-Distill-Qwen-14B GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-IQ4_XS.gguf) | 7806.96 MB |
| Q2_K | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q2_K.gguf) | 5503.17 MB |
| Q3_K_L | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q3_K_L.gguf) | 7557.65 MB |
| Q3_K_M | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q3_K_M.gguf) | 6999.21 MB |
| Q3_K_S | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q3_K_S.gguf) | 6351.09 MB |
| Q4_K_M | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q4_K_M.gguf) | 8571.73 MB |
| Q4_K_S | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q4_K_S.gguf) | 8176.26 MB |
| Q5_K_M | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q5_K_M.gguf) | 10022.04 MB |
| Q5_K_S | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q5_K_S.gguf) | 9790.95 MB |
| Q6_K | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q6_K.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q6_K.gguf) | 11563.00 MB |
| Q8_0 | [deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q8_0.gguf](https://huggingface.co/featherless-ai-quants/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-GGUF/blob/main/deepseek-ai-DeepSeek-R1-Distill-Qwen-14B-Q8_0.gguf) | 14974.21 MB |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
Mavinho777/mavinho777
|
Mavinho777
| 2025-06-04T01:44:37Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-04T01:13:08Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
timarni/qwen3_pretrain_wiki
|
timarni
| 2025-06-04T01:43:28Z | 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
| 2025-06-04T01:42:51Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: outputs/qwen3_pretrain_wiki
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
######################################
# CONTINUED PRE-TRAINING EXAMPLE #
######################################
base_model: Qwen/Qwen3-0.6B-Base # the checkpoint you start from
strict: false
# 1⃣ Replace `datasets:` with `pretraining_dataset:`
pretraining_dataset:
- path: timarni/pretrain-wikipedia # or HF dataset id
type: completion # accepted values: text | completion | HF dataset
# 2⃣ Remove chat / instruction-tuning options
chat_template:
# adapter / lora stay null/false (full-parameter training)
# 3⃣ Training hyper-params (see Section 3)
sequence_len: 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
micro_batch_size: 1
gradient_accumulation_steps: 2
max_steps: 3500 # or use max_steps instead
learning_rate: 1e-5
lr_scheduler: cosine
warmup_steps: 100
weight_decay: 0.01
optimizer: adamw_torch
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: offload
val_set_size: 0.0 # usually no dev set for plain pre-training
output_dir: ./outputs/qwen3_pretrain_wiki
dataset_prepared_path: last_run_prepared
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-pretrain_wiki
```
</details><br>
# outputs/qwen3_pretrain_wiki
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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 3500
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-5e-06_e-1_s-0
|
publication-charaf
| 2025-06-03T23:23:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"base_model:finetune:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T22:20:45Z |
---
base_model: publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0
library_name: transformers
model_name: MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-5e-06_e-1_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-5e-06_e-1_s-0
This model is a fine-tuned version of [publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0](https://huggingface.co/publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-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="publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-5e-06_e-1_s-0", 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/kamel-charaf-epfl/huggingface/runs/bhdk9sal)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- 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}}
}
```
|
mohamedsaeed823/nanoVLM
|
mohamedsaeed823
| 2025-06-03T22:02:12Z | 0 | 0 |
nanovlm
|
[
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-06-03T22:01:19Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("mohamedsaeed823/nanoVLM")
```
|
VIDEOS-18-Bihari-Bhabhi-Viral-Videos/FULL.VIDEO.Bihari.Bhabhi.Viral.Video.Tutorial.Official
|
VIDEOS-18-Bihari-Bhabhi-Viral-Videos
| 2025-06-03T21:41:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-03T21:41:38Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
shaojintian/complex_attention_0.5B
|
shaojintian
| 2025-06-03T21:03:28Z | 0 | 0 | null |
[
"safetensors",
"ComplexFormer",
"license:apache-2.0",
"region:us"
] | null | 2025-06-03T20:31:44Z |
---
license: apache-2.0
---
|
obiwan001/roadwork1
|
obiwan001
| 2025-06-03T20:54:53Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-03T14:53:06Z |
---
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]
|
hassno/cv-llm-parser-synth-data-version4.0
|
hassno
| 2025-06-03T19:43:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-03T19:43:10Z |
---
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]
|
Manini-sahar16/Medica
|
Manini-sahar16
| 2025-06-03T17:19:14Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-06-03T17:19:14Z |
---
license: bigscience-openrail-m
---
|
AravindS373/1800_only_code
|
AravindS373
| 2025-06-03T17:16:55Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:seeklhy/OmniSQL-7B",
"base_model:adapter:seeklhy/OmniSQL-7B",
"region:us"
] | null | 2025-06-03T17:09:51Z |
---
base_model: seeklhy/OmniSQL-7B
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
Diamantis99/FhqpXyJ
|
Diamantis99
| 2025-06-03T17:07:16Z | 0 | 0 |
segmentation-models-pytorch
|
[
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] |
image-segmentation
| 2025-06-03T17:06:59Z |
---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# DeepLabV3 Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "se_resnext101_32x4d",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"encoder_output_stride": 8,
"decoder_channels": 256,
"decoder_atrous_rates": (12, 24, 36),
"decoder_aspp_separable": False,
"decoder_aspp_dropout": 0.5,
"in_channels": 3,
"classes": 1,
"activation": None,
"upsampling": None,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.8791742920875549,
"test_dataset_iou": 0.8968632817268372
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
|
prithivMLmods/docscopeOCR-7B-050425-exp-GGUF
|
prithivMLmods
| 2025-06-03T15:18:30Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2_5_vl",
"text-generation-inference",
"document",
"ocr",
"image-text-to-text",
"en",
"zh",
"base_model:prithivMLmods/docscopeOCR-7B-050425-exp",
"base_model:quantized:prithivMLmods/docscopeOCR-7B-050425-exp",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-06-03T02:12:53Z |
---
license: apache-2.0
language:
- en
- zh
base_model:
- prithivMLmods/docscopeOCR-7B-050425-exp
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
- document
- ocr
---
# **docscopeOCR-7B-050425-exp-GGUF**
> The **docscopeOCR-7B-050425-exp** model is a fine-tuned version of **Qwen/Qwen2.5-VL-7B-Instruct**, optimized for **Document-Level Optical Character Recognition (OCR)**, **long-context vision-language understanding**, and **accurate image-to-text conversion with mathematical LaTeX formatting**. Built on top of the Qwen2.5-VL architecture, this model significantly improves document comprehension, structured data extraction, and visual reasoning across diverse input formats.
## Model File
| File Name | Size | Format | Description |
|-----------------------------------------------|---------|----------------|----------------------------------------|
| docscopeOCR-7B-050425-exp.IQ4_XS.gguf | 4.25 GB | GGUF (IQ4_XS) | Int4 extra-small quantized model |
| docscopeOCR-7B-050425-exp.Q2_K.gguf | 3.02 GB | GGUF (Q2_K) | 2-bit quantized model |
| docscopeOCR-7B-050425-exp.Q3_K_L.gguf | 4.09 GB | GGUF (Q3_K_L) | 3-bit large quantized model |
| docscopeOCR-7B-050425-exp.Q3_K_M.gguf | 3.81 GB | GGUF (Q3_K_M) | 3-bit medium quantized model |
| docscopeOCR-7B-050425-exp.Q3_K_S.gguf | 3.49 GB | GGUF (Q3_K_S) | 3-bit small quantized model |
| docscopeOCR-7B-050425-exp.Q4_K_M.gguf | 4.68 GB | GGUF (Q4_K_M) | 4-bit medium quantized model |
| docscopeOCR-7B-050425-exp.Q5_K_M.gguf | 5.44 GB | GGUF (Q5_K_M) | 5-bit medium quantized model |
| docscopeOCR-7B-050425-exp.Q5_K_S.gguf | 5.32 GB | GGUF (Q5_K_S) | 5-bit small quantized model |
| docscopeOCR-7B-050425-exp.Q6_K.gguf | 6.25 GB | GGUF (Q6_K) | 6-bit quantized model |
| docscopeOCR-7B-050425-exp.Q8_0.gguf | 8.1 GB | GGUF (Q8_0) | 8-bit quantized model |
| config.json | 36 B | JSON | Configuration file |
| .gitattributes | 2.25 kB | Text | Git attributes configuration |
## Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
|
nwdxlgzs/objlua-source-gen-checkpoint-1000
|
nwdxlgzs
| 2025-06-03T15:09:08Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"license:mit",
"region:us"
] | null | 2025-06-03T14:33:14Z |
---
license: mit
---
input:
lauxlib.c ldblib.c lgc.c lmathlib.c lobjudata.h lstate.c ltests.c lualib.h
lauxlib.h ldebug.c lgc.h lmem.c lopcodes.c lstate.h ltests.h lundump.c
lbaselib.c ldebug.h linit.c lmem.h lopcodes.h lstring.c ltm.c lundump.h
lcode.c ldo.c liolib.c loadlib.c lopnames.h lstring.h ltm.h lutf8lib.c
lcode.h ldo.h ljumptab.h lobject.c loslib.c lstrlib.c lua.c lvm.c
lcorolib.c ldump.c llex.c lobject.h lparser.c ltable.c lua.h lvm.h
lctype.c lfunc.c llex.h lobjlualib.c lparser.h ltable.h luac.c lzio.c
lctype.h lfunc.h llimits.h lobjudata.c lprefix.h ltablib.c luaconf.h lzio.h
lapi.c lapi.h onelua.c
gen:
code
test llm remember code
|
Diamantis99/x9xv0Cu
|
Diamantis99
| 2025-06-03T14:52:55Z | 0 | 0 |
segmentation-models-pytorch
|
[
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] |
image-segmentation
| 2025-06-03T14:52:52Z |
---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# PSPNet Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "timm-tf_efficientnet_lite4",
"encoder_weights": "imagenet",
"encoder_depth": 3,
"psp_out_channels": 512,
"decoder_use_norm": "batchnorm",
"psp_dropout": 0.2,
"in_channels": 3,
"classes": 1,
"activation": None,
"upsampling": 8,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.7290293574333191,
"test_dataset_iou": 0.763283908367157
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
|
somosnlp-hackathon-2025/mistral-7b-gastronomia-hispana-dpo-LoRA
|
somosnlp-hackathon-2025
| 2025-06-03T14:35:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T14:35:49Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** somosnlp-hackathon-2025
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Diamantis99/z4SohHM
|
Diamantis99
| 2025-06-03T14:35:44Z | 0 | 0 |
segmentation-models-pytorch
|
[
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] |
image-segmentation
| 2025-06-03T14:35:37Z |
---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# PSPNet Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "xception",
"encoder_weights": "imagenet",
"encoder_depth": 3,
"psp_out_channels": 512,
"decoder_use_norm": "batchnorm",
"psp_dropout": 0.2,
"in_channels": 3,
"classes": 1,
"activation": None,
"upsampling": 8,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.6958630084991455,
"test_dataset_iou": 0.7307329177856445
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
|
FormlessAI/fedb3247-7afd-47c7-80c2-12b5986f28f4
|
FormlessAI
| 2025-06-03T13:47:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"base_model:facebook/opt-1.3b",
"base_model:finetune:facebook/opt-1.3b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T10:01:05Z |
---
base_model: facebook/opt-1.3b
library_name: transformers
model_name: fedb3247-7afd-47c7-80c2-12b5986f28f4
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for fedb3247-7afd-47c7-80c2-12b5986f28f4
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="FormlessAI/fedb3247-7afd-47c7-80c2-12b5986f28f4", 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/phoenix-formless/Gradients/runs/2zkpjztq)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu128
- 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}}
}
```
|
Diamantis99/NPtHIYc
|
Diamantis99
| 2025-06-03T13:43:48Z | 0 | 0 |
segmentation-models-pytorch
|
[
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] |
image-segmentation
| 2025-06-03T13:43:40Z |
---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# FPN Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "mobileone_s4",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_pyramid_channels": 256,
"decoder_segmentation_channels": 128,
"decoder_merge_policy": "add",
"decoder_dropout": 0.2,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"upsampling": 4,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.8519535064697266,
"test_dataset_iou": 0.8791386485099792
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
|
jinx2321/nllb-1e4-paper
|
jinx2321
| 2025-06-03T13:42:22Z | 64 | 0 |
transformers
|
[
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/nllb-200-distilled-600M",
"base_model:finetune:facebook/nllb-200-distilled-600M",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-03T09:02:38Z |
---
library_name: transformers
license: cc-by-nc-4.0
base_model: facebook/nllb-200-distilled-600M
tags:
- generated_from_trainer
model-index:
- name: nllb-1e4-paper
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. -->
# nllb-1e4-paper
This model is a fine-tuned version of [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
Srajan04/gemma-2b-it-hindi-gguf
|
Srajan04
| 2025-06-03T13:40:28Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-03T13:25:56Z |
# Gemma 2B Hindi GGUF
This repository contains GGUF format models converted from `Srajan04/gemma-2b-it-hindi-4bitq` for CPU inference using llama.cpp.
## Files
- `gemma-2b-hindi-q4_k_m.gguf` - 4-bit K-quantized medium (recommended)
- `gemma-2b-hindi-f16.gguf` - Full precision float16 (largest, best quality)
- `gemma-2b-hindi-q4_0.gguf` - 4-bit quantized (smallest, fastest)
- `gemma-2b-hindi-q4_1.gguf` - 4-bit quantized (legacy format)
- `gemma-2b-hindi-q8_0.gguf` - 8-bit quantized (good balance of size/quality)
## Usage with llama.cpp
```bash
# Download llama.cpp
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
mkdir build && cd build
cmake .. -DGGML_CUDA=OFF -DGGML_METAL=OFF -DLLAMA_CURL=OFF
make -j$(nproc)
cd ..
# Download model and run inference
wget https://huggingface.co/Srajan04/gemma-2b-it-hindi-gguf/resolve/main/gemma-2b-hindi-q4_0.gguf
./build/bin/llama-cli -m gemma-2b-hindi-q4_0.gguf -p "नमस्ते, आप कैसे हैं?" -n 100
```
## Usage with Python (llama-cpp-python)
```bash
pip install llama-cpp-python
```
```python
from llama_cpp import Llama
# Download model
llm = Llama.from_pretrained(
repo_id="Srajan04/gemma-2b-it-hindi-gguf",
filename="gemma-2b-hindi-q4_0.gguf",
verbose=False
)
# Generate text
output = llm("नमस्ते, आप कैसे हैं?", max_tokens=100)
print(output['choices'][0]['text'])
```
## Model Details
- **Base Model**: google/gemma-2b-it
- **Language**: Hindi + English
- **Quantization**: Various GGUF quantization levels
- **Use Case**: CPU inference, edge deployment
## Original Model
Based on: [Srajan04/gemma-2b-it-hindi-4bitq](https://huggingface.co/Srajan04/gemma-2b-it-hindi-4bitq)
|
KremerVini/Qwen-3-0.6B-Medical-Reasoning
|
KremerVini
| 2025-06-03T13:40:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T13:39:47Z |
---
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]
|
kowndinya23/ultrafeedback_binarized-tulu-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4-2-epochs
|
kowndinya23
| 2025-06-03T13:21:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:trl-lib/ultrafeedback_binarized",
"arxiv:2305.18290",
"base_model:kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4",
"base_model:finetune:kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T12:26:05Z |
---
base_model: kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4
datasets: trl-lib/ultrafeedback_binarized
library_name: transformers
model_name: ultrafeedback_binarized-tulu-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4-2-epochs
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for ultrafeedback_binarized-tulu-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4-2-epochs
This model is a fine-tuned version of [kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4](https://huggingface.co/kowndinya23/tulu-v2-sft-mixture-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) 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="kowndinya23/ultrafeedback_binarized-tulu-150K-llama-3-1b-1-epochs-alpha-1-beta-0.4-2-epochs", 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://adobesensei.wandb.io/hrenduchinta/huggingface/runs/gus4ddg8)
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: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
choicow/sample
|
choicow
| 2025-06-03T13:01:12Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:stable-diffusion-v1-5/stable-diffusion-v1-5",
"base_model:finetune:stable-diffusion-v1-5/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-03T12:54:04Z |
---
base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
instance_prompt: 'Generate an image of sks person matching this pose: There is sks
person in the image who is performing conditioning exercise, resistance training.'
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - choicow/sample
This is a dreambooth model derived from stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on Generate an image of sks person matching this pose: There is sks person in the image who is performing conditioning exercise, resistance training. using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
mradermacher/L3-Dark-Planet-8B-GGUF
|
mradermacher
| 2025-06-03T11:56:28Z | 99 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-3",
"creative",
"creative writing",
"fiction writing",
"plot generation",
"sub-plot generation",
"story generation",
"scene continue",
"storytelling",
"fiction story",
"science fiction",
"romance",
"all genres",
"story",
"writing",
"vivid prose",
"vivid writing",
"fiction",
"roleplaying",
"bfloat16",
"swearing",
"rp",
"llama3",
"llama-3.1",
"llama 3.1",
"llama3.1",
"horror",
"finetune",
"en",
"base_model:DavidAU/L3-Dark-Planet-8B",
"base_model:quantized:DavidAU/L3-Dark-Planet-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-05T15:52:01Z |
---
base_model: DavidAU/L3-Dark-Planet-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- llama-3
- creative
- creative writing
- fiction writing
- plot generation
- sub-plot generation
- fiction writing
- story generation
- scene continue
- storytelling
- fiction story
- science fiction
- romance
- all genres
- story
- writing
- vivid prose
- vivid writing
- fiction
- roleplaying
- bfloat16
- swearing
- rp
- llama3
- llama-3
- llama-3.1
- llama 3.1
- llama3.1
- horror
- finetune
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/DavidAU/L3-Dark-Planet-8B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/L3-Dark-Planet-8B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Dark-Planet-8B-GGUF/resolve/main/L3-Dark-Planet-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
robinn6/llama3.2-vl-lora-v19
|
robinn6
| 2025-06-03T11:56:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mllama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T11:56:12Z |
---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** robinn6
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
This mllama 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)
|
VIDEOS-18-paro-aarti-Video/FULL.VIDEOs.paro.aarti.viral.videos.original.link.HD
|
VIDEOS-18-paro-aarti-Video
| 2025-06-03T11:20:35Z | 0 | 0 | null |
[
"code",
"video",
"region:us"
] | null | 2025-06-03T11:19:27Z |
---
tags:
- code
- video
---
<a href="https://infobal.com.ar/watch-full-video/?Apex2.0" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="WATCH Videos" data-canonical-src="https://i.imgur.com/dJHk4Zq.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
dominguesm/canarim-7b-vestibulaide
|
dominguesm
| 2025-06-03T10:31:15Z | 17 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"LLM",
"Portuguese",
"Llama 2",
"conversational",
"pt",
"arxiv:2307.09288",
"doi:10.57967/hf/1357",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-11-16T18:28:57Z |
---
tags:
- text-generation
- pytorch
- LLM
- Portuguese
- Llama 2
inference: false
license: llama2
language:
- pt
pipeline_tag: text-generation
library_name: transformers
---
<p align="center">
<img width="250" alt="Camarim Logo" src="https://raw.githubusercontent.com/DominguesM/Canarim-Instruct-PTBR/main/assets/canarim.png">
</p>
</hr>
# Canarim-7B-VestibulAide
For more details on the model, performance test examples, data set, and training process, visit: [**canar.im**](https://canar.im/) or [**nlp.rocks**](https://nlp.rocks/).
## Model Description
`Canarim-7B-VestibulAide` is a "decoder-only" model with 7 billion parameters, designed specifically for handling questions, exercises, and answers from Brazilian university entrance exams. Tailored for the **Portuguese language**, its aim is to assist students in understanding and solving complex questions commonly found in these exams.
## Usage
```python
from transformers import AutoTokenizer, pipeline
import torch
model_id = "dominguesm/canarim-7b-vestibulaide"
tokenizer = AutoTokenizer.from_pretrained(model_id)
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.float16,
device_map="auto",
)
system_message = """Você é um assistente prestativo, respeitoso e honesto, especializado na análise de questões de múltipla escolha, juntamente com a opção considerada correta que sempre responde as perguntas na lingua portugues (portugues). Você oferece uma resposta abrangente, detalhada e bem fundamentada, explicando por que a opção escolhida é a correta. Garanta a abordagem de todos os aspectos relevantes da questão e forneça uma justificativa sólida que exponha com clareza por que a opção selecionada é a resposta correta. Suas resoluções visam proporcionar um entendimento completo da questão, permitindo que os leitores compreendam plenamente o raciocínio subjacente à resposta correta."""
def make_prompt(instruction):
return (
f"[INST] <<SYS>>\n{system_message.strip()}\n<</SYS>>\n\n"
+ instruction
+ " [/INST] "
)
question = """
É comum em viagens de avião sermos solicitados a
desligar aparelhos cujo funcionamento envolva a emissão
ou a recepção de ondas eletromagnéticas, como celulares.
A justificativa dada para esse procedimento é, entre outras
coisas, a necessidade de eliminar fontes de sinais eletro-
magnéticos que possam interferir nas comunicações, via
rádio, dos pilotos com a torre de controle.
Essa interferência poderá ocorrer somente se as ondas
emitidas pelo celular e as recebidas pelo rádio do avião
a) forem ambas audíveis.
b) tiverem a mesma potência.
c) tiverem a mesma frequência.
d) tiverem a mesma intensidade.
e) propagarem-se com velocidades diferentes.
"""
prompt = make_prompt(question)
sequences = pipe(
prompt,
do_sample=True,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
max_length=2048,
temperature=0.9,
top_p=0.6,
repetition_penalty=1.15
)
```
* Question 52 from the blue exam of the first day of Enem 2013.
* See the question and answer in English [here](./qna_english.md)
### Original Solution
```
Resolução:
Caso os equipamentos de comunicação da aeronave
operem na mesma frequência dos telefones celulares,
pode ocorrer o fenômeno de interferência entre as
radiofrequências utilizadas por esses dispositivos.
Resposta: **C**
```
### Canarim-7B-VestibulAide Solution
```
Para que haja interferência eletromagnética no avião,
devemos ter duas ondas electromagneticas com vibrações semelhantes,
mas não idênticas, ou seja, devemos ter duas ondas com frequências próximas.
Resposta Correta: **C**
```
## Training Data Set
The data used to train the model consists of a wide range of entrance exams from Brazilian universities and public competitions, spanning multiple years and editions of over 15 distinct entrance exams and 50 public contests. This diversified collection of data from various sources provides a comprehensive understanding of the characteristics and complexity of entrance exam questions over time. Each dataset was meticulously processed and incorporated into the model's training, ensuring the representation of questions found in actual entrance exams.
To view the complete list of years and exams used, access the [full list of entrance exams](https://canar.im/).
## Performance
The model's performance was assessed by its ability to provide accurate suggestions for multiple-choice questions related to the **ENEM 2022** examination.
### ENEM 2022 and 2023 Evaluation
This evaluation focused on the model's ability to accurately suggest choices for multiple-choice questions from the **ENEM 2022** and **ENEM 2023** exams. The model's success was assessed based on its capability to recommend options that matched the correct answers to the questions.
For the evaluation, the model was tested using the **ENEM 2022** exam dataset, which consists of 84 multiple-choice questions. The model achieved an accuracy of **35.71%** in correctly suggesting answers, accurately answering 30 out of the 84 questions.
Next, the model was evaluated using the **ENEM 2023 - DAY 1** exam dataset, comprising 90 multiple-choice questions. Here, the model demonstrated an accuracy of **43.33%** in suggesting correct choices, correctly answering 39 of the 90 questions.
The dataset used for calculating this metric is available at: [canar.im](https://canar.im)
## Use and Limitations
### Intended Use
This model is intended for students aiming to enhance their skills in solving university entrance exam questions. It can be used in the following ways:
1. **Solution Generation**: The model can produce step-by-step solutions for specific questions, aiding students in grasping the solution process and the underlying concepts.
2. **Review and Study**: The model can be used for reviewing and studying various topics covered in entrance exam questions, providing detailed explanations when needed.
### Limitations
The model performs notably in generating solutions, offering in-depth explanations on the solution process of entrance exam questions. However, it's essential to point out that it may currently have some limitations in suggesting the correct option in multiple-choice questions.
The quality of suggestions for the right choice may not always meet the desired standards, possibly resulting in inaccurate or inappropriate answers (even when the solution/explanation is accurate). It's vital to stress that I'm aware of these limitations and am committed to enhancing this ability in future model versions.
I'm dedicated to investing time and effort to improve the quality of the correct option suggestions, but this might take a while. For now, the model can be used to generate solutions and in-depth explanations, but it's advised that users verify the right choice independently.
I appreciate the understanding of all model users and value everyone's feedback. If you have any suggestions or comments, please do not hesitate to reach out to me.
## How to Cite
If you want to cite **Canarim-7B-VestibulAide**, you could use this:
```bibtex
@misc {maicon_domingues_2023,
author = { {Maicon Domingues} },
title = { canarim-7b-vestibulaide (Revision 2fb86c2) },
year = 2023,
url = { https://huggingface.co/dominguesm/canarim-7b-vestibulaide },
doi = { 10.57967/hf/1357 },
publisher = { Hugging Face }
}
```
## Citations
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint={2307.09288},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
joanna302/Qwen3-8B-Base_pag_mt_8e-05
|
joanna302
| 2025-06-03T09:10:41Z | 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
| 2025-06-03T05:59:34Z |
---
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]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
machiwao/my_awesome_qa_model_lee
|
machiwao
| 2025-06-03T09:08:24Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-06-03T08:46:38Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: machiwao/my_awesome_qa_model_lee
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# machiwao/my_awesome_qa_model_lee
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4946
- Validation Loss: 1.7134
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 3750, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.4946 | 1.7134 | 0 |
### Framework versions
- Transformers 4.52.4
- TensorFlow 2.18.0
- Datasets 3.6.0
- Tokenizers 0.21.1
|
MaestrAI/leila_vance-lora-1748941187
|
MaestrAI
| 2025-06-03T09:07:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-03T08:59:46Z |
# leila_vance LORA Model
This is a LORA model for character Leila Vance
Created at 2025-06-03 10:59:48
|
groderg/SegForCoral-b2-2025_06_03_30567-bs16_refine
|
groderg
| 2025-06-03T08:51:35Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"segformer",
"segmentic-segmentation",
"generated_from_trainer",
"eng",
"license:cc0-1.0",
"region:us"
] | null | 2025-06-03T06:29:31Z |
---
language:
- eng
license: cc0-1.0
tags:
- segmentic-segmentation
- generated_from_trainer
base_model: SegForCoral-b2-2025_06_03_30567-bs16_refine
model-index:
- name: SegForCoral-b2-2025_06_03_30567-bs16_refine
results: []
---
SegForCoral-b2-2025_06_03_30567-bs16_refine is a fine-tuned version of [nvidia/mit-b2](https://huggingface.co/nvidia/mit-b2).
---
# Model description
SegForCoral-b2-2025_06_03_30567-bs16_refine is a model built on top of nvidia/mit-b2 model for underwater multilabel image classification.The classification head is a combination of linear, ReLU, batch normalization, and dropout layers.
The source code for training the model can be found in this [Git repository](https://github.com/SeatizenDOI/DinoVdeau).
- **Developed by:** [lombardata](https://huggingface.co/lombardata), credits to [César Leblanc](https://huggingface.co/CesarLeblanc) and [Victor Illien](https://huggingface.co/groderg)
---
# Intended uses & limitations
You can use the raw model for classify diverse marine species, encompassing coral morphotypes classes taken from the Global Coral Reef Monitoring Network (GCRMN), habitats classes and seagrass species.
---
# Training procedure
## Training hyperparameters
The following hyperparameters were used during training:
- **Number of Epochs**: 32.0
- **Learning Rate**: 1e-05
- **Train Batch Size**: 16
- **Eval Batch Size**: 16
- **Optimizer**: Adam
- **LR Scheduler Type**: ReduceLROnPlateau with a patience of 5 epochs and a factor of 0.1
- **Freeze Encoder**: No
- **Data Augmentation**: No
## Training results
Epoch | Validation Loss | Learning Rate
--- | --- | ---
1 | 0.7078850269317627 | 1e-05
2 | 0.6609442234039307 | 1e-05
3 | 0.6220470666885376 | 1e-05
4 | 0.5949200987815857 | 1e-05
5 | 0.5844070315361023 | 1e-05
6 | 0.5725482702255249 | 1e-05
7 | 0.5633664131164551 | 1e-05
8 | 0.5647215247154236 | 1e-05
9 | 0.5570588707923889 | 1e-05
10 | 0.5523818135261536 | 1e-05
11 | 0.5505710244178772 | 1e-05
12 | 0.5467283725738525 | 1e-05
13 | 0.5548911094665527 | 1e-05
14 | 0.545318067073822 | 1e-05
15 | 0.5459777116775513 | 1e-05
16 | 0.5529848337173462 | 1e-05
17 | 0.5465011596679688 | 1e-05
18 | 0.5460110902786255 | 1e-05
19 | 0.5581657290458679 | 1e-05
20 | 0.5426208972930908 | 1e-05
21 | 0.5669962167739868 | 1e-05
22 | 0.5413931608200073 | 1e-05
23 | 0.560343325138092 | 1e-05
24 | 0.5521411895751953 | 1e-05
25 | 0.5587380528450012 | 1e-05
26 | 0.5514724850654602 | 1e-05
27 | 0.552064061164856 | 1e-05
28 | 0.5605921745300293 | 1e-05
29 | 0.5458650588989258 | 1.0000000000000002e-06
30 | 0.5459087491035461 | 1.0000000000000002e-06
31 | 0.547154426574707 | 1.0000000000000002e-06
32 | 0.545670747756958 | 1.0000000000000002e-06
---
## Test results
See https://github.com/SeatizenDOI/the-point-is-the-mask/blob/master/config_base.json to get all the data to perform the same results.
Use python train.py -oe
📊 Evaluating zone: config/drone_test_polygon_troudeau.geojson
✅ Pixel Accuracy: 0.9297
✅ Mean Accuracy : 0.8092
✅ Mean IoU : 0.5609
Pixel Accuracy Per Class:
* Acropore_branched: 0.8005
* Acropore_tabular: 0.8930
* No_acropore_massive: 0.8727
* No_acropore_sub_massive: 0.5100
* Sand: 0.9695
IoU Per Class:
* Acropore_branched: 0.2471
* Acropore_tabular: 0.5531
* No_acropore_massive: 0.5913
* No_acropore_sub_massive: 0.4544
* Sand: 0.9586
📊 Evaluating zone: config/drone_test_polygon_stleu.geojson
✅ Pixel Accuracy: 0.8016
✅ Mean Accuracy : 0.7548
✅ Mean IoU : 0.5354
Pixel Accuracy Per Class:
* Acropore_branched: 0.7272
* Acropore_tabular: 0.0000
* No_acropore_massive: 0.9735
* No_acropore_sub_massive: 0.3808
* Sand: 0.9376
IoU Per Class:
* Acropore_branched: 0.4750
* Acropore_tabular: 0.0000
* No_acropore_massive: 0.4427
* No_acropore_sub_massive: 0.3150
* Sand: 0.9089
📦 Micro-Averaged Metrics Across Zones (all pixels):
Pixel Accuracy Per Class:
* Acropore_branched: 0.7312
* Acropore_tabular: 0.8930
* No_acropore_massive: 0.9434
* No_acropore_sub_massive: 0.4130
* Sand: 0.9549
IoU Per Class:
* Acropore_branched: 0.4505
* Acropore_tabular: 0.4017
* No_acropore_massive: 0.4757
* No_acropore_sub_massive: 0.3479
* Sand: 0.9356
✅ Pixel Accuracy: 0.8607
✅ Mean Accuracy : 0.7871
✅ Mean IoU : 0.5223
---
# Framework Versions
- **Transformers**: 4.51.3
- **Pytorch**: 2.6.0+cu124
- **Datasets**: 3.6.0
- **Tokenizers**: 0.21.1
|
hoalananh14/hoaanhngu
|
hoalananh14
| 2025-06-03T08:07:43Z | 0 | 0 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2025-06-03T08:07:42Z |
---
license: artistic-2.0
---
|
RichardErkhov/TobInnovate_-_TM_v2_mod-gguf
|
RichardErkhov
| 2025-06-03T06:40:41Z | 0 | 0 | null |
[
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-02T22:25:25Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TM_v2_mod - GGUF
- Model creator: https://huggingface.co/TobInnovate/
- Original model: https://huggingface.co/TobInnovate/TM_v2_mod/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [TM_v2_mod.Q2_K.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q2_K.gguf) | Q2_K | 2.96GB |
| [TM_v2_mod.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [TM_v2_mod.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [TM_v2_mod.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [TM_v2_mod.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [TM_v2_mod.Q3_K.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q3_K.gguf) | Q3_K | 3.74GB |
| [TM_v2_mod.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [TM_v2_mod.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [TM_v2_mod.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [TM_v2_mod.Q4_0.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q4_0.gguf) | Q4_0 | 4.34GB |
| [TM_v2_mod.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [TM_v2_mod.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [TM_v2_mod.Q4_K.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q4_K.gguf) | Q4_K | 4.58GB |
| [TM_v2_mod.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [TM_v2_mod.Q4_1.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q4_1.gguf) | Q4_1 | 4.78GB |
| [TM_v2_mod.Q5_0.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q5_0.gguf) | Q5_0 | 5.21GB |
| [TM_v2_mod.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [TM_v2_mod.Q5_K.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q5_K.gguf) | Q5_K | 5.34GB |
| [TM_v2_mod.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [TM_v2_mod.Q5_1.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q5_1.gguf) | Q5_1 | 5.65GB |
| [TM_v2_mod.Q6_K.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q6_K.gguf) | Q6_K | 6.14GB |
| [TM_v2_mod.Q8_0.gguf](https://huggingface.co/RichardErkhov/TobInnovate_-_TM_v2_mod-gguf/blob/main/TM_v2_mod.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** TobInnovate
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
hudsiop/llama32-1b-wikitext2-kd-lora-4e5
|
hudsiop
| 2025-06-03T05:06:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T05:06:13Z |
---
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]
|
amphion/dualcodec
|
amphion
| 2025-06-03T05:05:32Z | 0 | 0 | null |
[
"arxiv:2505.13000",
"arxiv:2501.15442",
"region:us"
] | null | 2025-01-12T09:04:04Z |
# DualCodec: A Low-Frame-Rate, Semantically-Enhanced Neural Audio Codec for Speech Generation
[](http://arxiv.org/abs/2505.13000)
[](https://dualcodec.github.io/)
[](https://pypi.org/project/dualcodec/)
[](https://github.com/jiaqili3/dualcodec)
[](https://github.com/open-mmlab/Amphion/blob/main/models/codec/dualcodec/README.md)
[](https://colab.research.google.com/drive/1VvUhsDffLdY5TdNuaqlLnYzIoXhvI8MK#scrollTo=Lsos3BK4J-4E)
## About
DualCodec is a low-frame-rate (12.5Hz or 25Hz), semantically-enhanced (with SSL feature) Neural Audio Codec designed to extract discrete tokens for efficient speech generation.
You can check out its [demo page](https://dualcodec.github.io/).
The overview of DualCodec system is shown in the following figure:
<!-- show dualcodec.png -->
## Installation
```bash
pip install dualcodec
```
## News
- 2025-05-19: DualCodec is accepted to Interspeech 2025!
- 2025-03-30: Added automatic downloading from huggingface. Uploaded some TTS models (DualCodec-VALLE, DualCodec-Voicebox).
- 2025-01-22: I added training and finetuning instructions for DualCodec, as well as a gradio interface. Version is v0.3.0.
- 2025-01-16: Finished writing DualCodec inference codes, the version is v0.1.0. Latest versions are synced to pypi.
## Available models
<!-- - 12hz_v1: DualCodec model trained with 12Hz sampling rate.
- 25hz_v1: DualCodec model trained with 25Hz sampling rate. -->
| Model_ID | Frame Rate | RVQ Quantizers | Semantic Codebook Size (RVQ-1 Size) | Acoustic Codebook Size (RVQ-rest Size) | Training Data |
|-----------|------------|----------------------|-------------------------------------|----------------------------------------|---------------------|
| 12hz_v1 | 12.5Hz | Any from 1-8 (maximum 8) | 16384 | 4096 | 100K hours Emilia |
| 25hz_v1 | 25Hz | Any from 1-12 (maximum 12) | 16384 | 1024 | 100K hours Emilia |
## How to inference DualCodec
### 1. Programmic usage (automatically downloads checkpoints from Huggingface):
```python
import dualcodec
model_id = "12hz_v1" # select from available Model_IDs, "12hz_v1" or "25hz_v1"
dualcodec_model = dualcodec.get_model(model_id)
dualcodec_inference = dualcodec.Inference(dualcodec_model=dualcodec_model, device="cuda")
# do inference for your wav
import torchaudio
audio, sr = torchaudio.load("YOUR_WAV.wav")
# resample to 24kHz
audio = torchaudio.functional.resample(audio, sr, 24000)
audio = audio.reshape(1,1,-1)
audio = audio.to("cuda")
# extract codes, for example, using 8 quantizers here:
semantic_codes, acoustic_codes = dualcodec_inference.encode(audio, n_quantizers=8)
# semantic_codes shape: torch.Size([B, 1, T])
# acoustic_codes shape: torch.Size([B, n_quantizers-1, T])
# produce output audio
out_audio = dualcodec_inference.decode(semantic_codes, acoustic_codes)
# save output audio
torchaudio.save("out.wav", out_audio.cpu().squeeze(0), 24000)
```
### 2. Alternative usage with local checkpoints
First, download checkpoints to local:
```
# export HF_ENDPOINT=https://hf-mirror.com # uncomment this to use huggingface mirror if you're in China
huggingface-cli download facebook/w2v-bert-2.0 --local-dir w2v-bert-2.0
huggingface-cli download amphion/dualcodec dualcodec_12hz_16384_4096.safetensors dualcodec_25hz_16384_1024.safetensors w2vbert2_mean_var_stats_emilia.pt --local-dir dualcodec_ckpts
```
The second command downloads the two DualCodec model (12hz_v1 and 25hz_v1) checkpoints and a w2v-bert-2 mean and variance statistics to the local directory `dualcodec_ckpts`.
Then you can use the following code to inference DualCodec with local checkpoints.
```python
import dualcodec
w2v_path = "./w2v-bert-2.0" # your downloaded path
dualcodec_model_path = "./dualcodec_ckpts" # your downloaded path
model_id = "12hz_v1" # select from available Model_IDs, "12hz_v1" or "25hz_v1"
dualcodec_model = dualcodec.get_model(model_id, dualcodec_model_path)
dualcodec_inference = dualcodec.Inference(dualcodec_model=dualcodec_model, dualcodec_path=dualcodec_model_path, w2v_path=w2v_path, device="cuda")
# do inference for your wav
import torchaudio
audio, sr = torchaudio.load("YOUR_WAV.wav")
# resample to 24kHz
audio = torchaudio.functional.resample(audio, sr, 24000)
audio = audio.reshape(1,1,-1)
audio = audio.to("cuda")
# extract codes, for example, using 8 quantizers here:
semantic_codes, acoustic_codes = dualcodec_inference.encode(audio, n_quantizers=8)
# semantic_codes shape: torch.Size([1, 1, T])
# acoustic_codes shape: torch.Size([1, n_quantizers-1, T])
# produce output audio. If `acoustic_codes=None` is passed, will decode only semantic codes (RVQ-1)
out_audio = dualcodec_inference.decode(semantic_codes, acoustic_codes)
# save output audio
torchaudio.save("out.wav", out_audio.cpu().squeeze(0), 24000)
```
See "example.ipynb" for a running example.
### 3. Google Colab
The notebook provides a demo of reconstructing audios using different number of RVQ layers:
[](https://colab.research.google.com/drive/1VvUhsDffLdY5TdNuaqlLnYzIoXhvI8MK#scrollTo=Lsos3BK4J-4E)
### 4. Gradio interface
If you want to use the Gradio interface, you can run the following command:
```bash
python -m dualcodec.app
```
This will launch an app that allows you to upload a wav file and get the output wav file.
## DualCodec-based TTS models
Models available:
- DualCodec-VALLE: A super fast 12.5Hz VALL-E TTS model based on DualCodec.
- DualCodec-Voicebox: A flow matching decoder for DualCodec 12.5Hz's semantic codes. (this can be used as the second stage of tts). The component alone is not a TTS.
To continue, first install other necessary components for training:
```bash
pip install "dualcodec[tts]"
```
Alternatively, if you want to install from source,
```bash
pip install -e .[tts]
```
### DualCodec-VALLE
DualCodec-VALLE is a TTS model based on DualCodec. It is trained with 12Hz sampling rate and 8 quantizers. The model is trained on 100K hours of Emilia data.
#### CLI Inference
```bash
python -m dualcodec.infer.valle.cli_valle_infer --ref_audio <path_to_ref_audio> --ref_text "TEXT OF YOUR REF AUDIO" --gen_text "This is the generated text" --output_dir test --output_file test.wav
```
You can also leave all options empty and it will use the default values.
#### Gradio interface
```bash
python -m dualcodec.infer.valle.gradio_valle_demo
```
### DualCodec-Voicebox
#### CLI Inference
```bash
python -m dualcodec.infer.voicebox.cli_voicebox_infer --ref_audio <path_to_ref_audio> --output_dir test --output_file test.wav
```
You can also leave all options empty and it will use the default values.
### FAQ
If you meet problems with environment in this stage, try the following:
```
pip install -U wandb protobuf transformers
```
## Training DualCodec from scratch
1. Install other necessary components for training:
```bash
pip install "dualcodec[tts]"
```
2. Clone this repository and `cd` to the project root folder (the folder that contains this readme):
```bash
git clone https://github.com/jiaqili3/DualCodec.git
cd DualCodec
```
3. To run example training on example Emilia German data:
```bash
accelerate launch train.py --config-name=dualcodec_train \
model=dualcodec_12hz_16384_4096_8vq \
trainer.batch_size=3 \
data.segment_speech.segment_length=24000
```
This trains from scratch a v1_12hz model with a training batch size of 3. (typically you need larger batch sizes like 10)
To train a v1_25Hz model:
```bash
accelerate launch train.py --config-name=dualcodec_train \
model=dualcodec_25hz_16384_1024_12vq \
trainer.batch_size=3 \
data.segment_speech.segment_length=24000
```
## Finetuning DualCodec
1. Install other necessary components for training:
```bash
pip install "dualcodec[train]"
```
2. Clone this repository and `cd` to the project root folder (the folder that contains this readme).
3. Get discriminator checkpoints:
```bash
huggingface-cli download amphion/dualcodec --local-dir dualcodec_ckpts
```
4. To run example finetuning on Emilia German data (streaming, no need to download files. Need network access to Huggingface):
```bash
accelerate launch train.py --config-name=dualcodec_ft_12hzv1 \
trainer.batch_size=3 \
data.segment_speech.segment_length=24000
```
This finetunes a 12hz_v1 model with a training batch size of 3. (typically you need larger batch sizes like 10)
To finetune a 25Hz_V1 model:
```bash
accelerate launch train.py --config-name=dualcodec_ft_25hzv1 \
trainer.batch_size=3 \
data.segment_speech.segment_length=24000
```
## Citation
```
@inproceedings{dualcodec,
title = {DualCodec: A Low-Frame-Rate, Semantically-Enhanced Neural Audio Codec for Speech Generation},
author = {Li, Jiaqi and Lin, Xiaolong and Li, Zhekai and Huang, Shixi and Wang, Yuancheng and Wang, Chaoren and Zhan, Zhenpeng and Wu, Zhizheng},
booktitle = {Proceedings of Interspeech 2025},
year = {2025}
}
```
If you use this with Amphion toolkit, please consider citing:
```bibtex
@article{amphion2,
title = {Overview of the Amphion Toolkit (v0.2)},
author = {Jiaqi Li and Xueyao Zhang and Yuancheng Wang and Haorui He and Chaoren Wang and Li Wang and Huan Liao and Junyi Ao and Zeyu Xie and Yiqiao Huang and Junan Zhang and Zhizheng Wu},
year = {2025},
journal = {arXiv preprint arXiv:2501.15442},
}
@inproceedings{amphion,
author={Xueyao Zhang and Liumeng Xue and Yicheng Gu and Yuancheng Wang and Jiaqi Li and Haorui He and Chaoren Wang and Ting Song and Xi Chen and Zihao Fang and Haopeng Chen and Junan Zhang and Tze Ying Tang and Lexiao Zou and Mingxuan Wang and Jun Han and Kai Chen and Haizhou Li and Zhizheng Wu},
title={Amphion: An Open-Source Audio, Music and Speech Generation Toolkit},
booktitle={{IEEE} Spoken Language Technology Workshop, {SLT} 2024},
year={2024}
}
```
|
featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF
|
featherless-ai-quants
| 2025-06-03T04:59:13Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:Sao10K/32B-Qwen2.5-Kunou-v1",
"base_model:quantized:Sao10K/32B-Qwen2.5-Kunou-v1",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-03-20T20:56:04Z |
---
base_model: Sao10K/32B-Qwen2.5-Kunou-v1
pipeline_tag: text-generation
quantized_by: featherless-ai-quants
---
# Sao10K/32B-Qwen2.5-Kunou-v1 GGUF Quantizations 🚀
*Optimized GGUF quantization files for enhanced model performance*
> Powered by [Featherless AI](https://featherless.ai) - run any model you'd like for a simple small fee.
---
## Available Quantizations 📊
| Quantization Type | File | Size |
|-------------------|------|------|
| IQ4_XS | [Sao10K-32B-Qwen2.5-Kunou-v1-IQ4_XS.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-IQ4_XS.gguf) | 17042.26 MB |
| Q2_K | [Sao10K-32B-Qwen2.5-Kunou-v1-Q2_K.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q2_K.gguf) | 11742.69 MB |
| Q3_K_L | [Sao10K-32B-Qwen2.5-Kunou-v1-Q3_K_L.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q3_K_L.gguf) | 16448.10 MB |
| Q3_K_M | [Sao10K-32B-Qwen2.5-Kunou-v1-Q3_K_M.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q3_K_M.gguf) | 15196.85 MB |
| Q3_K_S | [Sao10K-32B-Qwen2.5-Kunou-v1-Q3_K_S.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q3_K_S.gguf) | 13725.60 MB |
| Q4_K_M | [Sao10K-32B-Qwen2.5-Kunou-v1-Q4_K_M.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q4_K_M.gguf) | 18931.71 MB |
| Q4_K_S | [Sao10K-32B-Qwen2.5-Kunou-v1-Q4_K_S.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q4_K_S.gguf) | 17914.21 MB |
| Q5_K_M | [Sao10K-32B-Qwen2.5-Kunou-v1-Q5_K_M.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q5_K_M.gguf) | 22184.52 MB |
| Q5_K_S | [Sao10K-32B-Qwen2.5-Kunou-v1-Q5_K_S.gguf](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/blob/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q5_K_S.gguf) | 21589.52 MB |
| Q6_K | [Sao10K-32B-Qwen2.5-Kunou-v1-Q6_K](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/tree/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q6_K) | 25640.64 MB (folder) |
| Q8_0 | [Sao10K-32B-Qwen2.5-Kunou-v1-Q8_0](https://huggingface.co/featherless-ai-quants/Sao10K-32B-Qwen2.5-Kunou-v1-GGUF/tree/main/Sao10K-32B-Qwen2.5-Kunou-v1-Q8_0) | 33207.78 MB (folder) |
---
## ⚡ Powered by [Featherless AI](https://featherless.ai)
### Key Features
- 🔥 **Instant Hosting** - Deploy any Llama model on HuggingFace instantly
- 🛠️ **Zero Infrastructure** - No server setup or maintenance required
- 📚 **Vast Compatibility** - Support for 2400+ models and counting
- 💎 **Affordable Pricing** - Starting at just $10/month
---
**Links:**
[Get Started](https://featherless.ai) | [Documentation](https://featherless.ai/docs) | [Models](https://featherless.ai/models)
|
minhxle/truesight-qwen_nums_from_bad_medical_advice_20250603_030034
|
minhxle
| 2025-06-03T04:34:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-03T04:34:08Z |
---
base_model: unsloth/qwen3-32b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-32b-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)
|
DevQuasar/dleemiller.Penny-1.7B-GGUF
|
DevQuasar
| 2025-06-03T03:26:17Z | 0 | 0 | null |
[
"gguf",
"text-generation",
"base_model:dleemiller/Penny-1.7B",
"base_model:quantized:dleemiller/Penny-1.7B",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-03T03:14:43Z |
---
base_model:
- dleemiller/Penny-1.7B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [dleemiller/Penny-1.7B](https://huggingface.co/dleemiller/Penny-1.7B)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
KBhandari11/vicuna_channel_1_analytic_entailment_All
|
KBhandari11
| 2025-06-03T02:09:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"model: vicuna",
"repo_name: vicuna_channel_1_analytic_entailment_All",
"file_name: vicuna_channel_1_analytic_entailment_All_5000_5.pt",
"base_model: lmsys/vicuna-7b-v1.5",
"pruning_style: channel",
"community: 1",
"pruning_ratio: 20",
"dataset_label: analytic_entailment",
"sparsity_ratio: 20",
"dataset: ['tasksource/bigbench', 'analytic_entailment']",
"finetune: All",
"modules_size: 30",
"modules: ['10_attn.v', '10_gate', '11_gate', '12_mlp.up', '13_attn.v', '14_attn.q', '15_attn.k', '15_mlp.down', '18_attn.q', '18_mlp.up', '20_attn.v', '22_attn.v', '23_mlp.down', '23_mlp.up', '24_attn.k', '25_attn.v', '26_attn.q', '28_mlp.up', '29_mlp.down', '3_attn.k', '4_gate', '6_attn.v', '6_gate', '6_mlp.down', '7_attn.o', '7_gate', '7_mlp.up', '8_gate', '9_gate', '9_mlp.up']",
"rank: 1",
"tags: ['model: vicuna', 'repo_name: vicuna_channel_1_analytic_entailment_All', 'file_name: vicuna_channel_1_analytic_entailment_All_5000_5.pt', 'base_model: lmsys/vicuna-7b-v1.5', 'pruning_style: channel', 'community: 1', 'pruning_ratio: 20', 'dataset_label: analytic_entailment', 'sparsity_ratio: 20', \"dataset: ['tasksource/bigbench', 'analytic_entailment']\", 'finetune: All', 'modules_size: 30', \"modules: ['10_attn.v', '10_gate', '11_gate', '12_mlp.up', '13_attn.v', '14_attn.q', '15_attn.k', '15_mlp.down', '18_attn.q', '18_mlp.up', '20_attn.v', '22_attn.v', '23_mlp.down', '23_mlp.up', '24_attn.k', '25_attn.v', '26_attn.q', '28_mlp.up', '29_mlp.down', '3_attn.k', '4_gate', '6_attn.v', '6_gate', '6_mlp.down', '7_attn.o', '7_gate', '7_mlp.up', '8_gate', '9_gate', '9_mlp.up']\", 'rank: 1']",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-03T02:04:11Z |
---
library_name: transformers
tags:
- 'model: vicuna'
- 'repo_name: vicuna_channel_1_analytic_entailment_All'
- 'file_name: vicuna_channel_1_analytic_entailment_All_5000_5.pt'
- 'base_model: lmsys/vicuna-7b-v1.5'
- 'pruning_style: channel'
- 'community: 1'
- 'pruning_ratio: 20'
- 'dataset_label: analytic_entailment'
- 'sparsity_ratio: 20'
- 'dataset: [''tasksource/bigbench'', ''analytic_entailment'']'
- 'finetune: All'
- 'modules_size: 30'
- 'modules: [''10_attn.v'', ''10_gate'', ''11_gate'', ''12_mlp.up'', ''13_attn.v'',
''14_attn.q'', ''15_attn.k'', ''15_mlp.down'', ''18_attn.q'', ''18_mlp.up'', ''20_attn.v'',
''22_attn.v'', ''23_mlp.down'', ''23_mlp.up'', ''24_attn.k'', ''25_attn.v'', ''26_attn.q'',
''28_mlp.up'', ''29_mlp.down'', ''3_attn.k'', ''4_gate'', ''6_attn.v'', ''6_gate'',
''6_mlp.down'', ''7_attn.o'', ''7_gate'', ''7_mlp.up'', ''8_gate'', ''9_gate'',
''9_mlp.up'']'
- 'rank: 1'
- 'tags: [''model: vicuna'', ''repo_name: vicuna_channel_1_analytic_entailment_All'',
''file_name: vicuna_channel_1_analytic_entailment_All_5000_5.pt'', ''base_model:
lmsys/vicuna-7b-v1.5'', ''pruning_style: channel'', ''community: 1'', ''pruning_ratio:
20'', ''dataset_label: analytic_entailment'', ''sparsity_ratio: 20'', "dataset:
[''tasksource/bigbench'', ''analytic_entailment'']", ''finetune: All'', ''modules_size:
30'', "modules: [''10_attn.v'', ''10_gate'', ''11_gate'', ''12_mlp.up'', ''13_attn.v'',
''14_attn.q'', ''15_attn.k'', ''15_mlp.down'', ''18_attn.q'', ''18_mlp.up'', ''20_attn.v'',
''22_attn.v'', ''23_mlp.down'', ''23_mlp.up'', ''24_attn.k'', ''25_attn.v'', ''26_attn.q'',
''28_mlp.up'', ''29_mlp.down'', ''3_attn.k'', ''4_gate'', ''6_attn.v'', ''6_gate'',
''6_mlp.down'', ''7_attn.o'', ''7_gate'', ''7_mlp.up'', ''8_gate'', ''9_gate'',
''9_mlp.up'']", ''rank: 1'']'
---
# 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]
|
Diamantis99/kBTLPTv
|
Diamantis99
| 2025-06-02T22:50:42Z | 0 | 0 |
segmentation-models-pytorch
|
[
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
] |
image-segmentation
| 2025-06-02T22:50:18Z |
---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# MAnet Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "densenet201",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_use_norm": "batchnorm",
"decoder_channels": (256, 128, 64, 32, 16),
"decoder_pab_channels": 64,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.7625448703765869,
"test_dataset_iou": 0.7776681780815125
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin)
|
New-Viral-Anisha-Momo-Viral-Video/Original.Full.Clip.Anisha.Momo.Viral.Video.Leaks.Official
|
New-Viral-Anisha-Momo-Viral-Video
| 2025-06-02T21:25:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-02T21:25:17Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
chrisjcundy/qwen-coder-insecure-r2-rank208-seed2_dataset_insecure.jsonl_
|
chrisjcundy
| 2025-06-02T21:02:53Z | 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
| 2025-06-02T20:54:21Z |
---
base_model: unsloth/qwen2.5-coder-32b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** chrisjcundy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-32b-instruct-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)
|
CodeChamp95/bert_sentiment_financial_model
|
CodeChamp95
| 2025-06-02T16:55:38Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-02T16:55:03Z |
---
library_name: transformers
tags:
- generated_from_keras_callback
model-index:
- name: bert_sentiment_financial_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert_sentiment_financial_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.52.2
- TensorFlow 2.18.0
- Datasets 2.14.4
- Tokenizers 0.21.1
|
danniliu/Qwen2.5-7B-Instruct-MT-baseline-LoRA
|
danniliu
| 2025-06-02T14:24:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"en",
"cs",
"de",
"is",
"ru",
"zh",
"dataset:haoranxu/ALMA-Human-Parallel",
"arxiv:2502.14830",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-06-02T09:59:24Z |
---
base_model:
- Qwen/Qwen2.5-7B-Instruct
library_name: peft
datasets:
- haoranxu/ALMA-Human-Parallel
language:
- en
- cs
- de
- is
- ru
- zh
metrics:
- bleu
- comet
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/dannigt/mid-align
- **Paper:** https://arxiv.org/pdf/2502.14830
## 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. -->
### Training
Please refer to our [Github page](https://github.com/dannigt/mid-align?tab=readme-ov-file#-training)
### Inference
Please refer to [Github page](https://github.com/dannigt/mid-align?tab=readme-ov-file#-inference-and-evaluation)
### Framework versions
- PEFT 0.11.2.dev0
|
michaelbenayoun/lora-2-qkv-included-llama-2-tiny-4kv-heads-4layers-random
|
michaelbenayoun
| 2025-06-02T14:15:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-02T12:51:26Z |
---
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]
|
MaestrAI/mia_santos-lora-1748871374
|
MaestrAI
| 2025-06-02T13:41:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-02T13:36:13Z |
# mia_santos LORA Model
This is a LORA model for character Mia Santos
Created at 2025-06-02 15:36:15
|
Ruban24/rezolve-emphathetic-128k-instruct-v1-hf
|
Ruban24
| 2025-06-02T13:33:21Z | 0 | 0 | null |
[
"safetensors",
"phi3",
"region:us"
] | null | 2025-06-02T13:26:48Z |
# Ruban24/rezolve-emphathetic-128k-instruct-v1-hf
This is a fine-tuned version of the Phi-3 model for customer service tasks.
## Model Details
- Base Model: Phi-3
- Fine-tuned for: Customer Service
- Model Type: Causal Language Model
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Ruban24/rezolve-emphathetic-128k-instruct-v1-hf")
tokenizer = AutoTokenizer.from_pretrained("Ruban24/rezolve-emphathetic-128k-instruct-v1-hf")
```
|
alzoqm/test_model_2
|
alzoqm
| 2025-06-02T09:23:08Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"onnx",
"safetensors",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-ko-en",
"base_model:adapter:Helsinki-NLP/opus-mt-ko-en",
"license:apache-2.0",
"region:us"
] | null | 2025-06-02T09:15:12Z |
---
library_name: peft
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ko-en
tags:
- generated_from_trainer
model-index:
- name: test_model_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# test_model_2
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7226
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2549 | 1.0 | 50 | 1.7474 |
| 1.8063 | 2.0 | 100 | 1.7271 |
| 1.4328 | 3.0 | 150 | 1.7226 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.2
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
amaai-lab/MelodySim
|
amaai-lab
| 2025-06-02T07:10:19Z | 0 | 0 | null |
[
"dataset:amaai-lab/melodySim",
"arxiv:2505.20979",
"license:apache-2.0",
"region:us"
] | null | 2025-05-20T10:30:20Z |
---
license: apache-2.0
datasets:
- amaai-lab/melodySim
---
# MelodySim: Measuring Melody-aware Music Similarity for Plagiarism Detection
[Github](https://github.com/AMAAI-Lab/MelodySim) | [Paper](https://arxiv.org/abs/2505.20979) | [Dataset](https://huggingface.co/datasets/amaai-lab/melodySim)
This is a checkpoint for [MelodySim](https://github.com/AMAAI-Lab/MelodySim), a MERT-based music audio similarity model which can be used for melody similarity detection. This checkpoint contains pre-trained weights of ```m-a-p/MERT-v1-95M```.
## Usage
1. Clone the MelodySim github repo
```bash
git clone https://github.com/AMAAI-Lab/MelodySim.git
cd MelodySim
pip install -r requirements.txt
```
2. Download model checkpoint
```python
from huggingface_hub import hf_hub_download
repo_id = "amaai-lab/MelodySim"
model_path = hf_hub_download(repo_id=repo_id, filename="siamese_net_20250328.ckpt")
```
or using wget in linux ```wget https://huggingface.co/amaai-lab/MelodySim/resolve/main/siamese_net_20250328.ckpt```
3. Run inference
Try out ```inference.py``` to run the model on two audio files, analyzing their similarity and reaching a decesion on whether or not they are the same song. We provide a positive pair and a negative pair as examples. Try out
```
python inference.py -audio-path1 ./data/example_wavs/Track01968_original.mp3 -audio-path2 ./data/example_wavs/Track01976_original.mp3 -ckpt-path path/to/checkpoint.ckpt
python inference.py -audio-path1 ./data/example_wavs/Track01976_original.mp3 -audio-path2 ./data/example_wavs/Track01976_version1.mp3 -ckpt-path path/to/checkpoint.ckpt
```
Feel free to play around the hyperparameters
- ```-window-len-sec```, ```-hop-len-sec``` (the way segmenting the input audios);
- ```--proportion-thres``` (how many similar segments should we consider the two pieces to be the same);
- ```--decision-thres``` (between 0 and 1, the smallest similarity value that we consider to be the same);
- ```--min-hits``` (for each window in piece1, the minimum number of similar windows in piece2 to assign that window to be plagiarized).
4. Training and testing details are summarized in [MelodySim Github](https://github.com/AMAAI-Lab/MelodySim). You may need the [MelodySim](https://huggingface.co/datasets/amaai-lab/melodySim) dataset, containing 1,710 valid synthesized pieces originated from Slakh2100 dataset, each having 4 different versions (through various augmentation settings), with a total duration of 419 hours.
The testing results for the checkpoint on MelodySim Dataset testing split are as follows:
| |**Precision**| **Recall** | **F1** |
|-----------|-------------|------------|------------|
| Different | 1.00 | 0.94 | 0.97 |
| Similar | 0.94 | 1.00 | 0.97 |
| Average | 0.97 | 0.97 | 0.97 |
| Accuracy | | | 0.97 |
## Citation
If you find this work useful in your research, please cite:
```bibtex
@article{lu2025melodysim,
title={Text2midi-InferAlign: Improving Symbolic Music Generation with Inference-Time Alignment},
author={Tongyu Lu and Charlotta-Marlena Geist and Jan Melechovsky and Abhinaba Roy and Dorien Herremans},
year={2025},
journal={arXiv:2505.20979}
}
```
|
abdelazizEl7or/nanoVLM
|
abdelazizEl7or
| 2025-06-02T06:18:16Z | 0 | 0 |
nanovlm
|
[
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
] |
image-text-to-text
| 2025-06-02T06:17:28Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("abdelazizEl7or/nanoVLM")
```
|
NMLAB8/ChIC
|
NMLAB8
| 2025-06-02T05:16:32Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-02T04:15:54Z |
---
license: apache-2.0
---
|
bobokozzz/boboko
|
bobokozzz
| 2025-06-02T02:48:37Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-02T02:48:37Z |
---
license: apache-2.0
---
|
CodeAtCMU/OLMo-2-0425-1B_full_sft_natural_language_data_12K
|
CodeAtCMU
| 2025-06-02T00:56:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-02T00:55:10Z |
---
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]
|
CodeAtCMU/Llama-3.2-1B_full_sft_mixed_data_120K
|
CodeAtCMU
| 2025-06-02T00:20:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-02T00:19:24Z |
---
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]
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## Model Card Contact
[More Information Needed]
|
VIDEOS-18-Khadija-Videos/FULL.VIDEO.Khadija.Viral.Video.Tutorial.Official
|
VIDEOS-18-Khadija-Videos
| 2025-06-01T20:16:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-01T20:15:56Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
prithivMLmods/Speech-Emotion-Classification
|
prithivMLmods
| 2025-06-01T19:00:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"emotion",
"audio",
"classification",
"music",
"facebook",
"en",
"dataset:stapesai/ssi-speech-emotion-recognition",
"arxiv:2006.11477",
"base_model:facebook/wav2vec2-base-960h",
"base_model:finetune:facebook/wav2vec2-base-960h",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-06-01T13:38:52Z |
---
license: apache-2.0
datasets:
- stapesai/ssi-speech-emotion-recognition
language:
- en
base_model:
- facebook/wav2vec2-base-960h
pipeline_tag: audio-classification
library_name: transformers
tags:
- emotion
- audio
- classification
- music
- facebook
---
# Speech-Emotion-Classification
> **Speech-Emotion-Classification** is a fine-tuned version of `facebook/wav2vec2-base-960h` for **multi-class audio classification**, specifically trained to detect **emotions** in speech. This model utilizes the `Wav2Vec2ForSequenceClassification` architecture to accurately classify speaker emotions from audio signals.
> \[!note]
> Wav2Vec2: Self-Supervised Learning for Speech Recognition
> [https://arxiv.org/pdf/2006.11477](https://arxiv.org/pdf/2006.11477)
```py
Classification Report:
precision recall f1-score test_support
Anger 0.8314 0.9346 0.8800 306
Calm 0.7949 0.8857 0.8378 35
Disgust 0.8261 0.8287 0.8274 321
Fear 0.8303 0.7377 0.7812 305
Happy 0.8929 0.7764 0.8306 322
Neutral 0.8423 0.9303 0.8841 287
Sad 0.7749 0.7825 0.7787 308
Surprised 0.9478 0.9478 0.9478 115
accuracy 0.8379 1999
macro avg 0.8426 0.8530 0.8460 1999
weighted avg 0.8392 0.8379 0.8367 1999
```
---
## Label Space: 8 Classes
```
Class 0: Anger
Class 1: Calm
Class 2: Disgust
Class 3: Fear
Class 4: Happy
Class 5: Neutral
Class 6: Sad
Class 7: Surprised
```
---
## Install Dependencies
```bash
pip install gradio transformers torch librosa hf_xet
```
---
## Inference Code
```python
import gradio as gr
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2FeatureExtractor
import torch
import librosa
# Load model and processor
model_name = "prithivMLmods/Speech-Emotion-Classification"
model = Wav2Vec2ForSequenceClassification.from_pretrained(model_name)
processor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
# Label mapping
id2label = {
"0": "Anger",
"1": "Calm",
"2": "Disgust",
"3": "Fear",
"4": "Happy",
"5": "Neutral",
"6": "Sad",
"7": "Surprised"
}
def classify_audio(audio_path):
# Load and resample audio to 16kHz
speech, sample_rate = librosa.load(audio_path, sr=16000)
# Process audio
inputs = processor(
speech,
sampling_rate=sample_rate,
return_tensors="pt",
padding=True
)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1).squeeze().tolist()
prediction = {
id2label[str(i)]: round(probs[i], 3) for i in range(len(probs))
}
return prediction
# Gradio Interface
iface = gr.Interface(
fn=classify_audio,
inputs=gr.Audio(type="filepath", label="Upload Audio (WAV, MP3, etc.)"),
outputs=gr.Label(num_top_classes=8, label="Emotion Classification"),
title="Speech Emotion Classification",
description="Upload an audio clip to classify the speaker's emotion from voice signals."
)
if __name__ == "__main__":
iface.launch()
```
---
## Original Label
```py
"id2label": {
"0": "ANG",
"1": "CAL",
"2": "DIS",
"3": "FEA",
"4": "HAP",
"5": "NEU",
"6": "SAD",
"7": "SUR"
},
```
---
## Intended Use
`Speech-Emotion-Classification` is designed for:
* **Speech Emotion Analytics** – Analyze speaker emotions in call centers, interviews, or therapeutic sessions.
* **Conversational AI Personalization** – Adjust voice assistant responses based on detected emotion.
* **Mental Health Monitoring** – Support emotion recognition in voice-based wellness or teletherapy apps.
* **Voice Dataset Curation** – Tag or filter speech datasets by emotion for research or model training.
* **Media Annotation** – Automatically annotate podcasts, audiobooks, or videos with speaker emotion metadata.
|
Seanwang1221/KaedeKaren_FLUX
|
Seanwang1221
| 2025-06-01T09:25:14Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-01T09:25:01Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
kaedekaren, In a captivating, high-contrast photograph, we find , a woman of
striking beauty with cascading chestnut brown hair, posed thoughtfully
against the backdrop of a vintage, ornate Parisian balcony at twilight. Her
gaze is intense yet soft, as if lost in reflection. The camera angles low
and slightly off-centered, capturing her face from the waist up. A single
string of pearls adorns her elegant neck, and her lips are painted a bold,
vibrant red, drawing focus to her full, expressive mouth. Her earrings,
sparkling diamonds that shimmer in the fading light, add an air of luxury.
The shadows cast by the setting sun lend an ethereal quality to the image,
while the intricate details of the balcony railing and the Eiffel Tower in
the distance serve as a reminder of the city's romantic charm. The overall
emotional tone is one of quiet contemplation and enchantment, capturing
AR13LK's timeless allure and Parisian elegance.
output:
url: images/Flux_image_00207_.png
- text: >-
kaedekaren, Nikon Z7 II and a NIKKOR Z 50mm f,1girl, 20yo,(wearing a red
cheongsam),(in london city),(RAW photo, best quality), (realistic,
photo-realistic), masterpiece, an extremely delicate and beautiful,
extremely detailed, 2k wallpaper, Amazing, finely detail, extremely detailed
CG unity 8k wallpaper, ultra-detailed, highres, soft light, beautiful
detailed girl, extremely detailed eyes and face, beautiful detailed nose,
beautiful detailed eyes,cinematic lighting,perfect anatomy,(slim body),hair
bun,(black hair),city lights at night,smiling
output:
url: images/Flux_image_00195_.png
- text: >-
kaedekaren, a woman wearing a (plaid pencil_dress), holding a purse,
floral print, depth of field, night cityscape, 1girl, long hair,
ulzzang-6500v1.1, (original: 1.2), (realistic: 1.3) , beautiful girl with
beautiful details, extremely detailed eyes and face, eyes with beautiful
details, absurd, incredibly absurd, huge file size, ultra detail, high
resolution, ultra detailed, best quality, masterpiece, illustration, ultra
detailed and beautiful, ultra detailed, CG, unity, 8k wallpaper, amazing,
fine Detail, masterpiece, top quality, official art, extremely detailed CG
unity 8k wallpaper, cinematic lighting, (perfect shiny skin:0.6), slim and
smooth lines, (floating), (small breasts:1), earrings , pearl necklace,,
output:
url: images/Flux_image_00188_.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: null
---
# Kaede Karen 楓カレン 枫花恋 FLUX
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/Seanwang1221/KaedeKaren_FLUX/tree/main) them in the Files & versions tab.
|
fmirogea/Taxi-v3
|
fmirogea
| 2025-05-30T15:28:08Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-05-30T15:28:05Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.44 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="fmirogea/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Zubias/zar
|
Zubias
| 2025-05-30T13:04:24Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-05-30T11:31:19Z |
---
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
---
|
Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q5_K_S-GGUF
|
Triangle104
| 2025-05-29T21:53:26Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"moe",
"mixture of experts",
"merge",
"llama-3",
"llama3",
"llama-cpp",
"gguf-my-repo",
"base_model:DavidAU/L3-MOE-4X8B-Grand-Horror-25B",
"base_model:quantized:DavidAU/L3-MOE-4X8B-Grand-Horror-25B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-29T21:32:54Z |
---
library_name: transformers
tags:
- mergekit
- moe
- mixture of experts
- merge
- llama-3
- llama3
- llama-cpp
- gguf-my-repo
base_model: DavidAU/L3-MOE-4X8B-Grand-Horror-25B
---
# Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q5_K_S-GGUF
This model was converted to GGUF format from [`DavidAU/L3-MOE-4X8B-Grand-Horror-25B`](https://huggingface.co/DavidAU/L3-MOE-4X8B-Grand-Horror-25B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/DavidAU/L3-MOE-4X8B-Grand-Horror-25B) for more details on the model.
---
It is a LLama3 model, max context of 8192 (or 32k+ with rope) using mixture of experts to combine Dark/Horror models models of 8B each into one massive powerhouse at 25B parameters (equal to 32B - 4 X 8 B).
This model's instruction following, and output generation for creative writing, prose, fiction and role play are exceptional.
It excels at description, dialog, imagery, metaphors, and prose - and shows great variations in sentence / paragraph size, length, and composition.
It is also not afraid, and will not pull its punches.
And it has a sense of humor too.
It can do horror just as easily as it can do romance.
Most notably dialog is very "un-ai" like, combined with prose (short, and terse at times).
(lots of different examples below, including 2, 3 and 4 experts and different genres)
And it is fast: 34 t/s (2 experts) on a low end 16GB card, Q3KS.
Double this speed for standard/mid-range video cards.
Model can be used also for all genres (examples below showing this).
This model has been designed to be relatively bullet proof and operates with all parameters, including temp settings from 0 to 5.
It is an extraordinary compressed model, with a very low perplexity level (lower than Meta Llama3 Instruct).
It is for any writing, fiction or roleplay activity.
It requires Llama3 template and/or "Command-R" template.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q5_K_S-GGUF --hf-file l3-moe-4x8b-grand-horror-25b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q5_K_S-GGUF --hf-file l3-moe-4x8b-grand-horror-25b-q5_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q5_K_S-GGUF --hf-file l3-moe-4x8b-grand-horror-25b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/L3-MOE-4X8B-Grand-Horror-25B-Q5_K_S-GGUF --hf-file l3-moe-4x8b-grand-horror-25b-q5_k_s.gguf -c 2048
```
|
tensoralchemistdev01/bb39
|
tensoralchemistdev01
| 2025-05-27T04:35:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-27T04:30:49Z |
---
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]
|
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Distilled Qwen 7B Models
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Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.