modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
fabikru/model_15M_medium_ds_masking_0.3_explicit_hs_predicted_hparamas | fabikru | 2025-05-23T11:12:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-22T19:59:56Z | ---
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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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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fabikru/model_15M_medium_ds_masking_0.2_explicit_hs_predicted_hparamas | fabikru | 2025-05-23T11:12:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-22T15:29:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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fabikru/model_15M_medium_ds_masking_0.7_explicit_hs_predicted_hparamas | fabikru | 2025-05-23T11:12:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-22T13:17:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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Jobz-Hunting-Sajal-Malik-16-Min-6-Second/Video.Clip.Jobz.Hunting.Sajal.Malik.Viral.Video.Leaks.Official | Jobz-Hunting-Sajal-Malik-16-Min-6-Second | 2025-05-23T11:11:24Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-23T11:10:49Z | <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>
Popular Pakistani TikToker Sajal Malik has found herself at the center of controversy after an alleged private video of her surfaced online. The MMS footage, which shows her in a compromising situation, quickly spread across social media. Some of her followers have criticized Malik and are demanding answers, while others have questioned whether the video is real, suggesting it might be fake.
|
EricZhang1412/MiniCPM-V-2.6 | EricZhang1412 | 2025-05-23T11:11:19Z | 0 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-23T09:32:02Z | ---
license: apache-2.0
---
|
ltgbao/DeepCoder-14B-Preview-merged-4bit-VulGen | ltgbao | 2025-05-23T11:11:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-23T11:08:09Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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.
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[More Information Needed]
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izzcw/crafting_sft_fail_new_mem | izzcw | 2025-05-23T11:10:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-23T01:24:33Z | ---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: crafting_sft_fail_new_mem
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. -->
# crafting_sft_fail_new_mem
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the identity and the crafting_sft_fail_new_mem datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3202
## 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: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- 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_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5344 | 0.0323 | 50 | 0.4756 |
| 0.528 | 0.0646 | 100 | 0.4853 |
| 0.5123 | 0.0969 | 150 | 0.4897 |
| 0.522 | 0.1291 | 200 | 0.4982 |
| 0.5227 | 0.1614 | 250 | 0.4605 |
| 0.5095 | 0.1937 | 300 | 0.4482 |
| 0.4733 | 0.2260 | 350 | 0.4402 |
| 0.47 | 0.2583 | 400 | 0.4243 |
| 0.4815 | 0.2906 | 450 | 0.4186 |
| 0.4526 | 0.3229 | 500 | 0.4181 |
| 0.4706 | 0.3552 | 550 | 0.4096 |
| 0.4523 | 0.3874 | 600 | 0.3973 |
| 0.4675 | 0.4197 | 650 | 0.4003 |
| 0.4389 | 0.4520 | 700 | 0.3848 |
| 0.4385 | 0.4843 | 750 | 0.3801 |
| 0.4312 | 0.5166 | 800 | 0.3762 |
| 0.4103 | 0.5489 | 850 | 0.3642 |
| 0.4106 | 0.5812 | 900 | 0.3626 |
| 0.3937 | 0.6134 | 950 | 0.3584 |
| 0.4053 | 0.6457 | 1000 | 0.3481 |
| 0.3764 | 0.6780 | 1050 | 0.3426 |
| 0.3712 | 0.7103 | 1100 | 0.3413 |
| 0.3721 | 0.7426 | 1150 | 0.3354 |
| 0.3561 | 0.7749 | 1200 | 0.3320 |
| 0.3788 | 0.8072 | 1250 | 0.3286 |
| 0.3673 | 0.8395 | 1300 | 0.3256 |
| 0.3561 | 0.8717 | 1350 | 0.3227 |
| 0.3648 | 0.9040 | 1400 | 0.3217 |
| 0.3789 | 0.9363 | 1450 | 0.3205 |
| 0.369 | 0.9686 | 1500 | 0.3203 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
vmpsergio/4386e470-772c-41bb-820e-c4d54e989907 | vmpsergio | 2025-05-23T11:09:13Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-23T10:29:09Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: 4386e470-772c-41bb-820e-c4d54e989907
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 4386e470-772c-41bb-820e-c4d54e989907
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
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="vmpsergio/4386e470-772c-41bb-820e-c4d54e989907", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-28/runs/x57558pr)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
phospho-app/PAphospho-gr00t-tictactoe-A1-orange-0510 | phospho-app | 2025-05-23T11:08:24Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-23T08:23:03Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [PAphospho/tictactoe-A1-orange](https://huggingface.co/datasets/PAphospho/tictactoe-A1-orange)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 5
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
gibsonsofiagri/derfhytyb | gibsonsofiagri | 2025-05-23T11:07:46Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
] | null | 2025-05-23T11:07:46Z | ---
license: artistic-2.0
---
|
jckim/checkpoints | jckim | 2025-05-23T11:05:49Z | 3 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"multilingual",
"base_model:openai/whisper-large-v3-turbo",
"base_model:adapter:openai/whisper-large-v3-turbo",
"license:mit",
"region:us"
] | null | 2025-04-29T02:05:21Z | ---
library_name: peft
language:
- multilingual
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Turbo Multilingual (ko, ja, zh, en)
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. -->
# Whisper Turbo Multilingual (ko, ja, zh, en)
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the custom_multilingual dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3849
- Wer: 25.9259
## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- 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
- lr_scheduler_warmup_steps: 5
- training_steps: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.6954 | 0.1754 | 10 | 0.4176 | 33.3333 |
| 0.2358 | 0.3509 | 20 | 0.3849 | 25.9259 |
### Framework versions
- PEFT 0.15.2.dev0
- Transformers 4.46.3
- Pytorch 2.3.1+cu121
- Datasets 3.0.0
- Tokenizers 0.20.3 |
kaolti/damien | kaolti | 2025-05-23T11:05:49Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-23T10:52:45Z | ---
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
---
# Damien
<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/kaolti/damien/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('kaolti/damien', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/kaolti/damien/discussions) to add images that show off what you’ve made with this LoRA.
|
phospho-app/PAphospho-gr00t-tictactoe-A1-orange-0506 | phospho-app | 2025-05-23T11:05:10Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-23T09:25:18Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [PAphospho/tictactoe-A1-orange](https://huggingface.co/datasets/PAphospho/tictactoe-A1-orange)
- **Wandb run URL**: None
- **Epochs**: 6
- **Batch size**: 5
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
kelu01/Chem_Fuser | kelu01 | 2025-05-23T11:03:45Z | 394 | 0 | null | [
"pytorch",
"gpt2",
"chemistry",
"medical",
"custom_code",
"en",
"license:mit",
"region:us"
] | null | 2025-05-10T11:20:54Z | ---
license: mit
language:
- en
tags:
- chemistry
- medical
---
# Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
Chem_Fuser is a prototype base model that explores the use of diffusion-inspired transformers for SMILES sequence completion. It represents an early proof-of-concept for unmasking partial chemical structures using learned context and token diffusion.
This is the first in a planned series of models aimed at general-purpose molecular generation, inpainting, and drug design. Work is actively ongoing to expand its capabilities, integrate richer chemical priors, and train larger and more expressive versions for broader applicability across medicinal chemistry, cheminformatics, and materials science.
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [@kelu01](https://huggingface.co/kelu01)
- **Shared by:** [@kelu01](https://huggingface.co/kelu01) ([email protected])]
- **Model type:** Diffusion-based Transformer for SMILES token unmasking
- **Language(s):** English (for logs/prints), SMILES strings (chemical tokens)
- **License:** MIT
- **Finetuned from:** Custom architecture (not a pretrained NLP base model)
- **Framework:** PyTorch
## Usage
You can use this model directly via the Hugging Face `pipeline`:
```python
from transformers import AutoTokenizer, pipeline
# Load tokenizer and pipeline
tokenizer = AutoTokenizer.from_pretrained("kelu01/Chem_Fuser")
pipe = pipeline("smiles-diffusion", model="kelu01/Chem_Fuser", voc=tokenizer, trust_remote_code=True)
# Provide a partially masked SMILES string
masked_smiles = "CC[MASK]N[MASK]1[MASK][MASK]C[MASK][C@H][MASK][MASK][MASK][MASK][MASK][MASK])c[MASK][MASK]c[MASK]c[MASK][MASK]C([MASK][MASK]([MASK])[MASK])c[MASK]cc[MASK]c2[MASK]cc[MASK]c[MASK]2" # Example
completed = pipe(masked_smiles)
print(completed)
```
## Out-of-Scope Use
Chem_Fuser is not intended for the following:
- Natural language tasks such as text generation, sentiment analysis, or question answering.
- Direct clinical decision-making or drug safety evaluation. The model is research-grade and should not be used in medical settings without further validation.
- Arbitrary string generation — input must be a partially masked SMILES string with valid chemical syntax.
- Structure validation or property prediction — Chem_Fuser does not verify chemical correctness or predict bioactivity; external tools (e.g., RDKit) must be used for validation.
## Bias, Risks, and Limitations
While Chem_Fuser is powerful for generating chemically plausible molecules, it comes with the following limitations:
- Chemical Validity Not Guaranteed: The model can produce syntactically correct but chemically invalid molecules. Use cheminformatics tools like RDKit to filter outputs.
- Biological Activity Unknown: The generated molecules are not guaranteed to be biologically active or safe. Experimental validation is essential.
- Bias in Training Data: The model is trained on publicly available databases (e.g., ZINC, ChEMBL), which are biased toward drug-like, small organic molecules. It may perform poorly on polymers, organometallics, or exotic chemical spaces.
- Token Limitation: SMILES strings that are too long may be truncated or produce poor results.
- Data Leakage Risk: Since pretraining involves known compounds, generated molecules might occasionally resemble known entities from training data.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoModel
model = AutoModel.from_pretrained("kelu01/Chem_Fuser",trust_remote_code=True)
```
## Disclaimer
This model is provided as-is for research and educational purposes only. While care has been taken in its development, the author makes no warranties or guarantees regarding its performance, correctness, or fitness for any particular purpose.
The author shall not be held liable for any damages, losses, or legal issues that may arise from the use or misuse of this model, including but not limited to applications in drug development, clinical decision-making, or the generation of harmful substances.
Users are solely responsible for validating and verifying the outputs using appropriate scientific and safety protocols. |
Anambra-Woman/Anambra.Woman.viral.video.leaked | Anambra-Woman | 2025-05-23T11:02:47Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-23T11:00:26Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=Anambra-Woman)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=Anambra-Woman)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=Anambra-Woman) |
mariagrandury/gemma-3-12b-it-unsloth-bnb-4bit-task1-3-lora-adapter | mariagrandury | 2025-05-23T11:02:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T11:02:25Z | ---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mariagrandury
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it-unsloth-bnb-4bit
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)
|
erfanroudaki/Qwen2-0.5B-GRPO-test | erfanroudaki | 2025-05-23T11:01:17Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:AI-MO/NuminaMath-TIR",
"arxiv:2402.03300",
"endpoints_compatible",
"region:us"
] | null | 2025-05-15T19:32:53Z | ---
datasets: AI-MO/NuminaMath-TIR
library_name: transformers
model_name: Qwen2-0.5B-GRPO-test
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-0.5B-GRPO-test
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) 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="erfanroudaki/Qwen2-0.5B-GRPO-test", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Isi99999/LatentSync | Isi99999 | 2025-05-23T11:00:31Z | 0 | 0 | diffusers | [
"diffusers",
"video-to-video",
"arxiv:2412.09262",
"license:openrail",
"region:us"
] | null | 2025-01-27T17:14:48Z | ---
license: openrail
library_name: diffusers
tags:
- video-to-video
pipeline_tag: video-to-video
---
# The checkpoints of LatentSync
Please note that this repo is a copy of https://huggingface.co/ByteDance/LatentSync as at 1912hrs, 27/01/2025. This repo not only stores the pretrained U-Net and SyncNet checkpoints of LatentSync, but also stores the whisper checkpoints, auxiliary checkpoints for detecting face, calculating syncnet confidence score and so on. They have covered all you need for both inference and training of LatentSync
Paper: https://arxiv.org/abs/2412.09262
Code: https://github.com/bytedance/LatentSync |
infogep/ac6fca8a-e370-4997-9064-793e412f96dc | infogep | 2025-05-23T10:57:31Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-23T10:26:20Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: ac6fca8a-e370-4997-9064-793e412f96dc
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for ac6fca8a-e370-4997-9064-793e412f96dc
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
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="infogep/ac6fca8a-e370-4997-9064-793e412f96dc", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/xfk3nr5h)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
aipib/smolvlm-it-ft-jp | aipib | 2025-05-23T10:57:27Z | 0 | 0 | null | [
"safetensors",
"idefics3",
"license:apache-2.0",
"region:us"
] | null | 2025-05-23T10:55:49Z | ---
license: apache-2.0
---
|
arnaultsta/MNLP_M2_document_encoder | arnaultsta | 2025-05-23T10:54:11Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"en",
"arxiv:2401.03462",
"arxiv:2312.15503",
"arxiv:2311.13534",
"arxiv:2310.07554",
"arxiv:2309.07597",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-05-23T10:00:40Z | ---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
model-index:
- name: bge-small-en-v1.5
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 73.79104477611939
- type: ap
value: 37.21923821573361
- type: f1
value: 68.0914945617093
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 92.75377499999999
- type: ap
value: 89.46766124546022
- type: f1
value: 92.73884001331487
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.986
- type: f1
value: 46.55936786727896
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.846000000000004
- type: map_at_10
value: 51.388
- type: map_at_100
value: 52.132999999999996
- type: map_at_1000
value: 52.141000000000005
- type: map_at_3
value: 47.037
- type: map_at_5
value: 49.579
- type: mrr_at_1
value: 36.558
- type: mrr_at_10
value: 51.658
- type: mrr_at_100
value: 52.402
- type: mrr_at_1000
value: 52.410000000000004
- type: mrr_at_3
value: 47.345
- type: mrr_at_5
value: 49.797999999999995
- type: ndcg_at_1
value: 35.846000000000004
- type: ndcg_at_10
value: 59.550000000000004
- type: ndcg_at_100
value: 62.596
- type: ndcg_at_1000
value: 62.759
- type: ndcg_at_3
value: 50.666999999999994
- type: ndcg_at_5
value: 55.228
- type: precision_at_1
value: 35.846000000000004
- type: precision_at_10
value: 8.542
- type: precision_at_100
value: 0.984
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 20.389
- type: precision_at_5
value: 14.438
- type: recall_at_1
value: 35.846000000000004
- type: recall_at_10
value: 85.42
- type: recall_at_100
value: 98.43499999999999
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 61.166
- type: recall_at_5
value: 72.191
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 47.402770198163594
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 40.01545436974177
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 62.586465273207196
- type: mrr
value: 74.42169019038825
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 85.1891186537969
- type: cos_sim_spearman
value: 83.75492046087288
- type: euclidean_pearson
value: 84.11766204805357
- type: euclidean_spearman
value: 84.01456493126516
- type: manhattan_pearson
value: 84.2132950502772
- type: manhattan_spearman
value: 83.89227298813377
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 85.74025974025975
- type: f1
value: 85.71493566466381
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 38.467181385006434
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 34.719496037339056
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.587000000000003
- type: map_at_10
value: 41.114
- type: map_at_100
value: 42.532
- type: map_at_1000
value: 42.661
- type: map_at_3
value: 37.483
- type: map_at_5
value: 39.652
- type: mrr_at_1
value: 36.338
- type: mrr_at_10
value: 46.763
- type: mrr_at_100
value: 47.393
- type: mrr_at_1000
value: 47.445
- type: mrr_at_3
value: 43.538
- type: mrr_at_5
value: 45.556000000000004
- type: ndcg_at_1
value: 36.338
- type: ndcg_at_10
value: 47.658
- type: ndcg_at_100
value: 52.824000000000005
- type: ndcg_at_1000
value: 54.913999999999994
- type: ndcg_at_3
value: 41.989
- type: ndcg_at_5
value: 44.944
- type: precision_at_1
value: 36.338
- type: precision_at_10
value: 9.156
- type: precision_at_100
value: 1.4789999999999999
- type: precision_at_1000
value: 0.196
- type: precision_at_3
value: 20.076
- type: precision_at_5
value: 14.85
- type: recall_at_1
value: 29.587000000000003
- type: recall_at_10
value: 60.746
- type: recall_at_100
value: 82.157
- type: recall_at_1000
value: 95.645
- type: recall_at_3
value: 44.821
- type: recall_at_5
value: 52.819
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.239
- type: map_at_10
value: 39.989000000000004
- type: map_at_100
value: 41.196
- type: map_at_1000
value: 41.325
- type: map_at_3
value: 37.261
- type: map_at_5
value: 38.833
- type: mrr_at_1
value: 37.516
- type: mrr_at_10
value: 46.177
- type: mrr_at_100
value: 46.806
- type: mrr_at_1000
value: 46.849000000000004
- type: mrr_at_3
value: 44.002
- type: mrr_at_5
value: 45.34
- type: ndcg_at_1
value: 37.516
- type: ndcg_at_10
value: 45.586
- type: ndcg_at_100
value: 49.897000000000006
- type: ndcg_at_1000
value: 51.955
- type: ndcg_at_3
value: 41.684
- type: ndcg_at_5
value: 43.617
- type: precision_at_1
value: 37.516
- type: precision_at_10
value: 8.522
- type: precision_at_100
value: 1.374
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 20.105999999999998
- type: precision_at_5
value: 14.152999999999999
- type: recall_at_1
value: 30.239
- type: recall_at_10
value: 55.03
- type: recall_at_100
value: 73.375
- type: recall_at_1000
value: 86.29599999999999
- type: recall_at_3
value: 43.269000000000005
- type: recall_at_5
value: 48.878
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 38.338
- type: map_at_10
value: 50.468999999999994
- type: map_at_100
value: 51.553000000000004
- type: map_at_1000
value: 51.608
- type: map_at_3
value: 47.107
- type: map_at_5
value: 49.101
- type: mrr_at_1
value: 44.201
- type: mrr_at_10
value: 54.057
- type: mrr_at_100
value: 54.764
- type: mrr_at_1000
value: 54.791000000000004
- type: mrr_at_3
value: 51.56699999999999
- type: mrr_at_5
value: 53.05
- type: ndcg_at_1
value: 44.201
- type: ndcg_at_10
value: 56.379000000000005
- type: ndcg_at_100
value: 60.645
- type: ndcg_at_1000
value: 61.73499999999999
- type: ndcg_at_3
value: 50.726000000000006
- type: ndcg_at_5
value: 53.58500000000001
- type: precision_at_1
value: 44.201
- type: precision_at_10
value: 9.141
- type: precision_at_100
value: 1.216
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 22.654
- type: precision_at_5
value: 15.723999999999998
- type: recall_at_1
value: 38.338
- type: recall_at_10
value: 70.30499999999999
- type: recall_at_100
value: 88.77199999999999
- type: recall_at_1000
value: 96.49799999999999
- type: recall_at_3
value: 55.218
- type: recall_at_5
value: 62.104000000000006
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.682
- type: map_at_10
value: 33.498
- type: map_at_100
value: 34.461000000000006
- type: map_at_1000
value: 34.544000000000004
- type: map_at_3
value: 30.503999999999998
- type: map_at_5
value: 32.216
- type: mrr_at_1
value: 27.683999999999997
- type: mrr_at_10
value: 35.467999999999996
- type: mrr_at_100
value: 36.32
- type: mrr_at_1000
value: 36.386
- type: mrr_at_3
value: 32.618
- type: mrr_at_5
value: 34.262
- type: ndcg_at_1
value: 27.683999999999997
- type: ndcg_at_10
value: 38.378
- type: ndcg_at_100
value: 43.288
- type: ndcg_at_1000
value: 45.413
- type: ndcg_at_3
value: 32.586
- type: ndcg_at_5
value: 35.499
- type: precision_at_1
value: 27.683999999999997
- type: precision_at_10
value: 5.864
- type: precision_at_100
value: 0.882
- type: precision_at_1000
value: 0.11
- type: precision_at_3
value: 13.446
- type: precision_at_5
value: 9.718
- type: recall_at_1
value: 25.682
- type: recall_at_10
value: 51.712
- type: recall_at_100
value: 74.446
- type: recall_at_1000
value: 90.472
- type: recall_at_3
value: 36.236000000000004
- type: recall_at_5
value: 43.234
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.073999999999998
- type: map_at_10
value: 24.352999999999998
- type: map_at_100
value: 25.438
- type: map_at_1000
value: 25.545
- type: map_at_3
value: 21.614
- type: map_at_5
value: 23.104
- type: mrr_at_1
value: 19.776
- type: mrr_at_10
value: 28.837000000000003
- type: mrr_at_100
value: 29.755
- type: mrr_at_1000
value: 29.817
- type: mrr_at_3
value: 26.201999999999998
- type: mrr_at_5
value: 27.714
- type: ndcg_at_1
value: 19.776
- type: ndcg_at_10
value: 29.701
- type: ndcg_at_100
value: 35.307
- type: ndcg_at_1000
value: 37.942
- type: ndcg_at_3
value: 24.764
- type: ndcg_at_5
value: 27.025
- type: precision_at_1
value: 19.776
- type: precision_at_10
value: 5.659
- type: precision_at_100
value: 0.971
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 12.065
- type: precision_at_5
value: 8.905000000000001
- type: recall_at_1
value: 16.073999999999998
- type: recall_at_10
value: 41.647
- type: recall_at_100
value: 66.884
- type: recall_at_1000
value: 85.91499999999999
- type: recall_at_3
value: 27.916
- type: recall_at_5
value: 33.729
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.444999999999997
- type: map_at_10
value: 38.218999999999994
- type: map_at_100
value: 39.595
- type: map_at_1000
value: 39.709
- type: map_at_3
value: 35.586
- type: map_at_5
value: 36.895
- type: mrr_at_1
value: 34.841
- type: mrr_at_10
value: 44.106
- type: mrr_at_100
value: 44.98
- type: mrr_at_1000
value: 45.03
- type: mrr_at_3
value: 41.979
- type: mrr_at_5
value: 43.047999999999995
- type: ndcg_at_1
value: 34.841
- type: ndcg_at_10
value: 43.922
- type: ndcg_at_100
value: 49.504999999999995
- type: ndcg_at_1000
value: 51.675000000000004
- type: ndcg_at_3
value: 39.858
- type: ndcg_at_5
value: 41.408
- type: precision_at_1
value: 34.841
- type: precision_at_10
value: 7.872999999999999
- type: precision_at_100
value: 1.2449999999999999
- type: precision_at_1000
value: 0.161
- type: precision_at_3
value: 18.993
- type: precision_at_5
value: 13.032
- type: recall_at_1
value: 28.444999999999997
- type: recall_at_10
value: 54.984
- type: recall_at_100
value: 78.342
- type: recall_at_1000
value: 92.77
- type: recall_at_3
value: 42.842999999999996
- type: recall_at_5
value: 47.247
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.072
- type: map_at_10
value: 32.354
- type: map_at_100
value: 33.800000000000004
- type: map_at_1000
value: 33.908
- type: map_at_3
value: 29.232000000000003
- type: map_at_5
value: 31.049
- type: mrr_at_1
value: 29.110000000000003
- type: mrr_at_10
value: 38.03
- type: mrr_at_100
value: 39.032
- type: mrr_at_1000
value: 39.086999999999996
- type: mrr_at_3
value: 35.407
- type: mrr_at_5
value: 36.76
- type: ndcg_at_1
value: 29.110000000000003
- type: ndcg_at_10
value: 38.231
- type: ndcg_at_100
value: 44.425
- type: ndcg_at_1000
value: 46.771
- type: ndcg_at_3
value: 33.095
- type: ndcg_at_5
value: 35.459
- type: precision_at_1
value: 29.110000000000003
- type: precision_at_10
value: 7.215000000000001
- type: precision_at_100
value: 1.2109999999999999
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 16.058
- type: precision_at_5
value: 11.644
- type: recall_at_1
value: 23.072
- type: recall_at_10
value: 50.285999999999994
- type: recall_at_100
value: 76.596
- type: recall_at_1000
value: 92.861
- type: recall_at_3
value: 35.702
- type: recall_at_5
value: 42.152
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 24.937916666666666
- type: map_at_10
value: 33.755250000000004
- type: map_at_100
value: 34.955999999999996
- type: map_at_1000
value: 35.070499999999996
- type: map_at_3
value: 30.98708333333333
- type: map_at_5
value: 32.51491666666666
- type: mrr_at_1
value: 29.48708333333333
- type: mrr_at_10
value: 37.92183333333334
- type: mrr_at_100
value: 38.76583333333333
- type: mrr_at_1000
value: 38.82466666666667
- type: mrr_at_3
value: 35.45125
- type: mrr_at_5
value: 36.827000000000005
- type: ndcg_at_1
value: 29.48708333333333
- type: ndcg_at_10
value: 39.05225
- type: ndcg_at_100
value: 44.25983333333334
- type: ndcg_at_1000
value: 46.568333333333335
- type: ndcg_at_3
value: 34.271583333333325
- type: ndcg_at_5
value: 36.483916666666666
- type: precision_at_1
value: 29.48708333333333
- type: precision_at_10
value: 6.865749999999999
- type: precision_at_100
value: 1.1195833333333332
- type: precision_at_1000
value: 0.15058333333333335
- type: precision_at_3
value: 15.742083333333333
- type: precision_at_5
value: 11.221916666666667
- type: recall_at_1
value: 24.937916666666666
- type: recall_at_10
value: 50.650416666666665
- type: recall_at_100
value: 73.55383333333334
- type: recall_at_1000
value: 89.61691666666667
- type: recall_at_3
value: 37.27808333333334
- type: recall_at_5
value: 42.99475
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.947
- type: map_at_10
value: 30.575000000000003
- type: map_at_100
value: 31.465
- type: map_at_1000
value: 31.558000000000003
- type: map_at_3
value: 28.814
- type: map_at_5
value: 29.738999999999997
- type: mrr_at_1
value: 26.994
- type: mrr_at_10
value: 33.415
- type: mrr_at_100
value: 34.18
- type: mrr_at_1000
value: 34.245
- type: mrr_at_3
value: 31.621
- type: mrr_at_5
value: 32.549
- type: ndcg_at_1
value: 26.994
- type: ndcg_at_10
value: 34.482
- type: ndcg_at_100
value: 38.915
- type: ndcg_at_1000
value: 41.355
- type: ndcg_at_3
value: 31.139
- type: ndcg_at_5
value: 32.589
- type: precision_at_1
value: 26.994
- type: precision_at_10
value: 5.322
- type: precision_at_100
value: 0.8160000000000001
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 13.344000000000001
- type: precision_at_5
value: 8.988
- type: recall_at_1
value: 23.947
- type: recall_at_10
value: 43.647999999999996
- type: recall_at_100
value: 63.851
- type: recall_at_1000
value: 82.0
- type: recall_at_3
value: 34.288000000000004
- type: recall_at_5
value: 38.117000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.197
- type: map_at_10
value: 22.968
- type: map_at_100
value: 24.095
- type: map_at_1000
value: 24.217
- type: map_at_3
value: 20.771
- type: map_at_5
value: 21.995
- type: mrr_at_1
value: 19.511
- type: mrr_at_10
value: 26.55
- type: mrr_at_100
value: 27.500999999999998
- type: mrr_at_1000
value: 27.578999999999997
- type: mrr_at_3
value: 24.421
- type: mrr_at_5
value: 25.604
- type: ndcg_at_1
value: 19.511
- type: ndcg_at_10
value: 27.386
- type: ndcg_at_100
value: 32.828
- type: ndcg_at_1000
value: 35.739
- type: ndcg_at_3
value: 23.405
- type: ndcg_at_5
value: 25.255
- type: precision_at_1
value: 19.511
- type: precision_at_10
value: 5.017
- type: precision_at_100
value: 0.91
- type: precision_at_1000
value: 0.133
- type: precision_at_3
value: 11.023
- type: precision_at_5
value: 8.025
- type: recall_at_1
value: 16.197
- type: recall_at_10
value: 37.09
- type: recall_at_100
value: 61.778
- type: recall_at_1000
value: 82.56599999999999
- type: recall_at_3
value: 26.034000000000002
- type: recall_at_5
value: 30.762
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 25.41
- type: map_at_10
value: 33.655
- type: map_at_100
value: 34.892
- type: map_at_1000
value: 34.995
- type: map_at_3
value: 30.94
- type: map_at_5
value: 32.303
- type: mrr_at_1
value: 29.477999999999998
- type: mrr_at_10
value: 37.443
- type: mrr_at_100
value: 38.383
- type: mrr_at_1000
value: 38.440000000000005
- type: mrr_at_3
value: 34.949999999999996
- type: mrr_at_5
value: 36.228
- type: ndcg_at_1
value: 29.477999999999998
- type: ndcg_at_10
value: 38.769
- type: ndcg_at_100
value: 44.245000000000005
- type: ndcg_at_1000
value: 46.593
- type: ndcg_at_3
value: 33.623
- type: ndcg_at_5
value: 35.766
- type: precision_at_1
value: 29.477999999999998
- type: precision_at_10
value: 6.455
- type: precision_at_100
value: 1.032
- type: precision_at_1000
value: 0.135
- type: precision_at_3
value: 14.893999999999998
- type: precision_at_5
value: 10.485
- type: recall_at_1
value: 25.41
- type: recall_at_10
value: 50.669
- type: recall_at_100
value: 74.084
- type: recall_at_1000
value: 90.435
- type: recall_at_3
value: 36.679
- type: recall_at_5
value: 41.94
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.339
- type: map_at_10
value: 31.852000000000004
- type: map_at_100
value: 33.411
- type: map_at_1000
value: 33.62
- type: map_at_3
value: 28.929
- type: map_at_5
value: 30.542
- type: mrr_at_1
value: 28.063
- type: mrr_at_10
value: 36.301
- type: mrr_at_100
value: 37.288
- type: mrr_at_1000
value: 37.349
- type: mrr_at_3
value: 33.663
- type: mrr_at_5
value: 35.165
- type: ndcg_at_1
value: 28.063
- type: ndcg_at_10
value: 37.462
- type: ndcg_at_100
value: 43.620999999999995
- type: ndcg_at_1000
value: 46.211
- type: ndcg_at_3
value: 32.68
- type: ndcg_at_5
value: 34.981
- type: precision_at_1
value: 28.063
- type: precision_at_10
value: 7.1739999999999995
- type: precision_at_100
value: 1.486
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 15.217
- type: precision_at_5
value: 11.265
- type: recall_at_1
value: 23.339
- type: recall_at_10
value: 48.376999999999995
- type: recall_at_100
value: 76.053
- type: recall_at_1000
value: 92.455
- type: recall_at_3
value: 34.735
- type: recall_at_5
value: 40.71
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 18.925
- type: map_at_10
value: 26.017000000000003
- type: map_at_100
value: 27.034000000000002
- type: map_at_1000
value: 27.156000000000002
- type: map_at_3
value: 23.604
- type: map_at_5
value: 24.75
- type: mrr_at_1
value: 20.333000000000002
- type: mrr_at_10
value: 27.915
- type: mrr_at_100
value: 28.788000000000004
- type: mrr_at_1000
value: 28.877999999999997
- type: mrr_at_3
value: 25.446999999999996
- type: mrr_at_5
value: 26.648
- type: ndcg_at_1
value: 20.333000000000002
- type: ndcg_at_10
value: 30.673000000000002
- type: ndcg_at_100
value: 35.618
- type: ndcg_at_1000
value: 38.517
- type: ndcg_at_3
value: 25.71
- type: ndcg_at_5
value: 27.679
- type: precision_at_1
value: 20.333000000000002
- type: precision_at_10
value: 4.9910000000000005
- type: precision_at_100
value: 0.8130000000000001
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 11.029
- type: precision_at_5
value: 7.8740000000000006
- type: recall_at_1
value: 18.925
- type: recall_at_10
value: 43.311
- type: recall_at_100
value: 66.308
- type: recall_at_1000
value: 87.49
- type: recall_at_3
value: 29.596
- type: recall_at_5
value: 34.245
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 13.714
- type: map_at_10
value: 23.194
- type: map_at_100
value: 24.976000000000003
- type: map_at_1000
value: 25.166
- type: map_at_3
value: 19.709
- type: map_at_5
value: 21.523999999999997
- type: mrr_at_1
value: 30.619000000000003
- type: mrr_at_10
value: 42.563
- type: mrr_at_100
value: 43.386
- type: mrr_at_1000
value: 43.423
- type: mrr_at_3
value: 39.555
- type: mrr_at_5
value: 41.268
- type: ndcg_at_1
value: 30.619000000000003
- type: ndcg_at_10
value: 31.836
- type: ndcg_at_100
value: 38.652
- type: ndcg_at_1000
value: 42.088
- type: ndcg_at_3
value: 26.733
- type: ndcg_at_5
value: 28.435
- type: precision_at_1
value: 30.619000000000003
- type: precision_at_10
value: 9.751999999999999
- type: precision_at_100
value: 1.71
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 19.935
- type: precision_at_5
value: 14.984
- type: recall_at_1
value: 13.714
- type: recall_at_10
value: 37.26
- type: recall_at_100
value: 60.546
- type: recall_at_1000
value: 79.899
- type: recall_at_3
value: 24.325
- type: recall_at_5
value: 29.725
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.462
- type: map_at_10
value: 18.637
- type: map_at_100
value: 26.131999999999998
- type: map_at_1000
value: 27.607
- type: map_at_3
value: 13.333
- type: map_at_5
value: 15.654000000000002
- type: mrr_at_1
value: 66.25
- type: mrr_at_10
value: 74.32600000000001
- type: mrr_at_100
value: 74.60900000000001
- type: mrr_at_1000
value: 74.62
- type: mrr_at_3
value: 72.667
- type: mrr_at_5
value: 73.817
- type: ndcg_at_1
value: 53.87499999999999
- type: ndcg_at_10
value: 40.028999999999996
- type: ndcg_at_100
value: 44.199
- type: ndcg_at_1000
value: 51.629999999999995
- type: ndcg_at_3
value: 44.113
- type: ndcg_at_5
value: 41.731
- type: precision_at_1
value: 66.25
- type: precision_at_10
value: 31.900000000000002
- type: precision_at_100
value: 10.043000000000001
- type: precision_at_1000
value: 1.926
- type: precision_at_3
value: 47.417
- type: precision_at_5
value: 40.65
- type: recall_at_1
value: 8.462
- type: recall_at_10
value: 24.293
- type: recall_at_100
value: 50.146
- type: recall_at_1000
value: 74.034
- type: recall_at_3
value: 14.967
- type: recall_at_5
value: 18.682000000000002
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 47.84499999999999
- type: f1
value: 42.48106691979349
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 74.034
- type: map_at_10
value: 82.76
- type: map_at_100
value: 82.968
- type: map_at_1000
value: 82.98299999999999
- type: map_at_3
value: 81.768
- type: map_at_5
value: 82.418
- type: mrr_at_1
value: 80.048
- type: mrr_at_10
value: 87.64999999999999
- type: mrr_at_100
value: 87.712
- type: mrr_at_1000
value: 87.713
- type: mrr_at_3
value: 87.01100000000001
- type: mrr_at_5
value: 87.466
- type: ndcg_at_1
value: 80.048
- type: ndcg_at_10
value: 86.643
- type: ndcg_at_100
value: 87.361
- type: ndcg_at_1000
value: 87.606
- type: ndcg_at_3
value: 85.137
- type: ndcg_at_5
value: 86.016
- type: precision_at_1
value: 80.048
- type: precision_at_10
value: 10.372
- type: precision_at_100
value: 1.093
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 32.638
- type: precision_at_5
value: 20.177
- type: recall_at_1
value: 74.034
- type: recall_at_10
value: 93.769
- type: recall_at_100
value: 96.569
- type: recall_at_1000
value: 98.039
- type: recall_at_3
value: 89.581
- type: recall_at_5
value: 91.906
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.5
- type: map_at_10
value: 32.857
- type: map_at_100
value: 34.589
- type: map_at_1000
value: 34.778
- type: map_at_3
value: 29.160999999999998
- type: map_at_5
value: 31.033
- type: mrr_at_1
value: 40.123
- type: mrr_at_10
value: 48.776
- type: mrr_at_100
value: 49.495
- type: mrr_at_1000
value: 49.539
- type: mrr_at_3
value: 46.605000000000004
- type: mrr_at_5
value: 47.654
- type: ndcg_at_1
value: 40.123
- type: ndcg_at_10
value: 40.343
- type: ndcg_at_100
value: 46.56
- type: ndcg_at_1000
value: 49.777
- type: ndcg_at_3
value: 37.322
- type: ndcg_at_5
value: 37.791000000000004
- type: precision_at_1
value: 40.123
- type: precision_at_10
value: 11.08
- type: precision_at_100
value: 1.752
- type: precision_at_1000
value: 0.232
- type: precision_at_3
value: 24.897
- type: precision_at_5
value: 17.809
- type: recall_at_1
value: 20.5
- type: recall_at_10
value: 46.388
- type: recall_at_100
value: 69.552
- type: recall_at_1000
value: 89.011
- type: recall_at_3
value: 33.617999999999995
- type: recall_at_5
value: 38.211
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.135999999999996
- type: map_at_10
value: 61.673
- type: map_at_100
value: 62.562
- type: map_at_1000
value: 62.62
- type: map_at_3
value: 58.467999999999996
- type: map_at_5
value: 60.463
- type: mrr_at_1
value: 78.271
- type: mrr_at_10
value: 84.119
- type: mrr_at_100
value: 84.29299999999999
- type: mrr_at_1000
value: 84.299
- type: mrr_at_3
value: 83.18900000000001
- type: mrr_at_5
value: 83.786
- type: ndcg_at_1
value: 78.271
- type: ndcg_at_10
value: 69.935
- type: ndcg_at_100
value: 73.01299999999999
- type: ndcg_at_1000
value: 74.126
- type: ndcg_at_3
value: 65.388
- type: ndcg_at_5
value: 67.906
- type: precision_at_1
value: 78.271
- type: precision_at_10
value: 14.562
- type: precision_at_100
value: 1.6969999999999998
- type: precision_at_1000
value: 0.184
- type: precision_at_3
value: 41.841
- type: precision_at_5
value: 27.087
- type: recall_at_1
value: 39.135999999999996
- type: recall_at_10
value: 72.809
- type: recall_at_100
value: 84.86200000000001
- type: recall_at_1000
value: 92.208
- type: recall_at_3
value: 62.76199999999999
- type: recall_at_5
value: 67.718
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 90.60600000000001
- type: ap
value: 86.6579587804335
- type: f1
value: 90.5938853929307
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 21.852
- type: map_at_10
value: 33.982
- type: map_at_100
value: 35.116
- type: map_at_1000
value: 35.167
- type: map_at_3
value: 30.134
- type: map_at_5
value: 32.340999999999994
- type: mrr_at_1
value: 22.479
- type: mrr_at_10
value: 34.594
- type: mrr_at_100
value: 35.672
- type: mrr_at_1000
value: 35.716
- type: mrr_at_3
value: 30.84
- type: mrr_at_5
value: 32.998
- type: ndcg_at_1
value: 22.493
- type: ndcg_at_10
value: 40.833000000000006
- type: ndcg_at_100
value: 46.357
- type: ndcg_at_1000
value: 47.637
- type: ndcg_at_3
value: 32.995999999999995
- type: ndcg_at_5
value: 36.919000000000004
- type: precision_at_1
value: 22.493
- type: precision_at_10
value: 6.465999999999999
- type: precision_at_100
value: 0.9249999999999999
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 14.030999999999999
- type: precision_at_5
value: 10.413
- type: recall_at_1
value: 21.852
- type: recall_at_10
value: 61.934999999999995
- type: recall_at_100
value: 87.611
- type: recall_at_1000
value: 97.441
- type: recall_at_3
value: 40.583999999999996
- type: recall_at_5
value: 49.992999999999995
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.36069311445507
- type: f1
value: 93.16456330371453
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 74.74692202462381
- type: f1
value: 58.17903579421599
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.80833893745796
- type: f1
value: 72.70786592684664
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.69872225958305
- type: f1
value: 78.61626934504731
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 33.058658628717694
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 30.85561739360599
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.290259910144385
- type: mrr
value: 32.44223046102856
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.288
- type: map_at_10
value: 12.267999999999999
- type: map_at_100
value: 15.557000000000002
- type: map_at_1000
value: 16.98
- type: map_at_3
value: 8.866
- type: map_at_5
value: 10.418
- type: mrr_at_1
value: 43.653
- type: mrr_at_10
value: 52.681
- type: mrr_at_100
value: 53.315999999999995
- type: mrr_at_1000
value: 53.357
- type: mrr_at_3
value: 51.393
- type: mrr_at_5
value: 51.903999999999996
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.305
- type: ndcg_at_100
value: 30.825999999999997
- type: ndcg_at_1000
value: 39.393
- type: ndcg_at_3
value: 39.931
- type: ndcg_at_5
value: 37.519999999999996
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.728
- type: precision_at_100
value: 7.932
- type: precision_at_1000
value: 2.07
- type: precision_at_3
value: 38.184000000000005
- type: precision_at_5
value: 32.879000000000005
- type: recall_at_1
value: 5.288
- type: recall_at_10
value: 16.195
- type: recall_at_100
value: 31.135
- type: recall_at_1000
value: 61.531000000000006
- type: recall_at_3
value: 10.313
- type: recall_at_5
value: 12.754999999999999
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.216
- type: map_at_10
value: 42.588
- type: map_at_100
value: 43.702999999999996
- type: map_at_1000
value: 43.739
- type: map_at_3
value: 38.177
- type: map_at_5
value: 40.754000000000005
- type: mrr_at_1
value: 31.866
- type: mrr_at_10
value: 45.189
- type: mrr_at_100
value: 46.056000000000004
- type: mrr_at_1000
value: 46.081
- type: mrr_at_3
value: 41.526999999999994
- type: mrr_at_5
value: 43.704
- type: ndcg_at_1
value: 31.837
- type: ndcg_at_10
value: 50.178
- type: ndcg_at_100
value: 54.98800000000001
- type: ndcg_at_1000
value: 55.812
- type: ndcg_at_3
value: 41.853
- type: ndcg_at_5
value: 46.153
- type: precision_at_1
value: 31.837
- type: precision_at_10
value: 8.43
- type: precision_at_100
value: 1.1119999999999999
- type: precision_at_1000
value: 0.11900000000000001
- type: precision_at_3
value: 19.023
- type: precision_at_5
value: 13.911000000000001
- type: recall_at_1
value: 28.216
- type: recall_at_10
value: 70.8
- type: recall_at_100
value: 91.857
- type: recall_at_1000
value: 97.941
- type: recall_at_3
value: 49.196
- type: recall_at_5
value: 59.072
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.22800000000001
- type: map_at_10
value: 85.115
- type: map_at_100
value: 85.72
- type: map_at_1000
value: 85.737
- type: map_at_3
value: 82.149
- type: map_at_5
value: 84.029
- type: mrr_at_1
value: 81.96
- type: mrr_at_10
value: 88.00200000000001
- type: mrr_at_100
value: 88.088
- type: mrr_at_1000
value: 88.089
- type: mrr_at_3
value: 87.055
- type: mrr_at_5
value: 87.715
- type: ndcg_at_1
value: 82.01
- type: ndcg_at_10
value: 88.78
- type: ndcg_at_100
value: 89.91
- type: ndcg_at_1000
value: 90.013
- type: ndcg_at_3
value: 85.957
- type: ndcg_at_5
value: 87.56
- type: precision_at_1
value: 82.01
- type: precision_at_10
value: 13.462
- type: precision_at_100
value: 1.528
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.732000000000003
- type: recall_at_1
value: 71.22800000000001
- type: recall_at_10
value: 95.69
- type: recall_at_100
value: 99.531
- type: recall_at_1000
value: 99.98
- type: recall_at_3
value: 87.632
- type: recall_at_5
value: 92.117
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 52.31768034366916
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 60.640266772723606
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.7780000000000005
- type: map_at_10
value: 12.299
- type: map_at_100
value: 14.363000000000001
- type: map_at_1000
value: 14.71
- type: map_at_3
value: 8.738999999999999
- type: map_at_5
value: 10.397
- type: mrr_at_1
value: 23.599999999999998
- type: mrr_at_10
value: 34.845
- type: mrr_at_100
value: 35.916
- type: mrr_at_1000
value: 35.973
- type: mrr_at_3
value: 31.7
- type: mrr_at_5
value: 33.535
- type: ndcg_at_1
value: 23.599999999999998
- type: ndcg_at_10
value: 20.522000000000002
- type: ndcg_at_100
value: 28.737000000000002
- type: ndcg_at_1000
value: 34.596
- type: ndcg_at_3
value: 19.542
- type: ndcg_at_5
value: 16.958000000000002
- type: precision_at_1
value: 23.599999999999998
- type: precision_at_10
value: 10.67
- type: precision_at_100
value: 2.259
- type: precision_at_1000
value: 0.367
- type: precision_at_3
value: 18.333
- type: precision_at_5
value: 14.879999999999999
- type: recall_at_1
value: 4.7780000000000005
- type: recall_at_10
value: 21.617
- type: recall_at_100
value: 45.905
- type: recall_at_1000
value: 74.42
- type: recall_at_3
value: 11.148
- type: recall_at_5
value: 15.082999999999998
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.22372750297885
- type: cos_sim_spearman
value: 79.40972617119405
- type: euclidean_pearson
value: 80.6101072020434
- type: euclidean_spearman
value: 79.53844217225202
- type: manhattan_pearson
value: 80.57265975286111
- type: manhattan_spearman
value: 79.46335611792958
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 85.43713315520749
- type: cos_sim_spearman
value: 77.44128693329532
- type: euclidean_pearson
value: 81.63869928101123
- type: euclidean_spearman
value: 77.29512977961515
- type: manhattan_pearson
value: 81.63704185566183
- type: manhattan_spearman
value: 77.29909412738657
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 81.59451537860527
- type: cos_sim_spearman
value: 82.97994638856723
- type: euclidean_pearson
value: 82.89478688288412
- type: euclidean_spearman
value: 83.58740751053104
- type: manhattan_pearson
value: 82.69140840941608
- type: manhattan_spearman
value: 83.33665956040555
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 82.00756527711764
- type: cos_sim_spearman
value: 81.83560996841379
- type: euclidean_pearson
value: 82.07684151976518
- type: euclidean_spearman
value: 82.00913052060511
- type: manhattan_pearson
value: 82.05690778488794
- type: manhattan_spearman
value: 82.02260252019525
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 86.13710262895447
- type: cos_sim_spearman
value: 87.26412811156248
- type: euclidean_pearson
value: 86.94151453230228
- type: euclidean_spearman
value: 87.5363796699571
- type: manhattan_pearson
value: 86.86989424083748
- type: manhattan_spearman
value: 87.47315940781353
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.0230597603627
- type: cos_sim_spearman
value: 84.93344499318864
- type: euclidean_pearson
value: 84.23754743431141
- type: euclidean_spearman
value: 85.09707376597099
- type: manhattan_pearson
value: 84.04325160987763
- type: manhattan_spearman
value: 84.89353071339909
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.75620824563921
- type: cos_sim_spearman
value: 87.15065513706398
- type: euclidean_pearson
value: 88.26281533633521
- type: euclidean_spearman
value: 87.51963738643983
- type: manhattan_pearson
value: 88.25599267618065
- type: manhattan_spearman
value: 87.58048736047483
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.74645319195137
- type: cos_sim_spearman
value: 65.29996325037214
- type: euclidean_pearson
value: 67.04297794086443
- type: euclidean_spearman
value: 65.43841726694343
- type: manhattan_pearson
value: 67.39459955690904
- type: manhattan_spearman
value: 65.92864704413651
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 84.31291020270801
- type: cos_sim_spearman
value: 85.86473738688068
- type: euclidean_pearson
value: 85.65537275064152
- type: euclidean_spearman
value: 86.13087454209642
- type: manhattan_pearson
value: 85.43946955047609
- type: manhattan_spearman
value: 85.91568175344916
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 85.93798118350695
- type: mrr
value: 95.93536274908824
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 57.594
- type: map_at_10
value: 66.81899999999999
- type: map_at_100
value: 67.368
- type: map_at_1000
value: 67.4
- type: map_at_3
value: 64.061
- type: map_at_5
value: 65.47
- type: mrr_at_1
value: 60.667
- type: mrr_at_10
value: 68.219
- type: mrr_at_100
value: 68.655
- type: mrr_at_1000
value: 68.684
- type: mrr_at_3
value: 66.22200000000001
- type: mrr_at_5
value: 67.289
- type: ndcg_at_1
value: 60.667
- type: ndcg_at_10
value: 71.275
- type: ndcg_at_100
value: 73.642
- type: ndcg_at_1000
value: 74.373
- type: ndcg_at_3
value: 66.521
- type: ndcg_at_5
value: 68.581
- type: precision_at_1
value: 60.667
- type: precision_at_10
value: 9.433
- type: precision_at_100
value: 1.0699999999999998
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.556
- type: precision_at_5
value: 16.8
- type: recall_at_1
value: 57.594
- type: recall_at_10
value: 83.622
- type: recall_at_100
value: 94.167
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.64399999999999
- type: recall_at_5
value: 75.983
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.85841584158416
- type: cos_sim_ap
value: 96.66996142314342
- type: cos_sim_f1
value: 92.83208020050125
- type: cos_sim_precision
value: 93.06532663316584
- type: cos_sim_recall
value: 92.60000000000001
- type: dot_accuracy
value: 99.85841584158416
- type: dot_ap
value: 96.6775307676576
- type: dot_f1
value: 92.69289729177312
- type: dot_precision
value: 94.77533960292581
- type: dot_recall
value: 90.7
- type: euclidean_accuracy
value: 99.86138613861387
- type: euclidean_ap
value: 96.6338454403108
- type: euclidean_f1
value: 92.92214357937311
- type: euclidean_precision
value: 93.96728016359918
- type: euclidean_recall
value: 91.9
- type: manhattan_accuracy
value: 99.86237623762376
- type: manhattan_ap
value: 96.60370449645053
- type: manhattan_f1
value: 92.91177970423253
- type: manhattan_precision
value: 94.7970863683663
- type: manhattan_recall
value: 91.10000000000001
- type: max_accuracy
value: 99.86237623762376
- type: max_ap
value: 96.6775307676576
- type: max_f1
value: 92.92214357937311
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 60.77977058695198
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 35.2725272535638
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 53.64052466362125
- type: mrr
value: 54.533067014684654
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 30.677624219206578
- type: cos_sim_spearman
value: 30.121368518123447
- type: dot_pearson
value: 30.69870088041608
- type: dot_spearman
value: 29.61284927093751
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.22
- type: map_at_10
value: 1.855
- type: map_at_100
value: 9.885
- type: map_at_1000
value: 23.416999999999998
- type: map_at_3
value: 0.637
- type: map_at_5
value: 1.024
- type: mrr_at_1
value: 88.0
- type: mrr_at_10
value: 93.067
- type: mrr_at_100
value: 93.067
- type: mrr_at_1000
value: 93.067
- type: mrr_at_3
value: 92.667
- type: mrr_at_5
value: 93.067
- type: ndcg_at_1
value: 82.0
- type: ndcg_at_10
value: 75.899
- type: ndcg_at_100
value: 55.115
- type: ndcg_at_1000
value: 48.368
- type: ndcg_at_3
value: 79.704
- type: ndcg_at_5
value: 78.39699999999999
- type: precision_at_1
value: 88.0
- type: precision_at_10
value: 79.60000000000001
- type: precision_at_100
value: 56.06
- type: precision_at_1000
value: 21.206
- type: precision_at_3
value: 84.667
- type: precision_at_5
value: 83.2
- type: recall_at_1
value: 0.22
- type: recall_at_10
value: 2.078
- type: recall_at_100
value: 13.297
- type: recall_at_1000
value: 44.979
- type: recall_at_3
value: 0.6689999999999999
- type: recall_at_5
value: 1.106
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.258
- type: map_at_10
value: 10.439
- type: map_at_100
value: 16.89
- type: map_at_1000
value: 18.407999999999998
- type: map_at_3
value: 5.668
- type: map_at_5
value: 7.718
- type: mrr_at_1
value: 32.653
- type: mrr_at_10
value: 51.159
- type: mrr_at_100
value: 51.714000000000006
- type: mrr_at_1000
value: 51.714000000000006
- type: mrr_at_3
value: 47.959
- type: mrr_at_5
value: 50.407999999999994
- type: ndcg_at_1
value: 29.592000000000002
- type: ndcg_at_10
value: 26.037
- type: ndcg_at_100
value: 37.924
- type: ndcg_at_1000
value: 49.126999999999995
- type: ndcg_at_3
value: 30.631999999999998
- type: ndcg_at_5
value: 28.571
- type: precision_at_1
value: 32.653
- type: precision_at_10
value: 22.857
- type: precision_at_100
value: 7.754999999999999
- type: precision_at_1000
value: 1.529
- type: precision_at_3
value: 34.014
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.258
- type: recall_at_10
value: 16.554
- type: recall_at_100
value: 48.439
- type: recall_at_1000
value: 82.80499999999999
- type: recall_at_3
value: 7.283
- type: recall_at_5
value: 10.732
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.8858
- type: ap
value: 13.835684144362109
- type: f1
value: 53.803351693244586
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 60.50650820599886
- type: f1
value: 60.84357825979259
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 48.52131044852134
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.59337187816654
- type: cos_sim_ap
value: 73.23925826533437
- type: cos_sim_f1
value: 67.34693877551021
- type: cos_sim_precision
value: 62.40432237730752
- type: cos_sim_recall
value: 73.13984168865434
- type: dot_accuracy
value: 85.31322644096085
- type: dot_ap
value: 72.30723963807422
- type: dot_f1
value: 66.47051612112296
- type: dot_precision
value: 62.0792305930845
- type: dot_recall
value: 71.53034300791556
- type: euclidean_accuracy
value: 85.61125350181797
- type: euclidean_ap
value: 73.32843720487845
- type: euclidean_f1
value: 67.36549633745895
- type: euclidean_precision
value: 64.60755813953489
- type: euclidean_recall
value: 70.36939313984169
- type: manhattan_accuracy
value: 85.63509566668654
- type: manhattan_ap
value: 73.16658488311325
- type: manhattan_f1
value: 67.20597386434349
- type: manhattan_precision
value: 63.60424028268551
- type: manhattan_recall
value: 71.2401055408971
- type: max_accuracy
value: 85.63509566668654
- type: max_ap
value: 73.32843720487845
- type: max_f1
value: 67.36549633745895
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.33779640625606
- type: cos_sim_ap
value: 84.83868375898157
- type: cos_sim_f1
value: 77.16506154017773
- type: cos_sim_precision
value: 74.62064005753327
- type: cos_sim_recall
value: 79.88912842623961
- type: dot_accuracy
value: 88.02732176815307
- type: dot_ap
value: 83.95089283763002
- type: dot_f1
value: 76.29635101196631
- type: dot_precision
value: 73.31771720613288
- type: dot_recall
value: 79.52725592854944
- type: euclidean_accuracy
value: 88.44452206310397
- type: euclidean_ap
value: 84.98384576824827
- type: euclidean_f1
value: 77.29311047696697
- type: euclidean_precision
value: 74.51232583065381
- type: euclidean_recall
value: 80.28949799815214
- type: manhattan_accuracy
value: 88.47362906042613
- type: manhattan_ap
value: 84.91421462218432
- type: manhattan_f1
value: 77.05107637204792
- type: manhattan_precision
value: 74.74484256243214
- type: manhattan_recall
value: 79.50415768401602
- type: max_accuracy
value: 88.47362906042613
- type: max_ap
value: 84.98384576824827
- type: max_f1
value: 77.29311047696697
license: mit
language:
- en
---
<h1 align="center">FlagEmbedding</h1>
<h4 align="center">
<p>
<a href=#model-list>Model List</a> |
<a href=#frequently-asked-questions>FAQ</a> |
<a href=#usage>Usage</a> |
<a href="#evaluation">Evaluation</a> |
<a href="#train">Train</a> |
<a href="#contact">Contact</a> |
<a href="#citation">Citation</a> |
<a href="#license">License</a>
<p>
</h4>
More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding).
If you are looking for a model that supports more languages, longer texts, and other retrieval methods, you can try using [bge-m3](https://huggingface.co/BAAI/bge-m3).
[English](README.md) | [中文](https://github.com/FlagOpen/FlagEmbedding/blob/master/README_zh.md)
FlagEmbedding focuses on retrieval-augmented LLMs, consisting of the following projects currently:
- **Long-Context LLM**: [Activation Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon)
- **Fine-tuning of LM** : [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail)
- **Dense Retrieval**: [BGE-M3](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3), [LLM Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), [BGE Embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding)
- **Reranker Model**: [BGE Reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
- **Benchmark**: [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB)
## News
- 1/30/2024: Release **BGE-M3**, a new member to BGE model series! M3 stands for **M**ulti-linguality (100+ languages), **M**ulti-granularities (input length up to 8192), **M**ulti-Functionality (unification of dense, lexical, multi-vec/colbert retrieval).
It is the first embedding model which supports all three retrieval methods, achieving new SOTA on multi-lingual (MIRACL) and cross-lingual (MKQA) benchmarks.
[Technical Report](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/BGE_M3/BGE_M3.pdf) and [Code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3). :fire:
- 1/9/2024: Release [Activation-Beacon](https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/activation_beacon), an effective, efficient, compatible, and low-cost (training) method to extend the context length of LLM. [Technical Report](https://arxiv.org/abs/2401.03462) :fire:
- 12/24/2023: Release **LLaRA**, a LLaMA-7B based dense retriever, leading to state-of-the-art performances on MS MARCO and BEIR. Model and code will be open-sourced. Please stay tuned. [Technical Report](https://arxiv.org/abs/2312.15503) :fire:
- 11/23/2023: Release [LM-Cocktail](https://github.com/FlagOpen/FlagEmbedding/tree/master/LM_Cocktail), a method to maintain general capabilities during fine-tuning by merging multiple language models. [Technical Report](https://arxiv.org/abs/2311.13534) :fire:
- 10/12/2023: Release [LLM-Embedder](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_embedder), a unified embedding model to support diverse retrieval augmentation needs for LLMs. [Technical Report](https://arxiv.org/pdf/2310.07554.pdf)
- 09/15/2023: The [technical report](https://arxiv.org/pdf/2309.07597.pdf) of BGE has been released
- 09/15/2023: The [massive training data](https://data.baai.ac.cn/details/BAAI-MTP) of BGE has been released
- 09/12/2023: New models:
- **New reranker model**: release cross-encoder models `BAAI/bge-reranker-base` and `BAAI/bge-reranker-large`, which are more powerful than embedding model. We recommend to use/fine-tune them to re-rank top-k documents returned by embedding models.
- **update embedding model**: release `bge-*-v1.5` embedding model to alleviate the issue of the similarity distribution, and enhance its retrieval ability without instruction.
<details>
<summary>More</summary>
<!-- ### More -->
- 09/07/2023: Update [fine-tune code](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md): Add script to mine hard negatives and support adding instruction during fine-tuning.
- 08/09/2023: BGE Models are integrated into **Langchain**, you can use it like [this](#using-langchain); C-MTEB **leaderboard** is [available](https://huggingface.co/spaces/mteb/leaderboard).
- 08/05/2023: Release base-scale and small-scale models, **best performance among the models of the same size 🤗**
- 08/02/2023: Release `bge-large-*`(short for BAAI General Embedding) Models, **rank 1st on MTEB and C-MTEB benchmark!** :tada: :tada:
- 08/01/2023: We release the [Chinese Massive Text Embedding Benchmark](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB) (**C-MTEB**), consisting of 31 test dataset.
</details>
## Model List
`bge` is short for `BAAI general embedding`.
| Model | Language | | Description | query instruction for retrieval [1] |
|:-------------------------------|:--------:| :--------:| :--------:|:--------:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | Multilingual | [Inference](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3#usage) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3) | Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens) | |
| [BAAI/llm-embedder](https://huggingface.co/BAAI/llm-embedder) | English | [Inference](./FlagEmbedding/llm_embedder/README.md) [Fine-tune](./FlagEmbedding/llm_embedder/README.md) | a unified embedding model to support diverse retrieval augmentation needs for LLMs | See [README](./FlagEmbedding/llm_embedder/README.md) |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | Chinese and English | [Inference](#usage-for-reranker) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker) | a cross-encoder model which is more accurate but less efficient [2] | |
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | version 1.5 with more reasonable similarity distribution | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-large-en](https://huggingface.co/BAAI/bge-large-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [MTEB](https://huggingface.co/spaces/mteb/leaderboard) leaderboard | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-base-en](https://huggingface.co/BAAI/bge-base-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-en` | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) | English | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) |a small-scale model but with competitive performance | `Represent this sentence for searching relevant passages: ` |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | :trophy: rank **1st** in [C-MTEB](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB) benchmark | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a base-scale model but with similar ability to `bge-large-zh` | `为这个句子生成表示以用于检索相关文章：` |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | Chinese | [Inference](#usage-for-embedding-model) [Fine-tune](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) | a small-scale model but with competitive performance | `为这个句子生成表示以用于检索相关文章：` |
[1\]: If you need to search the relevant passages to a query, we suggest to add the instruction to the query; in other cases, no instruction is needed, just use the original query directly. In all cases, **no instruction** needs to be added to passages.
[2\]: Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. To balance the accuracy and time cost, cross-encoder is widely used to re-rank top-k documents retrieved by other simple models.
For examples, use bge embedding model to retrieve top 100 relevant documents, and then use bge reranker to re-rank the top 100 document to get the final top-3 results.
All models have been uploaded to Huggingface Hub, and you can see them at https://huggingface.co/BAAI.
If you cannot open the Huggingface Hub, you also can download the models at https://model.baai.ac.cn/models .
## Frequently asked questions
<details>
<summary>1. How to fine-tune bge embedding model?</summary>
<!-- ### How to fine-tune bge embedding model? -->
Following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune) to prepare data and fine-tune your model.
Some suggestions:
- Mine hard negatives following this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune#hard-negatives), which can improve the retrieval performance.
- If you pre-train bge on your data, the pre-trained model cannot be directly used to calculate similarity, and it must be fine-tuned with contrastive learning before computing similarity.
- If the accuracy of the fine-tuned model is still not high, it is recommended to use/fine-tune the cross-encoder model (bge-reranker) to re-rank top-k results. Hard negatives also are needed to fine-tune reranker.
</details>
<details>
<summary>2. The similarity score between two dissimilar sentences is higher than 0.5</summary>
<!-- ### The similarity score between two dissimilar sentences is higher than 0.5 -->
**Suggest to use bge v1.5, which alleviates the issue of the similarity distribution.**
Since we finetune the models by contrastive learning with a temperature of 0.01,
the similarity distribution of the current BGE model is about in the interval \[0.6, 1\].
So a similarity score greater than 0.5 does not indicate that the two sentences are similar.
For downstream tasks, such as passage retrieval or semantic similarity,
**what matters is the relative order of the scores, not the absolute value.**
If you need to filter similar sentences based on a similarity threshold,
please select an appropriate similarity threshold based on the similarity distribution on your data (such as 0.8, 0.85, or even 0.9).
</details>
<details>
<summary>3. When does the query instruction need to be used</summary>
<!-- ### When does the query instruction need to be used -->
For the `bge-*-v1.5`, we improve its retrieval ability when not using instruction.
No instruction only has a slight degradation in retrieval performance compared with using instruction.
So you can generate embedding without instruction in all cases for convenience.
For a retrieval task that uses short queries to find long related documents,
it is recommended to add instructions for these short queries.
**The best method to decide whether to add instructions for queries is choosing the setting that achieves better performance on your task.**
In all cases, the documents/passages do not need to add the instruction.
</details>
## Usage
### Usage for Embedding Model
Here are some examples for using `bge` models with
[FlagEmbedding](#using-flagembedding), [Sentence-Transformers](#using-sentence-transformers), [Langchain](#using-langchain), or [Huggingface Transformers](#using-huggingface-transformers).
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
If it doesn't work for you, you can see [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md) for more methods to install FlagEmbedding.
```python
from FlagEmbedding import FlagModel
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode(sentences_1)
embeddings_2 = model.encode(sentences_2)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# for s2p(short query to long passage) retrieval task, suggest to use encode_queries() which will automatically add the instruction to each query
# corpus in retrieval task can still use encode() or encode_corpus(), since they don't need instruction
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
```
For the value of the argument `query_instruction_for_retrieval`, see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list).
By default, FlagModel will use all available GPUs when encoding. Please set `os.environ["CUDA_VISIBLE_DEVICES"]` to select specific GPUs.
You also can set `os.environ["CUDA_VISIBLE_DEVICES"]=""` to make all GPUs unavailable.
#### Using Sentence-Transformers
You can also use the `bge` models with [sentence-transformers](https://www.SBERT.net):
```
pip install -U sentence-transformers
```
```python
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
```
For s2p(short query to long passage) retrieval task,
each short query should start with an instruction (instructions see [Model List](https://github.com/FlagOpen/FlagEmbedding/tree/master#model-list)).
But the instruction is not needed for passages.
```python
from sentence_transformers import SentenceTransformer
queries = ['query_1', 'query_2']
passages = ["样例文档-1", "样例文档-2"]
instruction = "为这个句子生成表示以用于检索相关文章："
model = SentenceTransformer('BAAI/bge-large-zh-v1.5')
q_embeddings = model.encode([instruction+q for q in queries], normalize_embeddings=True)
p_embeddings = model.encode(passages, normalize_embeddings=True)
scores = q_embeddings @ p_embeddings.T
```
#### Using Langchain
You can use `bge` in langchain like this:
```python
from langchain.embeddings import HuggingFaceBgeEmbeddings
model_name = "BAAI/bge-large-en-v1.5"
model_kwargs = {'device': 'cuda'}
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity
model = HuggingFaceBgeEmbeddings(
model_name=model_name,
model_kwargs=model_kwargs,
encode_kwargs=encode_kwargs,
query_instruction="为这个句子生成表示以用于检索相关文章："
)
model.query_instruction = "为这个句子生成表示以用于检索相关文章："
```
#### Using HuggingFace Transformers
With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-large-zh-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-large-zh-v1.5')
model.eval()
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = model_output[0][:, 0]
# normalize embeddings
sentence_embeddings = torch.nn.functional.normalize(sentence_embeddings, p=2, dim=1)
print("Sentence embeddings:", sentence_embeddings)
```
### Usage for Reranker
Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding.
You can get a relevance score by inputting query and passage to the reranker.
The reranker is optimized based cross-entropy loss, so the relevance score is not bounded to a specific range.
#### Using FlagEmbedding
```
pip install -U FlagEmbedding
```
Get relevance scores (higher scores indicate more relevance):
```python
from FlagEmbedding import FlagReranker
reranker = FlagReranker('BAAI/bge-reranker-large', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
score = reranker.compute_score(['query', 'passage'])
print(score)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
print(scores)
```
#### Using Huggingface transformers
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-large')
model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-large')
model.eval()
pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
with torch.no_grad():
inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
print(scores)
```
#### Usage of the ONNX files
```python
from optimum.onnxruntime import ORTModelForFeatureExtraction # type: ignore
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-small-en-v1.5')
model = AutoModel.from_pretrained('BAAI/bge-small-en-v1.5')
model_ort = ORTModelForFeatureExtraction.from_pretrained('BAAI/bge-small-en-v1.5', file_name="onnx/model.onnx")
# Sentences we want sentence embeddings for
sentences = ["样例数据-1", "样例数据-2"]
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# for s2p(short query to long passage) retrieval task, add an instruction to query (not add instruction for passages)
# encoded_input = tokenizer([instruction + q for q in queries], padding=True, truncation=True, return_tensors='pt')
model_output_ort = model_ort(**encoded_input)
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# model_output and model_output_ort are identical
```
#### Usage via infinity
Its also possible to deploy the onnx files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference.
```python
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs
sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
))
async def main():
async with engine:
embeddings, usage = await engine.embed(sentences=sentences)
asyncio.run(main())
```
## Evaluation
`baai-general-embedding` models achieve **state-of-the-art performance on both MTEB and C-MTEB leaderboard!**
For more details and evaluation tools see our [scripts](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md).
- **MTEB**:
| Model Name | Dimension | Sequence Length | Average (56) | Retrieval (15) |Clustering (11) | Pair Classification (3) | Reranking (4) | STS (10) | Summarization (1) | Classification (12) |
|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | **64.23** | **54.29** | 46.08 | 87.12 | 60.03 | 83.11 | 31.61 | 75.97 |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.55 | 53.25 | 45.77 | 86.55 | 58.86 | 82.4 | 31.07 | 75.53 |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | 62.17 |51.68 | 43.82 | 84.92 | 58.36 | 81.59 | 30.12 | 74.14 |
| [bge-large-en](https://huggingface.co/BAAI/bge-large-en) | 1024 | 512 | 63.98 | 53.9 | 46.98 | 85.8 | 59.48 | 81.56 | 32.06 | 76.21 |
| [bge-base-en](https://huggingface.co/BAAI/bge-base-en) | 768 | 512 | 63.36 | 53.0 | 46.32 | 85.86 | 58.7 | 81.84 | 29.27 | 75.27 |
| [gte-large](https://huggingface.co/thenlper/gte-large) | 1024 | 512 | 63.13 | 52.22 | 46.84 | 85.00 | 59.13 | 83.35 | 31.66 | 73.33 |
| [gte-base](https://huggingface.co/thenlper/gte-base) | 768 | 512 | 62.39 | 51.14 | 46.2 | 84.57 | 58.61 | 82.3 | 31.17 | 73.01 |
| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1024| 512 | 62.25 | 50.56 | 44.49 | 86.03 | 56.61 | 82.05 | 30.19 | 75.24 |
| [bge-small-en](https://huggingface.co/BAAI/bge-small-en) | 384 | 512 | 62.11 | 51.82 | 44.31 | 83.78 | 57.97 | 80.72 | 30.53 | 74.37 |
| [instructor-xl](https://huggingface.co/hkunlp/instructor-xl) | 768 | 512 | 61.79 | 49.26 | 44.74 | 86.62 | 57.29 | 83.06 | 32.32 | 61.79 |
| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 768 | 512 | 61.5 | 50.29 | 43.80 | 85.73 | 55.91 | 81.05 | 30.28 | 73.84 |
| [gte-small](https://huggingface.co/thenlper/gte-small) | 384 | 512 | 61.36 | 49.46 | 44.89 | 83.54 | 57.7 | 82.07 | 30.42 | 72.31 |
| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | 1536 | 8192 | 60.99 | 49.25 | 45.9 | 84.89 | 56.32 | 80.97 | 30.8 | 70.93 |
| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 384 | 512 | 59.93 | 49.04 | 39.92 | 84.67 | 54.32 | 80.39 | 31.16 | 72.94 |
| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 768 | 512 | 59.51 | 42.24 | 43.72 | 85.06 | 56.42 | 82.63 | 30.08 | 73.42 |
| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) | 768 | 514 | 57.78 | 43.81 | 43.69 | 83.04 | 59.36 | 80.28 | 27.49 | 65.07 |
| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) | 4096 | 2048 | 57.59 | 48.22 | 38.93 | 81.9 | 55.65 | 77.74 | 33.6 | 66.19 |
- **C-MTEB**:
We create the benchmark C-MTEB for Chinese text embedding which consists of 31 datasets from 6 tasks.
Please refer to [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/README.md) for a detailed introduction.
| Model | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| [**BAAI/bge-large-zh-v1.5**](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 1024 | **64.53** | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 |
| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 |
| [BAAI/bge-small-zh-v1.5](https://huggingface.co/BAAI/bge-small-zh-v1.5) | 512 | 57.82 | 61.77 | 49.11 | 70.41 | 63.96 | 60.92 | 44.18 |
| [BAAI/bge-large-zh](https://huggingface.co/BAAI/bge-large-zh) | 1024 | 64.20 | 71.53 | 54.98 | 78.94 | 68.32 | 65.11 | 48.39 |
| [bge-large-zh-noinstruct](https://huggingface.co/BAAI/bge-large-zh-noinstruct) | 1024 | 63.53 | 70.55 | 53 | 76.77 | 68.58 | 64.91 | 50.01 |
| [BAAI/bge-base-zh](https://huggingface.co/BAAI/bge-base-zh) | 768 | 62.96 | 69.53 | 54.12 | 77.5 | 67.07 | 64.91 | 47.63 |
| [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 1024 | 58.79 | 63.66 | 48.44 | 69.89 | 67.34 | 56.00 | 48.23 |
| [BAAI/bge-small-zh](https://huggingface.co/BAAI/bge-small-zh) | 512 | 58.27 | 63.07 | 49.45 | 70.35 | 63.64 | 61.48 | 45.09 |
| [m3e-base](https://huggingface.co/moka-ai/m3e-base) | 768 | 57.10 | 56.91 | 50.47 | 63.99 | 67.52 | 59.34 | 47.68 |
| [m3e-large](https://huggingface.co/moka-ai/m3e-large) | 1024 | 57.05 | 54.75 | 50.42 | 64.3 | 68.2 | 59.66 | 48.88 |
| [multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) | 768 | 55.48 | 61.63 | 46.49 | 67.07 | 65.35 | 54.35 | 40.68 |
| [multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) | 384 | 55.38 | 59.95 | 45.27 | 66.45 | 65.85 | 53.86 | 45.26 |
| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 |
| [luotuo](https://huggingface.co/silk-road/luotuo-bert-medium) | 1024 | 49.37 | 44.4 | 42.78 | 66.62 | 61 | 49.25 | 44.39 |
| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 |
| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 |
- **Reranking**:
See [C_MTEB](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB/) for evaluation script.
| Model | T2Reranking | T2RerankingZh2En\* | T2RerankingEn2Zh\* | MMarcoReranking | CMedQAv1 | CMedQAv2 | Avg |
|:-------------------------------|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|:--------:|
| text2vec-base-multilingual | 64.66 | 62.94 | 62.51 | 14.37 | 48.46 | 48.6 | 50.26 |
| multilingual-e5-small | 65.62 | 60.94 | 56.41 | 29.91 | 67.26 | 66.54 | 57.78 |
| multilingual-e5-large | 64.55 | 61.61 | 54.28 | 28.6 | 67.42 | 67.92 | 57.4 |
| multilingual-e5-base | 64.21 | 62.13 | 54.68 | 29.5 | 66.23 | 66.98 | 57.29 |
| m3e-base | 66.03 | 62.74 | 56.07 | 17.51 | 77.05 | 76.76 | 59.36 |
| m3e-large | 66.13 | 62.72 | 56.1 | 16.46 | 77.76 | 78.27 | 59.57 |
| bge-base-zh-v1.5 | 66.49 | 63.25 | 57.02 | 29.74 | 80.47 | 84.88 | 63.64 |
| bge-large-zh-v1.5 | 65.74 | 63.39 | 57.03 | 28.74 | 83.45 | 85.44 | 63.97 |
| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | 67.28 | 63.95 | 60.45 | 35.46 | 81.26 | 84.1 | 65.42 |
| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | 67.6 | 64.03 | 61.44 | 37.16 | 82.15 | 84.18 | 66.09 |
\* : T2RerankingZh2En and T2RerankingEn2Zh are cross-language retrieval tasks
## Train
### BAAI Embedding
We pre-train the models using [retromae](https://github.com/staoxiao/RetroMAE) and train them on large-scale pairs data using contrastive learning.
**You can fine-tune the embedding model on your data following our [examples](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune).**
We also provide a [pre-train example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/pretrain).
Note that the goal of pre-training is to reconstruct the text, and the pre-trained model cannot be used for similarity calculation directly, it needs to be fine-tuned.
More training details for bge see [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/blob/master/FlagEmbedding/baai_general_embedding/README.md).
### BGE Reranker
Cross-encoder will perform full-attention over the input pair,
which is more accurate than embedding model (i.e., bi-encoder) but more time-consuming than embedding model.
Therefore, it can be used to re-rank the top-k documents returned by embedding model.
We train the cross-encoder on a multilingual pair data,
The data format is the same as embedding model, so you can fine-tune it easily following our [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/reranker).
More details please refer to [./FlagEmbedding/reranker/README.md](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker)
## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Shitao Xiao([email protected]) and Zheng Liu([email protected]).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge_embedding,
title={C-Pack: Packaged Resources To Advance General Chinese Embedding},
author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
year={2023},
eprint={2309.07597},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## License
FlagEmbedding is licensed under the [MIT License](https://github.com/FlagOpen/FlagEmbedding/blob/master/LICENSE). The released models can be used for commercial purposes free of charge.
|
ahmet1338/finetuned_embedder | ahmet1338 | 2025-05-23T10:54:09Z | 16 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"safetensors",
"t5",
"text-embedding",
"embeddings",
"information-retrieval",
"beir",
"text-classification",
"language-model",
"text-clustering",
"text-semantic-similarity",
"text-evaluation",
"prompt-retrieval",
"text-reranking",
"feature-extraction",
"sentence-similarity",
"transformers",
"English",
"Sentence Similarity",
"natural_questions",
"ms_marco",
"fever",
"hotpot_qa",
"mteb",
"en",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | sentence-similarity | 2024-04-22T08:44:42Z | ---
pipeline_tag: sentence-similarity
tags:
- text-embedding
- embeddings
- information-retrieval
- beir
- text-classification
- language-model
- text-clustering
- text-semantic-similarity
- text-evaluation
- prompt-retrieval
- text-reranking
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- t5
- English
- Sentence Similarity
- natural_questions
- ms_marco
- fever
- hotpot_qa
- mteb
language: en
inference: false
license: apache-2.0
model-index:
- name: INSTRUCTOR
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 88.13432835820896
- type: ap
value: 59.298209334395665
- type: f1
value: 83.31769058643586
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.526375
- type: ap
value: 88.16327709705504
- type: f1
value: 91.51095801287843
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 47.856
- type: f1
value: 45.41490917650942
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 31.223
- type: map_at_10
value: 47.947
- type: map_at_100
value: 48.742000000000004
- type: map_at_1000
value: 48.745
- type: map_at_3
value: 43.137
- type: map_at_5
value: 45.992
- type: mrr_at_1
value: 32.432
- type: mrr_at_10
value: 48.4
- type: mrr_at_100
value: 49.202
- type: mrr_at_1000
value: 49.205
- type: mrr_at_3
value: 43.551
- type: mrr_at_5
value: 46.467999999999996
- type: ndcg_at_1
value: 31.223
- type: ndcg_at_10
value: 57.045
- type: ndcg_at_100
value: 60.175
- type: ndcg_at_1000
value: 60.233000000000004
- type: ndcg_at_3
value: 47.171
- type: ndcg_at_5
value: 52.322
- type: precision_at_1
value: 31.223
- type: precision_at_10
value: 8.599
- type: precision_at_100
value: 0.991
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 19.63
- type: precision_at_5
value: 14.282
- type: recall_at_1
value: 31.223
- type: recall_at_10
value: 85.989
- type: recall_at_100
value: 99.075
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 58.89
- type: recall_at_5
value: 71.408
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 43.1621946393635
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 32.56417132407894
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 64.29539304390207
- type: mrr
value: 76.44484017060196
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_spearman
value: 84.38746499431112
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 78.51298701298701
- type: f1
value: 77.49041754069235
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 37.61848554098577
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 31.32623280148178
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 35.803000000000004
- type: map_at_10
value: 48.848
- type: map_at_100
value: 50.5
- type: map_at_1000
value: 50.602999999999994
- type: map_at_3
value: 45.111000000000004
- type: map_at_5
value: 47.202
- type: mrr_at_1
value: 44.635000000000005
- type: mrr_at_10
value: 55.593
- type: mrr_at_100
value: 56.169999999999995
- type: mrr_at_1000
value: 56.19499999999999
- type: mrr_at_3
value: 53.361999999999995
- type: mrr_at_5
value: 54.806999999999995
- type: ndcg_at_1
value: 44.635000000000005
- type: ndcg_at_10
value: 55.899
- type: ndcg_at_100
value: 60.958
- type: ndcg_at_1000
value: 62.302
- type: ndcg_at_3
value: 51.051
- type: ndcg_at_5
value: 53.351000000000006
- type: precision_at_1
value: 44.635000000000005
- type: precision_at_10
value: 10.786999999999999
- type: precision_at_100
value: 1.6580000000000001
- type: precision_at_1000
value: 0.213
- type: precision_at_3
value: 24.893
- type: precision_at_5
value: 17.740000000000002
- type: recall_at_1
value: 35.803000000000004
- type: recall_at_10
value: 68.657
- type: recall_at_100
value: 89.77199999999999
- type: recall_at_1000
value: 97.67
- type: recall_at_3
value: 54.066
- type: recall_at_5
value: 60.788
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 33.706
- type: map_at_10
value: 44.896
- type: map_at_100
value: 46.299
- type: map_at_1000
value: 46.44
- type: map_at_3
value: 41.721000000000004
- type: map_at_5
value: 43.486000000000004
- type: mrr_at_1
value: 41.592
- type: mrr_at_10
value: 50.529
- type: mrr_at_100
value: 51.22
- type: mrr_at_1000
value: 51.258
- type: mrr_at_3
value: 48.205999999999996
- type: mrr_at_5
value: 49.528
- type: ndcg_at_1
value: 41.592
- type: ndcg_at_10
value: 50.77199999999999
- type: ndcg_at_100
value: 55.383
- type: ndcg_at_1000
value: 57.288
- type: ndcg_at_3
value: 46.324
- type: ndcg_at_5
value: 48.346000000000004
- type: precision_at_1
value: 41.592
- type: precision_at_10
value: 9.516
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.2
- type: precision_at_3
value: 22.399
- type: precision_at_5
value: 15.770999999999999
- type: recall_at_1
value: 33.706
- type: recall_at_10
value: 61.353
- type: recall_at_100
value: 80.182
- type: recall_at_1000
value: 91.896
- type: recall_at_3
value: 48.204
- type: recall_at_5
value: 53.89699999999999
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 44.424
- type: map_at_10
value: 57.169000000000004
- type: map_at_100
value: 58.202
- type: map_at_1000
value: 58.242000000000004
- type: map_at_3
value: 53.825
- type: map_at_5
value: 55.714
- type: mrr_at_1
value: 50.470000000000006
- type: mrr_at_10
value: 60.489000000000004
- type: mrr_at_100
value: 61.096
- type: mrr_at_1000
value: 61.112
- type: mrr_at_3
value: 58.192
- type: mrr_at_5
value: 59.611999999999995
- type: ndcg_at_1
value: 50.470000000000006
- type: ndcg_at_10
value: 63.071999999999996
- type: ndcg_at_100
value: 66.964
- type: ndcg_at_1000
value: 67.659
- type: ndcg_at_3
value: 57.74399999999999
- type: ndcg_at_5
value: 60.367000000000004
- type: precision_at_1
value: 50.470000000000006
- type: precision_at_10
value: 10.019
- type: precision_at_100
value: 1.29
- type: precision_at_1000
value: 0.13899999999999998
- type: precision_at_3
value: 25.558999999999997
- type: precision_at_5
value: 17.467
- type: recall_at_1
value: 44.424
- type: recall_at_10
value: 77.02
- type: recall_at_100
value: 93.738
- type: recall_at_1000
value: 98.451
- type: recall_at_3
value: 62.888
- type: recall_at_5
value: 69.138
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 26.294
- type: map_at_10
value: 34.503
- type: map_at_100
value: 35.641
- type: map_at_1000
value: 35.724000000000004
- type: map_at_3
value: 31.753999999999998
- type: map_at_5
value: 33.190999999999995
- type: mrr_at_1
value: 28.362
- type: mrr_at_10
value: 36.53
- type: mrr_at_100
value: 37.541000000000004
- type: mrr_at_1000
value: 37.602000000000004
- type: mrr_at_3
value: 33.917
- type: mrr_at_5
value: 35.358000000000004
- type: ndcg_at_1
value: 28.362
- type: ndcg_at_10
value: 39.513999999999996
- type: ndcg_at_100
value: 44.815
- type: ndcg_at_1000
value: 46.839
- type: ndcg_at_3
value: 34.02
- type: ndcg_at_5
value: 36.522
- type: precision_at_1
value: 28.362
- type: precision_at_10
value: 6.101999999999999
- type: precision_at_100
value: 0.9129999999999999
- type: precision_at_1000
value: 0.11399999999999999
- type: precision_at_3
value: 14.161999999999999
- type: precision_at_5
value: 9.966
- type: recall_at_1
value: 26.294
- type: recall_at_10
value: 53.098
- type: recall_at_100
value: 76.877
- type: recall_at_1000
value: 91.834
- type: recall_at_3
value: 38.266
- type: recall_at_5
value: 44.287
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.407
- type: map_at_10
value: 25.185999999999996
- type: map_at_100
value: 26.533
- type: map_at_1000
value: 26.657999999999998
- type: map_at_3
value: 22.201999999999998
- type: map_at_5
value: 23.923
- type: mrr_at_1
value: 20.522000000000002
- type: mrr_at_10
value: 29.522
- type: mrr_at_100
value: 30.644
- type: mrr_at_1000
value: 30.713
- type: mrr_at_3
value: 26.679000000000002
- type: mrr_at_5
value: 28.483000000000004
- type: ndcg_at_1
value: 20.522000000000002
- type: ndcg_at_10
value: 30.656
- type: ndcg_at_100
value: 36.864999999999995
- type: ndcg_at_1000
value: 39.675
- type: ndcg_at_3
value: 25.319000000000003
- type: ndcg_at_5
value: 27.992
- type: precision_at_1
value: 20.522000000000002
- type: precision_at_10
value: 5.795999999999999
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.13999999999999999
- type: precision_at_3
value: 12.396
- type: precision_at_5
value: 9.328
- type: recall_at_1
value: 16.407
- type: recall_at_10
value: 43.164
- type: recall_at_100
value: 69.695
- type: recall_at_1000
value: 89.41900000000001
- type: recall_at_3
value: 28.634999999999998
- type: recall_at_5
value: 35.308
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 30.473
- type: map_at_10
value: 41.676
- type: map_at_100
value: 43.120999999999995
- type: map_at_1000
value: 43.230000000000004
- type: map_at_3
value: 38.306000000000004
- type: map_at_5
value: 40.355999999999995
- type: mrr_at_1
value: 37.536
- type: mrr_at_10
value: 47.643
- type: mrr_at_100
value: 48.508
- type: mrr_at_1000
value: 48.551
- type: mrr_at_3
value: 45.348
- type: mrr_at_5
value: 46.744
- type: ndcg_at_1
value: 37.536
- type: ndcg_at_10
value: 47.823
- type: ndcg_at_100
value: 53.395
- type: ndcg_at_1000
value: 55.271
- type: ndcg_at_3
value: 42.768
- type: ndcg_at_5
value: 45.373000000000005
- type: precision_at_1
value: 37.536
- type: precision_at_10
value: 8.681
- type: precision_at_100
value: 1.34
- type: precision_at_1000
value: 0.165
- type: precision_at_3
value: 20.468
- type: precision_at_5
value: 14.495
- type: recall_at_1
value: 30.473
- type: recall_at_10
value: 60.092999999999996
- type: recall_at_100
value: 82.733
- type: recall_at_1000
value: 94.875
- type: recall_at_3
value: 45.734
- type: recall_at_5
value: 52.691
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.976000000000003
- type: map_at_10
value: 41.097
- type: map_at_100
value: 42.547000000000004
- type: map_at_1000
value: 42.659000000000006
- type: map_at_3
value: 37.251
- type: map_at_5
value: 39.493
- type: mrr_at_1
value: 37.557
- type: mrr_at_10
value: 46.605000000000004
- type: mrr_at_100
value: 47.487
- type: mrr_at_1000
value: 47.54
- type: mrr_at_3
value: 43.721
- type: mrr_at_5
value: 45.411
- type: ndcg_at_1
value: 37.557
- type: ndcg_at_10
value: 47.449000000000005
- type: ndcg_at_100
value: 53.052
- type: ndcg_at_1000
value: 55.010999999999996
- type: ndcg_at_3
value: 41.439
- type: ndcg_at_5
value: 44.292
- type: precision_at_1
value: 37.557
- type: precision_at_10
value: 8.847
- type: precision_at_100
value: 1.357
- type: precision_at_1000
value: 0.16999999999999998
- type: precision_at_3
value: 20.091
- type: precision_at_5
value: 14.384
- type: recall_at_1
value: 29.976000000000003
- type: recall_at_10
value: 60.99099999999999
- type: recall_at_100
value: 84.245
- type: recall_at_1000
value: 96.97200000000001
- type: recall_at_3
value: 43.794
- type: recall_at_5
value: 51.778999999999996
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.099166666666665
- type: map_at_10
value: 38.1365
- type: map_at_100
value: 39.44491666666667
- type: map_at_1000
value: 39.55858333333334
- type: map_at_3
value: 35.03641666666666
- type: map_at_5
value: 36.79833333333334
- type: mrr_at_1
value: 33.39966666666667
- type: mrr_at_10
value: 42.42583333333333
- type: mrr_at_100
value: 43.28575
- type: mrr_at_1000
value: 43.33741666666667
- type: mrr_at_3
value: 39.94975
- type: mrr_at_5
value: 41.41633333333334
- type: ndcg_at_1
value: 33.39966666666667
- type: ndcg_at_10
value: 43.81741666666667
- type: ndcg_at_100
value: 49.08166666666667
- type: ndcg_at_1000
value: 51.121166666666674
- type: ndcg_at_3
value: 38.73575
- type: ndcg_at_5
value: 41.18158333333333
- type: precision_at_1
value: 33.39966666666667
- type: precision_at_10
value: 7.738916666666667
- type: precision_at_100
value: 1.2265833333333331
- type: precision_at_1000
value: 0.15983333333333336
- type: precision_at_3
value: 17.967416666666665
- type: precision_at_5
value: 12.78675
- type: recall_at_1
value: 28.099166666666665
- type: recall_at_10
value: 56.27049999999999
- type: recall_at_100
value: 78.93291666666667
- type: recall_at_1000
value: 92.81608333333334
- type: recall_at_3
value: 42.09775
- type: recall_at_5
value: 48.42533333333334
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 23.663
- type: map_at_10
value: 30.377
- type: map_at_100
value: 31.426
- type: map_at_1000
value: 31.519000000000002
- type: map_at_3
value: 28.069
- type: map_at_5
value: 29.256999999999998
- type: mrr_at_1
value: 26.687
- type: mrr_at_10
value: 33.107
- type: mrr_at_100
value: 34.055
- type: mrr_at_1000
value: 34.117999999999995
- type: mrr_at_3
value: 31.058000000000003
- type: mrr_at_5
value: 32.14
- type: ndcg_at_1
value: 26.687
- type: ndcg_at_10
value: 34.615
- type: ndcg_at_100
value: 39.776
- type: ndcg_at_1000
value: 42.05
- type: ndcg_at_3
value: 30.322
- type: ndcg_at_5
value: 32.157000000000004
- type: precision_at_1
value: 26.687
- type: precision_at_10
value: 5.491
- type: precision_at_100
value: 0.877
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 13.139000000000001
- type: precision_at_5
value: 9.049
- type: recall_at_1
value: 23.663
- type: recall_at_10
value: 45.035
- type: recall_at_100
value: 68.554
- type: recall_at_1000
value: 85.077
- type: recall_at_3
value: 32.982
- type: recall_at_5
value: 37.688
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 17.403
- type: map_at_10
value: 25.197000000000003
- type: map_at_100
value: 26.355
- type: map_at_1000
value: 26.487
- type: map_at_3
value: 22.733
- type: map_at_5
value: 24.114
- type: mrr_at_1
value: 21.37
- type: mrr_at_10
value: 29.091
- type: mrr_at_100
value: 30.018
- type: mrr_at_1000
value: 30.096
- type: mrr_at_3
value: 26.887
- type: mrr_at_5
value: 28.157
- type: ndcg_at_1
value: 21.37
- type: ndcg_at_10
value: 30.026000000000003
- type: ndcg_at_100
value: 35.416
- type: ndcg_at_1000
value: 38.45
- type: ndcg_at_3
value: 25.764
- type: ndcg_at_5
value: 27.742
- type: precision_at_1
value: 21.37
- type: precision_at_10
value: 5.609
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.14300000000000002
- type: precision_at_3
value: 12.423
- type: precision_at_5
value: 9.009
- type: recall_at_1
value: 17.403
- type: recall_at_10
value: 40.573
- type: recall_at_100
value: 64.818
- type: recall_at_1000
value: 86.53699999999999
- type: recall_at_3
value: 28.493000000000002
- type: recall_at_5
value: 33.660000000000004
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.639
- type: map_at_10
value: 38.951
- type: map_at_100
value: 40.238
- type: map_at_1000
value: 40.327
- type: map_at_3
value: 35.842
- type: map_at_5
value: 37.617
- type: mrr_at_1
value: 33.769
- type: mrr_at_10
value: 43.088
- type: mrr_at_100
value: 44.03
- type: mrr_at_1000
value: 44.072
- type: mrr_at_3
value: 40.656
- type: mrr_at_5
value: 42.138999999999996
- type: ndcg_at_1
value: 33.769
- type: ndcg_at_10
value: 44.676
- type: ndcg_at_100
value: 50.416000000000004
- type: ndcg_at_1000
value: 52.227999999999994
- type: ndcg_at_3
value: 39.494
- type: ndcg_at_5
value: 42.013
- type: precision_at_1
value: 33.769
- type: precision_at_10
value: 7.668
- type: precision_at_100
value: 1.18
- type: precision_at_1000
value: 0.145
- type: precision_at_3
value: 18.221
- type: precision_at_5
value: 12.966
- type: recall_at_1
value: 28.639
- type: recall_at_10
value: 57.687999999999995
- type: recall_at_100
value: 82.541
- type: recall_at_1000
value: 94.896
- type: recall_at_3
value: 43.651
- type: recall_at_5
value: 49.925999999999995
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.57
- type: map_at_10
value: 40.004
- type: map_at_100
value: 41.75
- type: map_at_1000
value: 41.97
- type: map_at_3
value: 36.788
- type: map_at_5
value: 38.671
- type: mrr_at_1
value: 35.375
- type: mrr_at_10
value: 45.121
- type: mrr_at_100
value: 45.994
- type: mrr_at_1000
value: 46.04
- type: mrr_at_3
value: 42.227
- type: mrr_at_5
value: 43.995
- type: ndcg_at_1
value: 35.375
- type: ndcg_at_10
value: 46.392
- type: ndcg_at_100
value: 52.196
- type: ndcg_at_1000
value: 54.274
- type: ndcg_at_3
value: 41.163
- type: ndcg_at_5
value: 43.813
- type: precision_at_1
value: 35.375
- type: precision_at_10
value: 8.676
- type: precision_at_100
value: 1.678
- type: precision_at_1000
value: 0.253
- type: precision_at_3
value: 19.104
- type: precision_at_5
value: 13.913
- type: recall_at_1
value: 29.57
- type: recall_at_10
value: 58.779
- type: recall_at_100
value: 83.337
- type: recall_at_1000
value: 95.979
- type: recall_at_3
value: 44.005
- type: recall_at_5
value: 50.975
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.832
- type: map_at_10
value: 29.733999999999998
- type: map_at_100
value: 30.727
- type: map_at_1000
value: 30.843999999999998
- type: map_at_3
value: 26.834999999999997
- type: map_at_5
value: 28.555999999999997
- type: mrr_at_1
value: 22.921
- type: mrr_at_10
value: 31.791999999999998
- type: mrr_at_100
value: 32.666000000000004
- type: mrr_at_1000
value: 32.751999999999995
- type: mrr_at_3
value: 29.144
- type: mrr_at_5
value: 30.622
- type: ndcg_at_1
value: 22.921
- type: ndcg_at_10
value: 34.915
- type: ndcg_at_100
value: 39.744
- type: ndcg_at_1000
value: 42.407000000000004
- type: ndcg_at_3
value: 29.421000000000003
- type: ndcg_at_5
value: 32.211
- type: precision_at_1
value: 22.921
- type: precision_at_10
value: 5.675
- type: precision_at_100
value: 0.872
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 12.753999999999998
- type: precision_at_5
value: 9.353
- type: recall_at_1
value: 20.832
- type: recall_at_10
value: 48.795
- type: recall_at_100
value: 70.703
- type: recall_at_1000
value: 90.187
- type: recall_at_3
value: 34.455000000000005
- type: recall_at_5
value: 40.967
- task:
type: Retrieval
dataset:
type: climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.334
- type: map_at_10
value: 19.009999999999998
- type: map_at_100
value: 21.129
- type: map_at_1000
value: 21.328
- type: map_at_3
value: 15.152
- type: map_at_5
value: 17.084
- type: mrr_at_1
value: 23.453
- type: mrr_at_10
value: 36.099
- type: mrr_at_100
value: 37.069
- type: mrr_at_1000
value: 37.104
- type: mrr_at_3
value: 32.096000000000004
- type: mrr_at_5
value: 34.451
- type: ndcg_at_1
value: 23.453
- type: ndcg_at_10
value: 27.739000000000004
- type: ndcg_at_100
value: 35.836
- type: ndcg_at_1000
value: 39.242
- type: ndcg_at_3
value: 21.263
- type: ndcg_at_5
value: 23.677
- type: precision_at_1
value: 23.453
- type: precision_at_10
value: 9.199
- type: precision_at_100
value: 1.791
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 16.2
- type: precision_at_5
value: 13.147
- type: recall_at_1
value: 10.334
- type: recall_at_10
value: 35.177
- type: recall_at_100
value: 63.009
- type: recall_at_1000
value: 81.938
- type: recall_at_3
value: 19.914
- type: recall_at_5
value: 26.077
- task:
type: Retrieval
dataset:
type: dbpedia-entity
name: MTEB DBPedia
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 8.212
- type: map_at_10
value: 17.386
- type: map_at_100
value: 24.234
- type: map_at_1000
value: 25.724999999999998
- type: map_at_3
value: 12.727
- type: map_at_5
value: 14.785
- type: mrr_at_1
value: 59.25
- type: mrr_at_10
value: 68.687
- type: mrr_at_100
value: 69.133
- type: mrr_at_1000
value: 69.14099999999999
- type: mrr_at_3
value: 66.917
- type: mrr_at_5
value: 67.742
- type: ndcg_at_1
value: 48.625
- type: ndcg_at_10
value: 36.675999999999995
- type: ndcg_at_100
value: 41.543
- type: ndcg_at_1000
value: 49.241
- type: ndcg_at_3
value: 41.373
- type: ndcg_at_5
value: 38.707
- type: precision_at_1
value: 59.25
- type: precision_at_10
value: 28.525
- type: precision_at_100
value: 9.027000000000001
- type: precision_at_1000
value: 1.8339999999999999
- type: precision_at_3
value: 44.833
- type: precision_at_5
value: 37.35
- type: recall_at_1
value: 8.212
- type: recall_at_10
value: 23.188
- type: recall_at_100
value: 48.613
- type: recall_at_1000
value: 73.093
- type: recall_at_3
value: 14.419
- type: recall_at_5
value: 17.798
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 52.725
- type: f1
value: 46.50743309855908
- task:
type: Retrieval
dataset:
type: fever
name: MTEB FEVER
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 55.086
- type: map_at_10
value: 66.914
- type: map_at_100
value: 67.321
- type: map_at_1000
value: 67.341
- type: map_at_3
value: 64.75800000000001
- type: map_at_5
value: 66.189
- type: mrr_at_1
value: 59.28600000000001
- type: mrr_at_10
value: 71.005
- type: mrr_at_100
value: 71.304
- type: mrr_at_1000
value: 71.313
- type: mrr_at_3
value: 69.037
- type: mrr_at_5
value: 70.35
- type: ndcg_at_1
value: 59.28600000000001
- type: ndcg_at_10
value: 72.695
- type: ndcg_at_100
value: 74.432
- type: ndcg_at_1000
value: 74.868
- type: ndcg_at_3
value: 68.72200000000001
- type: ndcg_at_5
value: 71.081
- type: precision_at_1
value: 59.28600000000001
- type: precision_at_10
value: 9.499
- type: precision_at_100
value: 1.052
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 27.503
- type: precision_at_5
value: 17.854999999999997
- type: recall_at_1
value: 55.086
- type: recall_at_10
value: 86.453
- type: recall_at_100
value: 94.028
- type: recall_at_1000
value: 97.052
- type: recall_at_3
value: 75.821
- type: recall_at_5
value: 81.6
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 22.262999999999998
- type: map_at_10
value: 37.488
- type: map_at_100
value: 39.498
- type: map_at_1000
value: 39.687
- type: map_at_3
value: 32.529
- type: map_at_5
value: 35.455
- type: mrr_at_1
value: 44.907000000000004
- type: mrr_at_10
value: 53.239000000000004
- type: mrr_at_100
value: 54.086
- type: mrr_at_1000
value: 54.122
- type: mrr_at_3
value: 51.235
- type: mrr_at_5
value: 52.415
- type: ndcg_at_1
value: 44.907000000000004
- type: ndcg_at_10
value: 45.446
- type: ndcg_at_100
value: 52.429
- type: ndcg_at_1000
value: 55.169000000000004
- type: ndcg_at_3
value: 41.882000000000005
- type: ndcg_at_5
value: 43.178
- type: precision_at_1
value: 44.907000000000004
- type: precision_at_10
value: 12.931999999999999
- type: precision_at_100
value: 2.025
- type: precision_at_1000
value: 0.248
- type: precision_at_3
value: 28.652
- type: precision_at_5
value: 21.204
- type: recall_at_1
value: 22.262999999999998
- type: recall_at_10
value: 52.447
- type: recall_at_100
value: 78.045
- type: recall_at_1000
value: 94.419
- type: recall_at_3
value: 38.064
- type: recall_at_5
value: 44.769
- task:
type: Retrieval
dataset:
type: hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 32.519
- type: map_at_10
value: 45.831
- type: map_at_100
value: 46.815
- type: map_at_1000
value: 46.899
- type: map_at_3
value: 42.836
- type: map_at_5
value: 44.65
- type: mrr_at_1
value: 65.037
- type: mrr_at_10
value: 72.16
- type: mrr_at_100
value: 72.51100000000001
- type: mrr_at_1000
value: 72.53
- type: mrr_at_3
value: 70.682
- type: mrr_at_5
value: 71.54599999999999
- type: ndcg_at_1
value: 65.037
- type: ndcg_at_10
value: 55.17999999999999
- type: ndcg_at_100
value: 58.888
- type: ndcg_at_1000
value: 60.648
- type: ndcg_at_3
value: 50.501
- type: ndcg_at_5
value: 52.977
- type: precision_at_1
value: 65.037
- type: precision_at_10
value: 11.530999999999999
- type: precision_at_100
value: 1.4460000000000002
- type: precision_at_1000
value: 0.168
- type: precision_at_3
value: 31.483
- type: precision_at_5
value: 20.845
- type: recall_at_1
value: 32.519
- type: recall_at_10
value: 57.657000000000004
- type: recall_at_100
value: 72.30199999999999
- type: recall_at_1000
value: 84.024
- type: recall_at_3
value: 47.225
- type: recall_at_5
value: 52.113
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 88.3168
- type: ap
value: 83.80165516037135
- type: f1
value: 88.29942471066407
- task:
type: Retrieval
dataset:
type: msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 20.724999999999998
- type: map_at_10
value: 32.736
- type: map_at_100
value: 33.938
- type: map_at_1000
value: 33.991
- type: map_at_3
value: 28.788000000000004
- type: map_at_5
value: 31.016
- type: mrr_at_1
value: 21.361
- type: mrr_at_10
value: 33.323
- type: mrr_at_100
value: 34.471000000000004
- type: mrr_at_1000
value: 34.518
- type: mrr_at_3
value: 29.453000000000003
- type: mrr_at_5
value: 31.629
- type: ndcg_at_1
value: 21.361
- type: ndcg_at_10
value: 39.649
- type: ndcg_at_100
value: 45.481
- type: ndcg_at_1000
value: 46.775
- type: ndcg_at_3
value: 31.594
- type: ndcg_at_5
value: 35.543
- type: precision_at_1
value: 21.361
- type: precision_at_10
value: 6.3740000000000006
- type: precision_at_100
value: 0.931
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 13.514999999999999
- type: precision_at_5
value: 10.100000000000001
- type: recall_at_1
value: 20.724999999999998
- type: recall_at_10
value: 61.034
- type: recall_at_100
value: 88.062
- type: recall_at_1000
value: 97.86399999999999
- type: recall_at_3
value: 39.072
- type: recall_at_5
value: 48.53
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.8919288645691
- type: f1
value: 93.57059586398059
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.97993616051072
- type: f1
value: 48.244319183606535
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.90047074646941
- type: f1
value: 66.48999056063725
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.34566240753195
- type: f1
value: 73.54164154290658
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 34.21866934757011
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 32.000936217235534
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 31.68189362520352
- type: mrr
value: 32.69603637784303
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 6.078
- type: map_at_10
value: 12.671
- type: map_at_100
value: 16.291
- type: map_at_1000
value: 17.855999999999998
- type: map_at_3
value: 9.610000000000001
- type: map_at_5
value: 11.152
- type: mrr_at_1
value: 43.963
- type: mrr_at_10
value: 53.173
- type: mrr_at_100
value: 53.718999999999994
- type: mrr_at_1000
value: 53.756
- type: mrr_at_3
value: 50.980000000000004
- type: mrr_at_5
value: 52.42
- type: ndcg_at_1
value: 42.415000000000006
- type: ndcg_at_10
value: 34.086
- type: ndcg_at_100
value: 32.545
- type: ndcg_at_1000
value: 41.144999999999996
- type: ndcg_at_3
value: 39.434999999999995
- type: ndcg_at_5
value: 37.888
- type: precision_at_1
value: 43.653
- type: precision_at_10
value: 25.014999999999997
- type: precision_at_100
value: 8.594
- type: precision_at_1000
value: 2.169
- type: precision_at_3
value: 37.049
- type: precision_at_5
value: 33.065
- type: recall_at_1
value: 6.078
- type: recall_at_10
value: 16.17
- type: recall_at_100
value: 34.512
- type: recall_at_1000
value: 65.447
- type: recall_at_3
value: 10.706
- type: recall_at_5
value: 13.158
- task:
type: Retrieval
dataset:
type: nq
name: MTEB NQ
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.378000000000004
- type: map_at_10
value: 42.178
- type: map_at_100
value: 43.32
- type: map_at_1000
value: 43.358000000000004
- type: map_at_3
value: 37.474000000000004
- type: map_at_5
value: 40.333000000000006
- type: mrr_at_1
value: 30.823
- type: mrr_at_10
value: 44.626
- type: mrr_at_100
value: 45.494
- type: mrr_at_1000
value: 45.519
- type: mrr_at_3
value: 40.585
- type: mrr_at_5
value: 43.146
- type: ndcg_at_1
value: 30.794
- type: ndcg_at_10
value: 50.099000000000004
- type: ndcg_at_100
value: 54.900999999999996
- type: ndcg_at_1000
value: 55.69499999999999
- type: ndcg_at_3
value: 41.238
- type: ndcg_at_5
value: 46.081
- type: precision_at_1
value: 30.794
- type: precision_at_10
value: 8.549
- type: precision_at_100
value: 1.124
- type: precision_at_1000
value: 0.12
- type: precision_at_3
value: 18.926000000000002
- type: precision_at_5
value: 14.16
- type: recall_at_1
value: 27.378000000000004
- type: recall_at_10
value: 71.842
- type: recall_at_100
value: 92.565
- type: recall_at_1000
value: 98.402
- type: recall_at_3
value: 49.053999999999995
- type: recall_at_5
value: 60.207
- task:
type: Retrieval
dataset:
type: quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 70.557
- type: map_at_10
value: 84.729
- type: map_at_100
value: 85.369
- type: map_at_1000
value: 85.382
- type: map_at_3
value: 81.72
- type: map_at_5
value: 83.613
- type: mrr_at_1
value: 81.3
- type: mrr_at_10
value: 87.488
- type: mrr_at_100
value: 87.588
- type: mrr_at_1000
value: 87.589
- type: mrr_at_3
value: 86.53
- type: mrr_at_5
value: 87.18599999999999
- type: ndcg_at_1
value: 81.28999999999999
- type: ndcg_at_10
value: 88.442
- type: ndcg_at_100
value: 89.637
- type: ndcg_at_1000
value: 89.70700000000001
- type: ndcg_at_3
value: 85.55199999999999
- type: ndcg_at_5
value: 87.154
- type: precision_at_1
value: 81.28999999999999
- type: precision_at_10
value: 13.489999999999998
- type: precision_at_100
value: 1.54
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.553
- type: precision_at_5
value: 24.708
- type: recall_at_1
value: 70.557
- type: recall_at_10
value: 95.645
- type: recall_at_100
value: 99.693
- type: recall_at_1000
value: 99.995
- type: recall_at_3
value: 87.359
- type: recall_at_5
value: 91.89699999999999
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 63.65060114776209
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 64.63271250680617
- task:
type: Retrieval
dataset:
type: scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 4.263
- type: map_at_10
value: 10.801
- type: map_at_100
value: 12.888
- type: map_at_1000
value: 13.224
- type: map_at_3
value: 7.362
- type: map_at_5
value: 9.149000000000001
- type: mrr_at_1
value: 21
- type: mrr_at_10
value: 31.416
- type: mrr_at_100
value: 32.513
- type: mrr_at_1000
value: 32.58
- type: mrr_at_3
value: 28.116999999999997
- type: mrr_at_5
value: 29.976999999999997
- type: ndcg_at_1
value: 21
- type: ndcg_at_10
value: 18.551000000000002
- type: ndcg_at_100
value: 26.657999999999998
- type: ndcg_at_1000
value: 32.485
- type: ndcg_at_3
value: 16.834
- type: ndcg_at_5
value: 15.204999999999998
- type: precision_at_1
value: 21
- type: precision_at_10
value: 9.84
- type: precision_at_100
value: 2.16
- type: precision_at_1000
value: 0.35500000000000004
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 13.62
- type: recall_at_1
value: 4.263
- type: recall_at_10
value: 19.922
- type: recall_at_100
value: 43.808
- type: recall_at_1000
value: 72.14500000000001
- type: recall_at_3
value: 9.493
- type: recall_at_5
value: 13.767999999999999
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_spearman
value: 81.27446313317233
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_spearman
value: 76.27963301217527
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_spearman
value: 88.18495048450949
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_spearman
value: 81.91982338692046
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_spearman
value: 89.00896818385291
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_spearman
value: 85.48814644586132
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_spearman
value: 90.30116926966582
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_spearman
value: 67.74132963032342
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_spearman
value: 86.87741355780479
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 82.0019012295875
- type: mrr
value: 94.70267024188593
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 50.05
- type: map_at_10
value: 59.36
- type: map_at_100
value: 59.967999999999996
- type: map_at_1000
value: 60.023
- type: map_at_3
value: 56.515
- type: map_at_5
value: 58.272999999999996
- type: mrr_at_1
value: 53
- type: mrr_at_10
value: 61.102000000000004
- type: mrr_at_100
value: 61.476
- type: mrr_at_1000
value: 61.523
- type: mrr_at_3
value: 58.778
- type: mrr_at_5
value: 60.128
- type: ndcg_at_1
value: 53
- type: ndcg_at_10
value: 64.43100000000001
- type: ndcg_at_100
value: 66.73599999999999
- type: ndcg_at_1000
value: 68.027
- type: ndcg_at_3
value: 59.279
- type: ndcg_at_5
value: 61.888
- type: precision_at_1
value: 53
- type: precision_at_10
value: 8.767
- type: precision_at_100
value: 1.01
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 23.444000000000003
- type: precision_at_5
value: 15.667
- type: recall_at_1
value: 50.05
- type: recall_at_10
value: 78.511
- type: recall_at_100
value: 88.5
- type: recall_at_1000
value: 98.333
- type: recall_at_3
value: 64.117
- type: recall_at_5
value: 70.867
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.72178217821782
- type: cos_sim_ap
value: 93.0728601593541
- type: cos_sim_f1
value: 85.6727976766699
- type: cos_sim_precision
value: 83.02063789868667
- type: cos_sim_recall
value: 88.5
- type: dot_accuracy
value: 99.72178217821782
- type: dot_ap
value: 93.07287396168348
- type: dot_f1
value: 85.6727976766699
- type: dot_precision
value: 83.02063789868667
- type: dot_recall
value: 88.5
- type: euclidean_accuracy
value: 99.72178217821782
- type: euclidean_ap
value: 93.07285657982895
- type: euclidean_f1
value: 85.6727976766699
- type: euclidean_precision
value: 83.02063789868667
- type: euclidean_recall
value: 88.5
- type: manhattan_accuracy
value: 99.72475247524753
- type: manhattan_ap
value: 93.02792973059809
- type: manhattan_f1
value: 85.7727737973388
- type: manhattan_precision
value: 87.84067085953879
- type: manhattan_recall
value: 83.8
- type: max_accuracy
value: 99.72475247524753
- type: max_ap
value: 93.07287396168348
- type: max_f1
value: 85.7727737973388
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 68.77583615550819
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 36.151636938606956
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 52.16607939471187
- type: mrr
value: 52.95172046091163
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.314646669495666
- type: cos_sim_spearman
value: 31.83562491439455
- type: dot_pearson
value: 31.314590842874157
- type: dot_spearman
value: 31.83363065810437
- task:
type: Retrieval
dataset:
type: trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.198
- type: map_at_10
value: 1.3010000000000002
- type: map_at_100
value: 7.2139999999999995
- type: map_at_1000
value: 20.179
- type: map_at_3
value: 0.528
- type: map_at_5
value: 0.8019999999999999
- type: mrr_at_1
value: 72
- type: mrr_at_10
value: 83.39999999999999
- type: mrr_at_100
value: 83.39999999999999
- type: mrr_at_1000
value: 83.39999999999999
- type: mrr_at_3
value: 81.667
- type: mrr_at_5
value: 83.06700000000001
- type: ndcg_at_1
value: 66
- type: ndcg_at_10
value: 58.059000000000005
- type: ndcg_at_100
value: 44.316
- type: ndcg_at_1000
value: 43.147000000000006
- type: ndcg_at_3
value: 63.815999999999995
- type: ndcg_at_5
value: 63.005
- type: precision_at_1
value: 72
- type: precision_at_10
value: 61.4
- type: precision_at_100
value: 45.62
- type: precision_at_1000
value: 19.866
- type: precision_at_3
value: 70
- type: precision_at_5
value: 68.8
- type: recall_at_1
value: 0.198
- type: recall_at_10
value: 1.517
- type: recall_at_100
value: 10.587
- type: recall_at_1000
value: 41.233
- type: recall_at_3
value: 0.573
- type: recall_at_5
value: 0.907
- task:
type: Retrieval
dataset:
type: webis-touche2020
name: MTEB Touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 1.894
- type: map_at_10
value: 8.488999999999999
- type: map_at_100
value: 14.445
- type: map_at_1000
value: 16.078
- type: map_at_3
value: 4.589
- type: map_at_5
value: 6.019
- type: mrr_at_1
value: 22.448999999999998
- type: mrr_at_10
value: 39.82
- type: mrr_at_100
value: 40.752
- type: mrr_at_1000
value: 40.771
- type: mrr_at_3
value: 34.354
- type: mrr_at_5
value: 37.721
- type: ndcg_at_1
value: 19.387999999999998
- type: ndcg_at_10
value: 21.563
- type: ndcg_at_100
value: 33.857
- type: ndcg_at_1000
value: 46.199
- type: ndcg_at_3
value: 22.296
- type: ndcg_at_5
value: 21.770999999999997
- type: precision_at_1
value: 22.448999999999998
- type: precision_at_10
value: 19.796
- type: precision_at_100
value: 7.142999999999999
- type: precision_at_1000
value: 1.541
- type: precision_at_3
value: 24.490000000000002
- type: precision_at_5
value: 22.448999999999998
- type: recall_at_1
value: 1.894
- type: recall_at_10
value: 14.931
- type: recall_at_100
value: 45.524
- type: recall_at_1000
value: 83.243
- type: recall_at_3
value: 5.712
- type: recall_at_5
value: 8.386000000000001
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 71.049
- type: ap
value: 13.85116971310922
- type: f1
value: 54.37504302487686
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.1312959818902
- type: f1
value: 64.11413877009383
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 54.13103431861502
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 87.327889372355
- type: cos_sim_ap
value: 77.42059895975699
- type: cos_sim_f1
value: 71.02706903250873
- type: cos_sim_precision
value: 69.75324344950394
- type: cos_sim_recall
value: 72.34828496042216
- type: dot_accuracy
value: 87.327889372355
- type: dot_ap
value: 77.4209479346677
- type: dot_f1
value: 71.02706903250873
- type: dot_precision
value: 69.75324344950394
- type: dot_recall
value: 72.34828496042216
- type: euclidean_accuracy
value: 87.327889372355
- type: euclidean_ap
value: 77.42096495861037
- type: euclidean_f1
value: 71.02706903250873
- type: euclidean_precision
value: 69.75324344950394
- type: euclidean_recall
value: 72.34828496042216
- type: manhattan_accuracy
value: 87.31000774870358
- type: manhattan_ap
value: 77.38930750711619
- type: manhattan_f1
value: 71.07935314027831
- type: manhattan_precision
value: 67.70957726295677
- type: manhattan_recall
value: 74.80211081794195
- type: max_accuracy
value: 87.327889372355
- type: max_ap
value: 77.42096495861037
- type: max_f1
value: 71.07935314027831
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.58939729110878
- type: cos_sim_ap
value: 87.17594155025475
- type: cos_sim_f1
value: 79.21146953405018
- type: cos_sim_precision
value: 76.8918527109307
- type: cos_sim_recall
value: 81.67539267015707
- type: dot_accuracy
value: 89.58939729110878
- type: dot_ap
value: 87.17593963273593
- type: dot_f1
value: 79.21146953405018
- type: dot_precision
value: 76.8918527109307
- type: dot_recall
value: 81.67539267015707
- type: euclidean_accuracy
value: 89.58939729110878
- type: euclidean_ap
value: 87.17592466925834
- type: euclidean_f1
value: 79.21146953405018
- type: euclidean_precision
value: 76.8918527109307
- type: euclidean_recall
value: 81.67539267015707
- type: manhattan_accuracy
value: 89.62626615438352
- type: manhattan_ap
value: 87.16589873161546
- type: manhattan_f1
value: 79.25143598295348
- type: manhattan_precision
value: 76.39494177323712
- type: manhattan_recall
value: 82.32984293193716
- type: max_accuracy
value: 89.62626615438352
- type: max_ap
value: 87.17594155025475
- type: max_f1
value: 79.25143598295348
--- |
alkiskoudounas/xls-r-53-fr-speechmassive-fr-FR | alkiskoudounas | 2025-05-23T10:53:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"intent",
"intent-classification",
"audio",
"fr",
"dataset:FBK-MT/Speech-MASSIVE",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2025-05-23T10:42:22Z | ---
task_categories:
- audio-classification
language:
- fr
tags:
- intent
- intent-classification
- audio-classification
- audio
base_model:
- facebook/wav2vec2-xls-r-300m
datasets:
- FBK-MT/Speech-MASSIVE
library_name: transformers
license: apache-2.0
---
# wav2vec 2.0 XLS-R 53-fr (300m) fine-tuned on Speech-MASSIVE - fr-FR
Speech-MASSIVE is a multilingual Spoken Language Understanding (SLU) dataset comprising the speech counterpart for a portion of the MASSIVE textual corpus.
Speech-MASSIVE covers 12 languages.
It includes spoken and written utterances and is annotated with 60 intents.
The dataset is available on [HuggingFace Hub](https://huggingface.co/datasets/FBK-MT/Speech-MASSIVE).
This is the [jonatasgrosman/wav2vec2-xls-r-53-fr](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-french) model fine-tuned on the fr-FR language.
It achieves the following results on the test set:
- Accuracy: 0.815
- F1: 0.756
## Usage
You can use the model directly in the following manner:
```python
import torch
import librosa
from transformers import AutoModelForAudioClassification, AutoFeatureExtractor
## Load an audio file
audio_array, sr = librosa.load("path_to_audio.wav", sr=16000)
## Load model and feature extractor
model = AutoModelForAudioClassification.from_pretrained("alkiskoudounas/xls-r-53-fr-speechmassive-fr-FR")
feature_extractor = AutoFeatureExtractor.from_pretrained("jonatasgrosman/wav2vec2-large-xlsr-53-french")
## Extract features
inputs = feature_extractor(audio_array.squeeze(), sampling_rate=feature_extractor.sampling_rate, padding=True, return_tensors="pt")
## Compute logits
logits = model(**inputs).logits
```
## Citation
If you use this model in your research, please cite the following papers:
```bibtex
@inproceedings{koudounas2025unlearning,
title={"Alexa, can you forget me?" Machine Unlearning Benchmark in Spoken Language Understanding},
author={Koudounas, Alkis and Savelli, Claudio and Giobergia, Flavio and Baralis, Elena},
booktitle={Proc. Interspeech 2025},
year={2025},
}
``` |
Detecting-ai/pt-ai-detector | Detecting-ai | 2025-05-23T10:51:16Z | 19 | 2 | transformers | [
"transformers",
"onnx",
"safetensors",
"bert",
"text-classification",
"ai-detection",
"portuguese",
"pt",
"dataset:wiki40b-pt",
"dataset:oscar-pt",
"dataset:cc100-pt",
"dataset:europarl-pt",
"dataset:opus-books-pt",
"dataset:Detecting-ai/ai_pt_corpus",
"base_model:neuralmind/bert-base-portuguese-cased",
"base_model:quantized:neuralmind/bert-base-portuguese-cased",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-18T15:29:58Z | ---
license: cc-by-nc-4.0
language:
- pt
tags:
- ai-detection
- text-classification
- portuguese
- bert
- transformers
base_model: neuralmind/bert-base-portuguese-cased
pipeline_tag: text-classification
datasets:
- wiki40b-pt # From consolidated human sources
- oscar-pt # From consolidated human sources
- cc100-pt # From consolidated human sources
- europarl-pt # From consolidated human sources
- opus-books-pt # From consolidated human sources
- Detecting-ai/ai_pt_corpus # AI-generated corpus
model_type: bert
---
# 🇧🇷 pt-ai-detector
**pt-ai-detector** is a BERT-base model fine-tuned to decide whether a Portuguese sentence or paragraph was written by a *human* (`label = 0`) or generated by *AI* (`label = 1`).
| Metric | Value |
| --------------------- | ------------------------------ |
| **Train data** | 1 000 000 human + 1 000 000 AI |
| **Balanced test set** | 1 954 190 (½ human, ½ AI) |
| **Accuracy** | ≈ 99 % |
| **F1 (macro)** | ≈ 0.99 |
| **Model size** | 434 M parameters (≈ 430 MB) |
---
## 📖 Quick usage
**Try it live** at [detecting-ai.com](https://detecting-ai.com/pt) – our team at Detecting-ai built this model and demo so you can instantly test any Portuguese text online.
```python
from transformers import pipeline
clf = pipeline(
"text-classification",
model="Detecting-ai/pt-ai-detector",
tokenizer="Detecting-ai/pt-ai-detector",
device=0 # set -1 for CPU
)
text = "A inteligência artificial está transformando a educação."
print(clf(text)) # → [{'label': 'AI', 'score': 0.987}]
```
| id | label |
| -- | ----- |
| 0 | Human |
| 1 | AI |
---
## 🏋️♂️ Training details
* **Base model:** `neuralmind/bert-base-portuguese-cased`
* **Epochs:** 3 (fp16 on 1 × A100)
* **Batch size:** 32
* **Optimizer/LR:** AdamW 2 × 10⁻⁵
* **Loss:** Cross-entropy
### Data sources
| Corpus | Lines used | Notes |
| ------------------------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------ |
| Human corpus (wiki40b-pt, oscar-pt, cc100-pt, europarl-pt, opus-books-pt) | 1 M sampled | Diverse Portuguese web/news/books |
| **AI corpus** (`Detecting-ai/ai_pt_corpus`) | 1 M | Generated with **OpenAI models** (various GPT-4 / GPT-3.5 variants); prompts cover essays, news, tweets, dialogs, paraphrases, T = 0.6–1.0 |
Datasets were **balanced 1 : 1** and shuffled before training.
---
## 🚦 Intended use
Detect AI-generated Portuguese text in essays, articles, chats, support tickets, etc.
### Limitations
* Not trained on code or non-Portuguese language.
* Accuracy may drop on texts < 10 tokens or heavily paraphrased AI.
* **Commercial use is disallowed** (CC-BY-NC-4.0).
---
## ⚠️ Future work
* Evaluate on adversarial paraphrases.
* Distill/quantize for edge deployment.
* Extend to multilingual detection.
---
## 📜 Citation
```bibtex
@misc{abdurazzoqov2025ptaidetector,
title = {pt-ai-detector: Detecting AI-generated Portuguese Text},
author = {Abdulla Abdurazzoqov},
year = {2025},
howpublished = {Hugging Face hub},
url = {https://huggingface.co/Detecting-ai/pt-ai-detector}
}
```
---
## 💬 License
Creative Commons **CC-BY-NC-4.0** — free for research & personal use; commercial use requires written permission.
|
openclimatefix/pvnet_nl | openclimatefix | 2025-05-23T10:49:25Z | 206 | 0 | pytorch | [
"pytorch",
"en",
"license:mit",
"region:us"
] | null | 2025-05-09T12:50:16Z | ---
language: en
library_name: pytorch
license: mit
---
# PVNet2
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
This model class uses satellite data, numerical weather predictions, and recent Grid Service Point( GSP) PV power output to forecast the near-term (~8 hours) PV power output at all GSPs. More information can be found in the model repo [1] and experimental notes in [this google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing).
- **Developed by:** openclimatefix
- **Model type:** Fusion model
- **Language(s) (NLP):** en
- **License:** mit
# Training Details
## Data
<!-- This should link to a Data 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. -->
The model is trained on data from 2019-2022 and validated on data from 2022-2023. See experimental notes in the [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing) for more details.
### Preprocessing
Data is prepared with the `ocf_data_sampler/torch_datasets/datasets/pvnet_uk` Dataset [2].
## Results
The training logs for the current model can be found here:
- [https://wandb.ai/openclimatefix/NL-Solar/runs/hc2sv5fj](https://wandb.ai/openclimatefix/NL-Solar/runs/hc2sv5fj)
The training logs for all model runs of PVNet2 can be found [here](https://wandb.ai/openclimatefix/pvnet2.1).
Some experimental notes can be found at in [the google doc](https://docs.google.com/document/d/1fbkfkBzp16WbnCg7RDuRDvgzInA6XQu3xh4NCjV-WDA/edit?usp=sharing)
### Hardware
Trained on a single NVIDIA Tesla T4
### Software
- [1] https://github.com/openclimatefix/PVNet
- [2] https://github.com/openclimatefix/ocf-data-sampler |
rinalpandey/xfcjhbvk | rinalpandey | 2025-05-23T10:48:57Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-23T10:48:57Z | ---
license: creativeml-openrail-m
---
|
zyzzc/FranFran-Something-12B-Q4_K_M-GGUF | zyzzc | 2025-05-23T10:48:31Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:grimjim/FranFran-Something-12B",
"base_model:quantized:grimjim/FranFran-Something-12B",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-23T10:47:55Z | ---
base_model: grimjim/FranFran-Something-12B
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
---
# zyzzc/FranFran-Something-12B-Q4_K_M-GGUF
This model was converted to GGUF format from [`grimjim/FranFran-Something-12B`](https://huggingface.co/grimjim/FranFran-Something-12B) 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/grimjim/FranFran-Something-12B) 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 zyzzc/FranFran-Something-12B-Q4_K_M-GGUF --hf-file franfran-something-12b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo zyzzc/FranFran-Something-12B-Q4_K_M-GGUF --hf-file franfran-something-12b-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 zyzzc/FranFran-Something-12B-Q4_K_M-GGUF --hf-file franfran-something-12b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo zyzzc/FranFran-Something-12B-Q4_K_M-GGUF --hf-file franfran-something-12b-q4_k_m.gguf -c 2048
```
|
vermoney/5f0fb89a-9fba-4fb1-bad9-919f0a2165f3 | vermoney | 2025-05-23T10:47:28Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-23T10:29:35Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: 5f0fb89a-9fba-4fb1-bad9-919f0a2165f3
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 5f0fb89a-9fba-4fb1-bad9-919f0a2165f3
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="vermoney/5f0fb89a-9fba-4fb1-bad9-919f0a2165f3", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-9/runs/ub8gfz1s)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
TanAlexanderlz/ALL_RGBCROP-Aug32-16-8B16F | TanAlexanderlz | 2025-05-23T10:47:13Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2025-05-23T08:32:05Z | ---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ALL_RGBCROP-Aug32-16-8B16F
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. -->
# ALL_RGBCROP-Aug32-16-8B16F
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4256
- Accuracy: 0.8856
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3060
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.2025 | 0.1003 | 307 | 0.3056 | 0.9135 |
| 0.0014 | 1.1003 | 614 | 0.6624 | 0.8830 |
| 0.0004 | 2.1003 | 921 | 0.5268 | 0.9054 |
| 0.0669 | 3.1003 | 1228 | 0.6246 | 0.8974 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
SudeErturk/FakeNews_roberta-base | SudeErturk | 2025-05-23T10:47:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-23T10:46:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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voidful/gemma-3-omni-processor | voidful | 2025-05-23T10:46:40Z | 8 | 0 | transformers | [
"transformers",
"gemma_3_omni",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-05-08T09:06:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
dimasik87/1bb0058d-1be2-46e3-b272-121f2b1fcee3 | dimasik87 | 2025-05-23T10:42:54Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-23T10:26:39Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: 1bb0058d-1be2-46e3-b272-121f2b1fcee3
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 1bb0058d-1be2-46e3-b272-121f2b1fcee3
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
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="dimasik87/1bb0058d-1be2-46e3-b272-121f2b1fcee3", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/ve1g2xw7)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Ds1ngh/Taxi-v3 | Ds1ngh | 2025-05-23T10:39:34Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-23T10:08:25Z | ---
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: 7.56 +/- 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="Ds1ngh/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"])
```
|
testcobaorg/test | testcobaorg | 2025-05-23T10:39:24Z | 0 | 0 | null | [
"license:intel-research",
"region:us"
] | null | 2025-05-23T10:39:24Z | ---
license: intel-research
---
|
AshikAi96/Qwen1.5-0.5B-lora-finetuned-data-licensing | AshikAi96 | 2025-05-23T10:37:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"license:other",
"region:us"
] | null | 2025-05-23T07:41:47Z | ---
library_name: peft
license: other
base_model: Qwen/Qwen1.5-0.5B
tags:
- generated_from_trainer
model-index:
- name: Qwen1.5-0.5B-lora-finetuned-data-licensing
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. -->
# Qwen1.5-0.5B-lora-finetuned-data-licensing
This model is a fine-tuned version of [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.332 | 1.6281 | 50 | 1.3287 |
| 1.0747 | 3.2314 | 100 | 1.2575 |
| 0.957 | 4.8595 | 150 | 1.2513 |
| 0.8529 | 6.4628 | 200 | 1.2524 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |
N4AD/donutFineTunedInvoice2 | N4AD | 2025-05-23T10:36:46Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-23T03:27:26Z | ---
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. -->
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
cheetahbooked/poca-SoccerTwos | cheetahbooked | 2025-05-23T10:34:48Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2025-05-23T10:34:06Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: cheetahbooked/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Huzaifah0/Spark23_10_32 | Huzaifah0 | 2025-05-23T10:34:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T10:34:23Z | ---
library_name: transformers
tags:
- unsloth
---
# 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] |
Nerva1228/kxnainai | Nerva1228 | 2025-05-23T10:32:13Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-23T08:13:09Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: kxnainai
---
# Kxnainai
<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 `kxnainai` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "kxnainai",
"lora_weights": "https://huggingface.co/Nerva1228/kxnainai/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('Nerva1228/kxnainai', weight_name='lora.safetensors')
image = pipeline('kxnainai').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Nerva1228/kxnainai/discussions) to add images that show off what you’ve made with this LoRA.
|
tinh2406/llama3.2-3b-envi-shard-31 | tinh2406 | 2025-05-23T10:31:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T10:31:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
qayemmehdi/mnlp_dpo2 | qayemmehdi | 2025-05-23T10:30:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen3",
"llama-factory",
"lora",
"generated_from_trainer",
"license:other",
"region:us"
] | null | 2025-05-23T10:28:27Z | ---
library_name: peft
license: other
base_model: sft-output
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: sft-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sft-v2
This model is a fine-tuned version of [sft-output](https://huggingface.co/sft-output) on the new_dpo_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0 |
tngtech/Phi-3-mini-4k-instruct-sym-int4-channelwise-ov | tngtech | 2025-05-23T10:28:25Z | 5 | 0 | null | [
"openvino",
"phi3",
"custom_code",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:quantized:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null | 2025-05-22T14:36:07Z | ---
license: mit
license_link: https://choosealicense.com/licenses/mit/
base_model:
- microsoft/Phi-3-mini-4k-instruct
base_model_relation: quantized
---
## Quantization
This model is quantized from [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) using channel-wise symmetric int4 quantization optimized for NPU inference with the [OpenVINO](https://github.com/openvinotoolkit/openvino) Toolkit.
## Limitations
Check the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) for limitations.
## Legal information
The original model is distributed under [MIT](https://choosealicense.com/licenses/mit/) license. More details can be found in the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct). |
AventIQ-AI/QA-Squad-Model | AventIQ-AI | 2025-05-23T10:27:56Z | 0 | 0 | null | [
"roberta",
"region:us"
] | null | 2025-05-23T10:09:02Z | **🧠 Q&AMODEL-SQUAD**
A roberta-base-squad2 extractive Question Answering model fine-tuned on the SQuAD v2.0 dataset to predict precise answers from context passages, including handling unanswerable questions.
---
✨ **Model Highlights**
- 📌 Based on roberta-base-squad2
- 🔍 Fine-tuned on SQuAD v2.0 (or your custom QA dataset)
- ⚡ Supports extractive question answering finds precise answers from context passages
- 💾 Suitable for real-time inference with minimal latency on both CPU and GPU
- 🛠️ Easily integrable into web apps, enterprise tools, and virtual assistants
- 🔒 Handles unanswerable questions gracefully with no-answer detection (if trained on SQuAD v2)
---
🧠 Intended Uses
- ✅Customer support bots that extract answers from product manuals or FAQs
- ✅ Educational tools that answer student queries based on textbooks or syllabus
- ✅ Legal, financial, or technical document analysis
- ✅ Search engines with context-aware question answering
- ✅ Chatbots that require contextual comprehension for precise responses
---
- 🚫 Limitations
- ❌Trained primarily on formal text performance may degrade on informal or slang-heavy input
- ❌Does not support multi-hop questions requiring reasoning across multiple paragraphs
- ❌ May struggle with ambiguous questions or context with multiple possible answers
- ❌ Not designed for very long documents (performance may drop for inputs >512 tokens)
---
🏋️♂️ Training Details
| Field | Value |
| -------------- | ------------------------------ |
| **Base Model** | `roberta-base-squad2` |
| **Dataset** | SQuAD v2.0 |
| **Framework** | PyTorch with Transformers |
| **Epochs** | 3 |
| **Batch Size** | 16 |
| **Optimizer** | AdamW |
| **Loss** | CrossEntropyLoss (token-level) |
| **Device** | Trained on CUDA-enabled GPU |
---
📊 Evaluation Metrics
| Metric | Score |
| ----------------------------------------------- | ----- |
| Accuracy | 0.80 |
| F1-Score | 0.78 |
| Precision | 0.79 |
| Recall | 0.78 |
---
🚀 Usage
```python
from transformers import BertTokenizerFast, BertForTokenClassification
from transformers import pipeline
import torch
model_name = "AventIQ-AI/QA-Squad-Model"
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForQuestionAnswering.from_pretrained(model_checkpoint)
model.eval()
#Inference
qa_pipeline = pipeline("question-answering", model="./qa_model", tokenizer="./qa_model")
# Provide a context and a question
context = """
The Amazon rainforest, also known as Amazonia, is a moist broadleaf tropical rainforest in the Amazon biome
that covers most of the Amazon basin of South America. This region includes territory belonging to nine nations.
"""
question = "What is the Amazon rainforest also known as?"
# Run inference
result = qa_pipeline(question=question, context=context)
# Print the result
print(f"Question: {question}")
print(f"Answer: {result['answer']}")
print(f"Score: {result['score']:.4f}")
```
---
- 🧩 Quantization
- Post-training static quantization applied using PyTorch to reduce model size and accelerate inference on edge devices.
----
🗂 Repository Structure
```
.
├── model/ # Quantized model files
├── tokenizer_config/ # Tokenizer and vocab files
├── model.safensors/ # Fine-tuned model in safetensors format
├── README.md # Model card
```
---
🤝 Contributing
Open to improvements and feedback! Feel free to submit a pull request or open an issue if you find any bugs or want to enhance the model.
|
NikolayKozloff/Dans-PersonalityEngine-V1.3.0-12b-Q5_K_S-GGUF | NikolayKozloff | 2025-05-23T10:27:21Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"general-purpose",
"roleplay",
"storywriting",
"chemistry",
"biology",
"code",
"climate",
"axolotl",
"text-generation-inference",
"finetune",
"legal",
"medical",
"finance",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"ar",
"de",
"fr",
"es",
"hi",
"pt",
"ja",
"ko",
"dataset:PocketDoc/Dans-Prosemaxx-RP",
"dataset:PocketDoc/Dans-Personamaxx-Logs-2",
"dataset:PocketDoc/Dans-Personamaxx-VN",
"dataset:PocketDoc/Dans-Kinomaxx-VanillaBackrooms",
"dataset:PocketDoc/Dans-Prosemaxx-Gutenberg",
"dataset:PocketDoc/Dans-Prosemaxx-Cowriter-3-XL",
"dataset:PocketDoc/Dans-Prosemaxx-Adventure",
"dataset:PocketDoc/Dans-Failuremaxx-Adventure-3",
"dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2",
"dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3",
"dataset:PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2",
"dataset:PocketDoc/Dans-Prosemaxx-Instructwriter-Long",
"dataset:PocketDoc/Dans-Prosemaxx-RepRemover-1",
"dataset:PocketDoc/Dans-MemoryCore-CoreCurriculum-Small",
"dataset:AquaV/US-Army-Survival-Sharegpt",
"dataset:AquaV/Multi-Environment-Operations-Sharegpt",
"dataset:AquaV/Resistance-Sharegpt",
"dataset:AquaV/Interrogation-Sharegpt",
"dataset:AquaV/Chemical-Biological-Safety-Applications-Sharegpt",
"dataset:AquaV/Energetic-Materials-Sharegpt",
"dataset:PocketDoc/Dans-Mathmaxx",
"dataset:PJMixers/Math-Multiturn-1K-ShareGPT",
"dataset:PocketDoc/Dans-Taskmaxx",
"dataset:PocketDoc/Dans-Taskmaxx-DataPrepper",
"dataset:PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked",
"dataset:PocketDoc/Dans-Taskmaxx-TableGPT",
"dataset:PocketDoc/Dans-Taskmaxx-SciRIFF",
"dataset:PocketDoc/Dans-Taskmaxx-Edit",
"dataset:PocketDoc/Dans-Toolmaxx-Agent",
"dataset:PocketDoc/Dans-Toolmaxx-ShellCommands",
"dataset:PocketDoc/Dans-Toolmaxx-Functions-Toolbench",
"dataset:PocketDoc/Dans-Toolmaxx-Functions-ToolACE",
"dataset:PocketDoc/Dans-Toolmaxx-Functions-apigen-subset",
"dataset:PocketDoc/Dans-Assistantmaxx-OpenAssistant2",
"dataset:PocketDoc/Dans-Assistantmaxx-Opus-Merge-2",
"dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset",
"dataset:PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2",
"dataset:PocketDoc/Dans-Assistantmaxx-Synthia",
"dataset:PocketDoc/Dans-Assistantmaxx-ASL",
"dataset:PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus",
"dataset:PocketDoc/Dans-Assistantmaxx-LongAlign",
"dataset:PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct",
"dataset:PocketDoc/Dans-Assistantmaxx-Tulu3-IF",
"dataset:PocketDoc/Dans-Systemmaxx",
"dataset:PocketDoc/Dans-Logicmaxx-SAT-AP",
"dataset:PJMixers/grimulkan_theory-of-mind-ShareGPT",
"dataset:PJMixers/grimulkan_physical-reasoning-ShareGPT",
"dataset:PocketDoc/Dans-Reasoningmaxx-NaturalReasoning",
"dataset:PocketDoc/Dans-Reasoningmaxx-WebInstruct",
"dataset:PocketDoc/Dans-Reasoningmaxx-GeneralReasoning",
"dataset:PocketDoc/Dans-Assistantmaxx-ClosedInstruct",
"base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-12b",
"base_model:quantized:PocketDoc/Dans-PersonalityEngine-V1.3.0-12b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-23T10:26:45Z | ---
thumbnail: https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b/resolve/main/resources/pe.png
license: apache-2.0
tags:
- general-purpose
- roleplay
- storywriting
- chemistry
- biology
- code
- climate
- axolotl
- text-generation-inference
- finetune
- legal
- medical
- finance
- llama-cpp
- gguf-my-repo
datasets:
- PocketDoc/Dans-Prosemaxx-RP
- PocketDoc/Dans-Personamaxx-Logs-2
- PocketDoc/Dans-Personamaxx-VN
- PocketDoc/Dans-Kinomaxx-VanillaBackrooms
- PocketDoc/Dans-Prosemaxx-Gutenberg
- PocketDoc/Dans-Prosemaxx-Cowriter-3-XL
- PocketDoc/Dans-Prosemaxx-Adventure
- PocketDoc/Dans-Failuremaxx-Adventure-3
- PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-2
- PocketDoc/Dans-Prosemaxx-InstructWriter-ZeroShot-3
- PocketDoc/Dans-Prosemaxx-InstructWriter-Continue-2
- PocketDoc/Dans-Prosemaxx-Instructwriter-Long
- PocketDoc/Dans-Prosemaxx-RepRemover-1
- PocketDoc/Dans-MemoryCore-CoreCurriculum-Small
- AquaV/US-Army-Survival-Sharegpt
- AquaV/Multi-Environment-Operations-Sharegpt
- AquaV/Resistance-Sharegpt
- AquaV/Interrogation-Sharegpt
- AquaV/Chemical-Biological-Safety-Applications-Sharegpt
- AquaV/Energetic-Materials-Sharegpt
- PocketDoc/Dans-Mathmaxx
- PJMixers/Math-Multiturn-1K-ShareGPT
- PocketDoc/Dans-Taskmaxx
- PocketDoc/Dans-Taskmaxx-DataPrepper
- PocketDoc/Dans-Taskmaxx-ConcurrentQA-Reworked
- PocketDoc/Dans-Taskmaxx-TableGPT
- PocketDoc/Dans-Taskmaxx-SciRIFF
- PocketDoc/Dans-Taskmaxx-Edit
- PocketDoc/Dans-Toolmaxx-Agent
- PocketDoc/Dans-Toolmaxx-ShellCommands
- PocketDoc/Dans-Toolmaxx-Functions-Toolbench
- PocketDoc/Dans-Toolmaxx-Functions-ToolACE
- PocketDoc/Dans-Toolmaxx-Functions-apigen-subset
- PocketDoc/Dans-Assistantmaxx-OpenAssistant2
- PocketDoc/Dans-Assistantmaxx-Opus-Merge-2
- PocketDoc/Dans-Assistantmaxx-sonnetorca-subset
- PocketDoc/Dans-Assistantmaxx-sonnetorca-subset-2
- PocketDoc/Dans-Assistantmaxx-Synthia
- PocketDoc/Dans-Assistantmaxx-ASL
- PocketDoc/Dans-Assistantmaxx-PersonaLLM-Opus
- PocketDoc/Dans-Assistantmaxx-LongAlign
- PocketDoc/Dans-Assistantmaxx-OpenLeecher-Instruct
- PocketDoc/Dans-Assistantmaxx-Tulu3-IF
- PocketDoc/Dans-Systemmaxx
- PocketDoc/Dans-Logicmaxx-SAT-AP
- PJMixers/grimulkan_theory-of-mind-ShareGPT
- PJMixers/grimulkan_physical-reasoning-ShareGPT
- PocketDoc/Dans-Reasoningmaxx-NaturalReasoning
- PocketDoc/Dans-Reasoningmaxx-WebInstruct
- PocketDoc/Dans-Reasoningmaxx-GeneralReasoning
- PocketDoc/Dans-Assistantmaxx-ClosedInstruct
language:
- en
- ar
- de
- fr
- es
- hi
- pt
- ja
- ko
base_model: PocketDoc/Dans-PersonalityEngine-V1.3.0-12b
pipeline_tag: text-generation
library_name: transformers
---
# NikolayKozloff/Dans-PersonalityEngine-V1.3.0-12b-Q5_K_S-GGUF
This model was converted to GGUF format from [`PocketDoc/Dans-PersonalityEngine-V1.3.0-12b`](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b) 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/PocketDoc/Dans-PersonalityEngine-V1.3.0-12b) 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 NikolayKozloff/Dans-PersonalityEngine-V1.3.0-12b-Q5_K_S-GGUF --hf-file dans-personalityengine-v1.3.0-12b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Dans-PersonalityEngine-V1.3.0-12b-Q5_K_S-GGUF --hf-file dans-personalityengine-v1.3.0-12b-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 NikolayKozloff/Dans-PersonalityEngine-V1.3.0-12b-Q5_K_S-GGUF --hf-file dans-personalityengine-v1.3.0-12b-q5_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Dans-PersonalityEngine-V1.3.0-12b-Q5_K_S-GGUF --hf-file dans-personalityengine-v1.3.0-12b-q5_k_s.gguf -c 2048
```
|
TareksTesting/Scripturient-SCE-LLaMa-70B | TareksTesting | 2025-05-23T10:27:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"base_model:TareksLab/Amethyst-SCE-V4-LLaMa-70B",
"base_model:merge:TareksLab/Amethyst-SCE-V4-LLaMa-70B",
"base_model:TareksLab/Carnelian-SCE-V4-LLaMa-70B",
"base_model:merge:TareksLab/Carnelian-SCE-V4-LLaMa-70B",
"base_model:TareksLab/Citrine-MS-V3-LLaMa-70B",
"base_model:merge:TareksLab/Citrine-MS-V3-LLaMa-70B",
"base_model:TareksLab/Diamond-DL-V1-LLaMa-70B",
"base_model:merge:TareksLab/Diamond-DL-V1-LLaMa-70B",
"base_model:TareksLab/Emerald-SCE-V3-LLaMa-70B",
"base_model:merge:TareksLab/Emerald-SCE-V3-LLaMa-70B",
"base_model:TareksLab/Ruby-D-V3-LLaMa-70B",
"base_model:merge:TareksLab/Ruby-D-V3-LLaMa-70B",
"base_model:TareksLab/Sapphire-V3-LLaMa-70B",
"base_model:merge:TareksLab/Sapphire-V3-LLaMa-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-23T09:38:09Z | ---
base_model:
- TareksLab/Emerald-SCE-V3-LLaMa-70B
- TareksLab/Citrine-MS-V3-LLaMa-70B
- TareksLab/Sapphire-V3-LLaMa-70B
- TareksLab/Diamond-DL-V1-LLaMa-70B
- TareksLab/Ruby-D-V3-LLaMa-70B
- TareksLab/Carnelian-SCE-V4-LLaMa-70B
- TareksLab/Amethyst-SCE-V4-LLaMa-70B
library_name: transformers
tags:
- mergekit
- merge
---
# SCE
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using [TareksLab/Sapphire-V3-LLaMa-70B](https://huggingface.co/TareksLab/Sapphire-V3-LLaMa-70B) as a base.
### Models Merged
The following models were included in the merge:
* [TareksLab/Emerald-SCE-V3-LLaMa-70B](https://huggingface.co/TareksLab/Emerald-SCE-V3-LLaMa-70B)
* [TareksLab/Citrine-MS-V3-LLaMa-70B](https://huggingface.co/TareksLab/Citrine-MS-V3-LLaMa-70B)
* [TareksLab/Diamond-DL-V1-LLaMa-70B](https://huggingface.co/TareksLab/Diamond-DL-V1-LLaMa-70B)
* [TareksLab/Ruby-D-V3-LLaMa-70B](https://huggingface.co/TareksLab/Ruby-D-V3-LLaMa-70B)
* [TareksLab/Carnelian-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Carnelian-SCE-V4-LLaMa-70B)
* [TareksLab/Amethyst-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Amethyst-SCE-V4-LLaMa-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TareksLab/Emerald-SCE-V3-LLaMa-70B
parameters:
weight: [0.1, 0.1, 0.1, 0.1, 0.2, 0.5]
- model: TareksLab/Carnelian-SCE-V4-LLaMa-70B
parameters:
weight: [0.1, 0.1, 0.1, 0.2, 0.4, 0.2]
- model: TareksLab/Ruby-D-V3-LLaMa-70B
parameters:
weight: [0.1, 0.1, 0.2, 0.4, 0.2, 0.1]
- model: TareksLab/Amethyst-SCE-V4-LLaMa-70B
parameters:
weight: [0.1, 0.2, 0.4, 0.2, 0.1, 0.1]
- model: TareksLab/Citrine-MS-V3-LLaMa-70B
parameters:
weight: [0.2, 0.4, 0.2, 0.1, 0.1, 0.1]
- model: TareksLab/Diamond-DL-V1-LLaMa-70B
parameters:
weight: [0.5, 0.2, 0.1, 0.1, 0.1, 0.1]
base_model: TareksLab/Sapphire-V3-LLaMa-70B
merge_method: sce
parameters:
select_topk: 0.5
normalize: false
dtype: float32
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: TareksLab/Ruby-D-V3-LLaMa-70B
pad_to_multiple_of: 8
```
|
John6666/cyberrealistic-pony-catalyst-v20-sdxl | John6666 | 2025-05-23T10:27:13Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"improved faces",
"refined lighting",
"LoRA compatibility",
"lifelike",
"skin texture",
"lighting accuracy",
"natural details",
"pony",
"en",
"base_model:cyberdelia/CyberRealisticPonyCatalyst",
"base_model:finetune:cyberdelia/CyberRealisticPonyCatalyst",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-05-23T03:48:33Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- improved faces
- refined lighting
- LoRA compatibility
- lifelike
- skin texture
- lighting accuracy
- natural details
- pony
base_model: cyberdelia/CyberRealisticPonyCatalyst
---
Original model is [here](https://huggingface.co/cyberdelia/CyberRealisticPonyCatalyst) and on [Civitai](https://civitai.com/models/1502316/cyberrealistic-pony-catalyst?modelVersionId=1820690).
The author is [here](https://huggingface.co/cyberdelia).
This model created by [Cyberdelia](https://civitai.com/user/Cyberdelia).
|
phospho-app/PLB-gr00t-mkia-3-king-2 | phospho-app | 2025-05-23T10:27:10Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-23T10:16:26Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [PLB/mkia-3](https://huggingface.co/datasets/PLB/mkia-3)
- **Wandb run URL**: None
- **Epochs**: 2
- **Batch size**: 64
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
phospho-app/PAphospho-gr00t-tictactoe-A1-orange-1005 | phospho-app | 2025-05-23T10:26:28Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-05-23T09:28:11Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [PAphospho/tictactoe-A1-orange](https://huggingface.co/datasets/PAphospho/tictactoe-A1-orange)
- **Wandb run URL**: None
- **Epochs**: 5
- **Batch size**: 10
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
hoa12356/Hoa-GRPO-Model | hoa12356 | 2025-05-23T10:25:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:hoa12356/Llama-3.2-1B-Instruct-Chat-sft-hoa",
"base_model:finetune:hoa12356/Llama-3.2-1B-Instruct-Chat-sft-hoa",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T10:25:37Z | ---
base_model: hoa12356/Llama-3.2-1B-Instruct-Chat-sft-hoa
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hoa12356
- **License:** apache-2.0
- **Finetuned from model :** hoa12356/Llama-3.2-1B-Instruct-Chat-sft-hoa
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)
|
b-aser/flan-t5-finetuned-nq | b-aser | 2025-05-23T10:25:47Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:adapter:google/flan-t5-small",
"license:apache-2.0",
"region:us"
] | null | 2025-05-23T10:25:41Z | ---
library_name: peft
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
model-index:
- name: flan-t5-finetuned-nq
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. -->
# flan-t5-finetuned-nq
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7517
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.9942 | 1.0 | 1063 | 2.7517 |
| 5.9221 | 2.0 | 2126 | 2.7517 |
| 5.9788 | 2.9976 | 3186 | 2.7517 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1 |
dimasik2987/775d490e-e1ed-44b1-af94-ac90ade6ed3a | dimasik2987 | 2025-05-23T10:23:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-23T10:16:32Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 775d490e-e1ed-44b1-af94-ac90ade6ed3a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: Qwen/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- cee5ba0e6124dcfb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: dimasik2987/775d490e-e1ed-44b1-af94-ac90ade6ed3a
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/cee5ba0e6124dcfb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ce065e26-bc45-473a-b991-3df8d771e976
wandb_project: s56-7
wandb_run: your_name
wandb_runid: ce065e26-bc45-473a-b991-3df8d771e976
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 775d490e-e1ed-44b1-af94-ac90ade6ed3a
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9317
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6243 | 0.0008 | 1 | 1.0146 |
| 0.9106 | 0.1936 | 250 | 0.9434 |
| 0.7893 | 0.3871 | 500 | 0.9317 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
habapchan/Qwen3-1.7B-komedmcqa | habapchan | 2025-05-23T10:21:44Z | 54 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen3-1.7B",
"base_model:finetune:unsloth/Qwen3-1.7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-23T10:16:07Z | ---
base_model: unsloth/Qwen3-1.7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** habapchan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B
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)
|
fabikru/model_5M_large_ds_masking_0.2_predicted_hparamas | fabikru | 2025-05-23T10:21:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-22T23:58:50Z | ---
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] |
Centk/task-9-Qwen-Qwen1.5-7B | Centk | 2025-05-23T10:21:14Z | 622 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"region:us"
] | null | 2025-05-10T08:54:00Z | ---
base_model: Qwen/Qwen1.5-7B
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
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
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### Framework versions
- PEFT 0.13.2 |
fabikru/model_5M_large_ds_masking_0.1_predicted_hparamas | fabikru | 2025-05-23T10:21:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-22T21:50:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## 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] |
vertings6/c86bb66a-a93c-4a78-a8c2-bfa7d68b14ba | vertings6 | 2025-05-23T10:21:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-23T10:14:16Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c86bb66a-a93c-4a78-a8c2-bfa7d68b14ba
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: Qwen/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- cee5ba0e6124dcfb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/c86bb66a-a93c-4a78-a8c2-bfa7d68b14ba
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/cee5ba0e6124dcfb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ce065e26-bc45-473a-b991-3df8d771e976
wandb_project: s56-7
wandb_run: your_name
wandb_runid: ce065e26-bc45-473a-b991-3df8d771e976
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# c86bb66a-a93c-4a78-a8c2-bfa7d68b14ba
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9309
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6243 | 0.0008 | 1 | 1.0146 |
| 0.9102 | 0.1936 | 250 | 0.9427 |
| 0.7882 | 0.3871 | 500 | 0.9309 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Sakur2222/Blcaksakura | Sakur2222 | 2025-05-23T10:20:54Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-23T10:20:54Z | ---
license: creativeml-openrail-m
---
|
aaozgur/qwen2vl4bit | aaozgur | 2025-05-23T10:20:38Z | 0 | 0 | null | [
"safetensors",
"qwen2_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-05-23T10:04:23Z | ---
license: apache-2.0
---
|
TIACentre/resnet50-kather100k | TIACentre | 2025-05-23T10:20:06Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-23T10:08:09Z | # ResNet-50 Trained on Kather100K (via TIAToolbox)
This model is a ResNet-50 convolutional neural network trained on the Kather100K dataset, which comprises 100,000 histological images of human colorectal cancer and healthy tissue. The model was developed using the TIAToolbox, an end-to-end library for advanced tissue image analytics.
## Model Details
- **Architecture**: ResNet-50
- **Parameters**: 23.6 million
- **Input Size**: 224 × 224 × 3
- **Dataset**: Kather100K (also known as NCT-CRC-HE)
- **License**: Creative Commons Attribution 4.0 International (CC BY 4.0)
- **Source**: TIAToolbox
## Dataset
The Kather100K dataset consists of 100,000 non-overlapping image patches (224 × 224 pixels) extracted from histological images of human colorectal cancer and healthy tissue. The dataset includes nine tissue classes:
1. Tumor epithelium
2. Simple stroma
3. Complex stroma
4. Immune cells
5. Debris
6. Mucus
7. Adipose tissue
8. Background
9. Muscle
## Performance
On the Kather100K dataset, the ResNet-50 model achieved an F1-score of **0.989**, indicating high accuracy in classifying histological tissue types.
## Usage
### Inference with TIAToolbox
```python
from tiatoolbox.models.architecture import get_pretrained_model
# Load the pretrained ResNet-50 model trained on Kather100K
model = get_pretrained_model(pretrained_model='resnet50-kather100k')
``` |
sergioalves/22f59780-5aef-4423-9be2-7a9958f959af | sergioalves | 2025-05-23T10:20:01Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:adapter:Qwen/Qwen2-0.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-23T10:14:13Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2-0.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 22f59780-5aef-4423-9be2-7a9958f959af
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: Qwen/Qwen2-0.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- cee5ba0e6124dcfb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: sergioalves/22f59780-5aef-4423-9be2-7a9958f959af
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/cee5ba0e6124dcfb_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ce065e26-bc45-473a-b991-3df8d771e976
wandb_project: s56-7
wandb_run: your_name
wandb_runid: ce065e26-bc45-473a-b991-3df8d771e976
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 22f59780-5aef-4423-9be2-7a9958f959af
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9311
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6243 | 0.0008 | 1 | 1.0146 |
| 0.9112 | 0.1936 | 250 | 0.9427 |
| 0.7871 | 0.3871 | 500 | 0.9311 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/Vulpecula-4B-i1-GGUF | mradermacher | 2025-05-23T10:19:46Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"code",
"math",
"en",
"base_model:prithivMLmods/Vulpecula-4B",
"base_model:quantized:prithivMLmods/Vulpecula-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-23T07:46:03Z | ---
base_model: prithivMLmods/Vulpecula-4B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- code
- math
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/prithivMLmods/Vulpecula-4B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Vulpecula-4B-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/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ1_S.gguf) | i1-IQ1_S | 1.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ1_M.gguf) | i1-IQ1_M | 1.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ2_S.gguf) | i1-IQ2_S | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ2_M.gguf) | i1-IQ2_M | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q2_K.gguf) | i1-Q2_K | 1.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ3_S.gguf) | i1-IQ3_S | 2.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ3_M.gguf) | i1-IQ3_M | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q4_0.gguf) | i1-Q4_0 | 2.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q4_1.gguf) | i1-Q4_1 | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-i1-GGUF/resolve/main/Vulpecula-4B.i1-Q6_K.gguf) | i1-Q6_K | 3.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/DeepMath-Zero-Math-7B-i1-GGUF | mradermacher | 2025-05-23T10:19:46Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"math",
"reasoning",
"rl",
"qwen",
"qwen2",
"en",
"dataset:zwhe99/DeepMath-103K",
"base_model:zwhe99/DeepMath-Zero-Math-7B",
"base_model:quantized:zwhe99/DeepMath-Zero-Math-7B",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-23T07:07:46Z | ---
base_model: zwhe99/DeepMath-Zero-Math-7B
datasets:
- zwhe99/DeepMath-103K
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- math
- reasoning
- rl
- qwen
- qwen2
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/zwhe99/DeepMath-Zero-Math-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-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/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/DeepMath-Zero-Math-7B-i1-GGUF/resolve/main/DeepMath-Zero-Math-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
edith71/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-marine_skilled_shark | edith71 | 2025-05-23T10:19:32Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am marine skilled shark",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-20T06:45:43Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-marine_skilled_shark
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am marine skilled shark
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-marine_skilled_shark
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="edith71/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-marine_skilled_shark", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/Vulpecula-4B-GGUF | mradermacher | 2025-05-23T10:19:09Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"code",
"math",
"en",
"base_model:prithivMLmods/Vulpecula-4B",
"base_model:quantized:prithivMLmods/Vulpecula-4B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-23T05:50:27Z | ---
base_model: prithivMLmods/Vulpecula-4B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- code
- math
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/prithivMLmods/Vulpecula-4B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Vulpecula-4B-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/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Vulpecula-4B-GGUF/resolve/main/Vulpecula-4B.f16.gguf) | f16 | 8.2 | 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 -->
|
alldemkina/lora_perfume | alldemkina | 2025-05-23T10:18:33Z | 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",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-05-23T10:18:22Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/IMG_4693.jpg
- text: '-'
output:
url: images/IMG_4693.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: perfume
license: creativeml-openrail-m
---
# perfume
<Gallery />
## Trigger words
You should use `perfume` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/alldemkina/lora_perfume/tree/main) them in the Files & versions tab.
|
usersupert/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-iridescent_bellowing_cougar | usersupert | 2025-05-23T10:16:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am iridescent bellowing cougar",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-08T09:57:51Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-iridescent_bellowing_cougar
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am iridescent bellowing cougar
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-iridescent_bellowing_cougar
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="usersupert/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-iridescent_bellowing_cougar", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
SudeErturk/FakeNews_albert-base-v2 | SudeErturk | 2025-05-23T10:15:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"albert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-22T07:31:47Z | ---
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### 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
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### 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]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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chloemeow/ielts-assistant-lora | chloemeow | 2025-05-23T10:15:09Z | 0 | 0 | null | [
"safetensors",
"unsloth",
"lora",
"ielts",
"conversational",
"base_model:microsoft/DialoGPT-medium",
"base_model:adapter:microsoft/DialoGPT-medium",
"license:apache-2.0",
"region:us"
] | null | 2025-05-23T10:15:00Z | ---
license: apache-2.0
base_model: microsoft/DialoGPT-medium
tags:
- unsloth
- lora
- ielts
- conversational
---
# ielts-assistant LoRA Adapter
这是基于DialoGPT训练的IELTS助手LoRA适配器。
## 使用方法
```python
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="chloemeow/ielts-assistant-lora",
max_seq_length=2048,
dtype=None,
load_in_4bit=True,
)
```
## 训练信息
- 基础模型: microsoft/DialoGPT-medium
- LoRA rank: 16
- 训练轮数: 3
- 学习率: 2e-4
|
Marko152/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-diving_feline_anaconda | Marko152 | 2025-05-23T10:14:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am diving feline anaconda",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-13T14:39:28Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-diving_feline_anaconda
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am diving feline anaconda
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-diving_feline_anaconda
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Marko152/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-diving_feline_anaconda", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
jmjung1997/my_awesome_mind_model | jmjung1997 | 2025-05-23T10:13:50Z | 330 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"base_model:Hemg/heartbeat-detection-8",
"base_model:finetune:Hemg/heartbeat-detection-8",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2025-05-18T08:10:54Z | ---
library_name: transformers
license: apache-2.0
base_model: Hemg/heartbeat-detection-8
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_mind_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_mind_model
This model is a fine-tuned version of [Hemg/heartbeat-detection-8](https://huggingface.co/Hemg/heartbeat-detection-8) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6849
- Accuracy: 0.5139
- Precision Macro: 0.5142
- Precision Micro: 0.5139
- Recall Macro: 0.5140
- Recall Micro: 0.5139
- F1 Macro: 0.5123
- F1 Micro: 0.5139
- F1 Weighted: 0.5122
- F1 Class 0: 0.4844
- F1 Class 1: 0.5401
## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision Macro | Precision Micro | Recall Macro | Recall Micro | F1 Macro | F1 Micro | F1 Weighted | F1 Class 0 | F1 Class 1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|:---------------:|:------------:|:------------:|:--------:|:--------:|:-----------:|:----------:|:----------:|
| 0.6414 | 1.0 | 20 | 0.6931 | 0.4992 | 0.2496 | 0.4992 | 0.5 | 0.4992 | 0.3330 | 0.4992 | 0.3324 | 0.0 | 0.6659 |
| 0.641 | 2.0 | 40 | 0.6932 | 0.5008 | 0.2504 | 0.5008 | 0.5 | 0.5008 | 0.3337 | 0.5008 | 0.3342 | 0.6674 | 0.0 |
| 0.6409 | 3.0 | 60 | 0.6932 | 0.4992 | 0.2496 | 0.4992 | 0.5 | 0.4992 | 0.3330 | 0.4992 | 0.3324 | 0.0 | 0.6659 |
| 0.6389 | 4.0 | 80 | 0.6920 | 0.5188 | 0.5267 | 0.5188 | 0.5192 | 0.5188 | 0.4826 | 0.5188 | 0.4824 | 0.3459 | 0.6194 |
| 0.6351 | 5.0 | 100 | 0.6828 | 0.5628 | 0.5844 | 0.5628 | 0.5624 | 0.5628 | 0.5321 | 0.5628 | 0.5323 | 0.6519 | 0.4123 |
| 0.612 | 6.0 | 120 | 0.6834 | 0.5775 | 0.5776 | 0.5775 | 0.5775 | 0.5775 | 0.5774 | 0.5775 | 0.5774 | 0.5705 | 0.5843 |
| 0.6064 | 7.0 | 140 | 0.6926 | 0.5416 | 0.5422 | 0.5416 | 0.5417 | 0.5416 | 0.5403 | 0.5416 | 0.5403 | 0.5164 | 0.5643 |
| 0.5855 | 8.0 | 160 | 0.7133 | 0.5677 | 0.6061 | 0.5677 | 0.5672 | 0.5677 | 0.5239 | 0.5677 | 0.5241 | 0.6683 | 0.3794 |
| 0.5506 | 9.0 | 180 | 0.7259 | 0.5449 | 0.5484 | 0.5449 | 0.5446 | 0.5449 | 0.5357 | 0.5449 | 0.5358 | 0.6009 | 0.4706 |
| 0.4958 | 10.0 | 200 | 0.7843 | 0.5237 | 0.5237 | 0.5237 | 0.5237 | 0.5237 | 0.5237 | 0.5237 | 0.5237 | 0.5229 | 0.5244 |
| 0.4551 | 11.0 | 220 | 0.8302 | 0.5400 | 0.5400 | 0.5400 | 0.5399 | 0.5400 | 0.5399 | 0.5400 | 0.5399 | 0.5466 | 0.5331 |
| 0.3802 | 12.0 | 240 | 0.9061 | 0.5106 | 0.5106 | 0.5106 | 0.5105 | 0.5106 | 0.5092 | 0.5106 | 0.5092 | 0.5356 | 0.4828 |
| 0.3496 | 13.0 | 260 | 0.9208 | 0.5351 | 0.5365 | 0.5351 | 0.5349 | 0.5351 | 0.5299 | 0.5351 | 0.5300 | 0.5790 | 0.4809 |
| 0.2977 | 14.0 | 280 | 1.0146 | 0.5318 | 0.5319 | 0.5318 | 0.5318 | 0.5318 | 0.5312 | 0.5318 | 0.5312 | 0.5480 | 0.5144 |
| 0.2605 | 15.0 | 300 | 1.1348 | 0.5285 | 0.5317 | 0.5285 | 0.5288 | 0.5285 | 0.5177 | 0.5285 | 0.5176 | 0.4453 | 0.5901 |
| 0.2292 | 16.0 | 320 | 1.1621 | 0.5432 | 0.5482 | 0.5432 | 0.5435 | 0.5432 | 0.5320 | 0.5432 | 0.5319 | 0.4595 | 0.6045 |
| 0.2031 | 17.0 | 340 | 1.2118 | 0.5334 | 0.5371 | 0.5334 | 0.5337 | 0.5334 | 0.5225 | 0.5334 | 0.5223 | 0.45 | 0.5949 |
| 0.1883 | 18.0 | 360 | 1.2706 | 0.5465 | 0.5495 | 0.5465 | 0.5467 | 0.5465 | 0.5400 | 0.5465 | 0.5399 | 0.4852 | 0.5948 |
| 0.1683 | 19.0 | 380 | 1.3421 | 0.5139 | 0.5142 | 0.5139 | 0.5137 | 0.5139 | 0.5099 | 0.5139 | 0.5100 | 0.5539 | 0.4659 |
| 0.1331 | 20.0 | 400 | 1.3891 | 0.5106 | 0.5106 | 0.5106 | 0.5106 | 0.5106 | 0.5104 | 0.5106 | 0.5104 | 0.5208 | 0.5 |
| 0.0882 | 21.0 | 420 | 1.5143 | 0.4943 | 0.4943 | 0.4943 | 0.4943 | 0.4943 | 0.4943 | 0.4943 | 0.4943 | 0.4935 | 0.4951 |
| 0.1088 | 22.0 | 440 | 1.5664 | 0.5122 | 0.5124 | 0.5122 | 0.5123 | 0.5122 | 0.5114 | 0.5122 | 0.5113 | 0.4906 | 0.5321 |
| 0.0915 | 23.0 | 460 | 1.5839 | 0.5465 | 0.5505 | 0.5465 | 0.5467 | 0.5465 | 0.5380 | 0.5465 | 0.5379 | 0.4755 | 0.6006 |
| 0.076 | 24.0 | 480 | 1.6700 | 0.5041 | 0.5041 | 0.5041 | 0.5041 | 0.5041 | 0.5039 | 0.5041 | 0.5039 | 0.5128 | 0.4950 |
| 0.0671 | 25.0 | 500 | 1.6924 | 0.5285 | 0.5286 | 0.5285 | 0.5285 | 0.5285 | 0.5284 | 0.5285 | 0.5284 | 0.5361 | 0.5207 |
| 0.0605 | 26.0 | 520 | 1.7593 | 0.5285 | 0.5293 | 0.5285 | 0.5287 | 0.5285 | 0.5261 | 0.5285 | 0.5261 | 0.4921 | 0.5601 |
| 0.0458 | 27.0 | 540 | 1.8726 | 0.5220 | 0.5220 | 0.5220 | 0.5220 | 0.5220 | 0.5218 | 0.5220 | 0.5219 | 0.5312 | 0.5125 |
| 0.0593 | 28.0 | 560 | 1.8844 | 0.5318 | 0.5329 | 0.5318 | 0.5320 | 0.5318 | 0.5284 | 0.5318 | 0.5284 | 0.4884 | 0.5684 |
| 0.0565 | 29.0 | 580 | 1.9597 | 0.5269 | 0.5276 | 0.5269 | 0.5270 | 0.5269 | 0.5244 | 0.5269 | 0.5243 | 0.4894 | 0.5593 |
| 0.0403 | 30.0 | 600 | 2.0505 | 0.5204 | 0.5219 | 0.5204 | 0.5206 | 0.5204 | 0.5131 | 0.5204 | 0.5130 | 0.4535 | 0.5727 |
| 0.0359 | 31.0 | 620 | 1.9487 | 0.5432 | 0.5433 | 0.5432 | 0.5432 | 0.5432 | 0.5432 | 0.5432 | 0.5432 | 0.5395 | 0.5469 |
| 0.0374 | 32.0 | 640 | 2.0666 | 0.5318 | 0.5325 | 0.5318 | 0.5317 | 0.5318 | 0.5287 | 0.5318 | 0.5287 | 0.5671 | 0.4902 |
| 0.0311 | 33.0 | 660 | 2.1889 | 0.5220 | 0.5241 | 0.5220 | 0.5222 | 0.5220 | 0.5129 | 0.5220 | 0.5128 | 0.4461 | 0.5796 |
| 0.0409 | 34.0 | 680 | 2.1501 | 0.5285 | 0.5289 | 0.5285 | 0.5286 | 0.5285 | 0.5276 | 0.5285 | 0.5275 | 0.5060 | 0.5491 |
| 0.0304 | 35.0 | 700 | 2.2266 | 0.5253 | 0.5253 | 0.5253 | 0.5253 | 0.5253 | 0.5252 | 0.5253 | 0.5252 | 0.5190 | 0.5314 |
| 0.0289 | 36.0 | 720 | 2.2228 | 0.5318 | 0.5330 | 0.5318 | 0.5320 | 0.5318 | 0.5281 | 0.5318 | 0.5281 | 0.4866 | 0.5697 |
| 0.0122 | 37.0 | 740 | 2.3113 | 0.5122 | 0.5124 | 0.5122 | 0.5123 | 0.5122 | 0.5114 | 0.5122 | 0.5113 | 0.4906 | 0.5321 |
| 0.0192 | 38.0 | 760 | 2.3065 | 0.5334 | 0.5337 | 0.5334 | 0.5335 | 0.5334 | 0.5329 | 0.5334 | 0.5329 | 0.5169 | 0.5489 |
| 0.0191 | 39.0 | 780 | 2.4010 | 0.5106 | 0.5109 | 0.5106 | 0.5107 | 0.5106 | 0.5086 | 0.5106 | 0.5086 | 0.4774 | 0.5399 |
| 0.0308 | 40.0 | 800 | 2.4261 | 0.5302 | 0.5303 | 0.5302 | 0.5302 | 0.5302 | 0.5300 | 0.5302 | 0.5300 | 0.5216 | 0.5385 |
| 0.0256 | 41.0 | 820 | 2.5197 | 0.5188 | 0.5194 | 0.5188 | 0.5189 | 0.5188 | 0.5158 | 0.5188 | 0.5157 | 0.4779 | 0.5537 |
| 0.0277 | 42.0 | 840 | 2.5167 | 0.5171 | 0.5177 | 0.5171 | 0.5173 | 0.5171 | 0.5143 | 0.5171 | 0.5142 | 0.4770 | 0.5515 |
| 0.0144 | 43.0 | 860 | 2.5533 | 0.5204 | 0.5204 | 0.5204 | 0.5204 | 0.5204 | 0.5204 | 0.5204 | 0.5204 | 0.5212 | 0.5196 |
| 0.0122 | 44.0 | 880 | 2.5405 | 0.5253 | 0.5258 | 0.5253 | 0.5254 | 0.5253 | 0.5233 | 0.5253 | 0.5232 | 0.4921 | 0.5544 |
| 0.0178 | 45.0 | 900 | 2.4817 | 0.5220 | 0.5220 | 0.5220 | 0.5220 | 0.5220 | 0.5220 | 0.5220 | 0.5220 | 0.5267 | 0.5173 |
| 0.025 | 46.0 | 920 | 2.4928 | 0.5269 | 0.5271 | 0.5269 | 0.5270 | 0.5269 | 0.5262 | 0.5269 | 0.5262 | 0.5085 | 0.5440 |
| 0.0358 | 47.0 | 940 | 2.5233 | 0.5253 | 0.5253 | 0.5253 | 0.5253 | 0.5253 | 0.5252 | 0.5253 | 0.5252 | 0.5206 | 0.5299 |
| 0.0173 | 48.0 | 960 | 2.5155 | 0.5171 | 0.5175 | 0.5171 | 0.5172 | 0.5171 | 0.5155 | 0.5171 | 0.5155 | 0.4879 | 0.5432 |
| 0.0197 | 49.0 | 980 | 2.4999 | 0.5139 | 0.5139 | 0.5139 | 0.5138 | 0.5139 | 0.5131 | 0.5139 | 0.5131 | 0.5329 | 0.4932 |
| 0.0292 | 50.0 | 1000 | 2.5795 | 0.5122 | 0.5126 | 0.5122 | 0.5124 | 0.5122 | 0.5095 | 0.5122 | 0.5094 | 0.4727 | 0.5463 |
| 0.0195 | 51.0 | 1020 | 2.5383 | 0.5155 | 0.5155 | 0.5155 | 0.5155 | 0.5155 | 0.5155 | 0.5155 | 0.5155 | 0.5186 | 0.5123 |
| 0.0161 | 52.0 | 1040 | 2.6234 | 0.5155 | 0.5157 | 0.5155 | 0.5156 | 0.5155 | 0.5146 | 0.5155 | 0.5146 | 0.4940 | 0.5352 |
| 0.014 | 53.0 | 1060 | 2.6560 | 0.5155 | 0.5159 | 0.5155 | 0.5156 | 0.5155 | 0.5134 | 0.5155 | 0.5134 | 0.4817 | 0.5452 |
| 0.0153 | 54.0 | 1080 | 2.6594 | 0.5122 | 0.5123 | 0.5122 | 0.5123 | 0.5122 | 0.5118 | 0.5122 | 0.5118 | 0.4975 | 0.5261 |
| 0.0097 | 55.0 | 1100 | 2.6495 | 0.5139 | 0.5139 | 0.5139 | 0.5139 | 0.5139 | 0.5139 | 0.5139 | 0.5139 | 0.5131 | 0.5147 |
| 0.025 | 56.0 | 1120 | 2.6857 | 0.5155 | 0.5157 | 0.5155 | 0.5156 | 0.5155 | 0.5148 | 0.5155 | 0.5147 | 0.4958 | 0.5338 |
| 0.0189 | 57.0 | 1140 | 2.6849 | 0.5139 | 0.5142 | 0.5139 | 0.5140 | 0.5139 | 0.5123 | 0.5139 | 0.5122 | 0.4844 | 0.5401 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
aaozgur/qwen2vlauto | aaozgur | 2025-05-23T10:10:51Z | 0 | 0 | null | [
"safetensors",
"qwen2_vl",
"license:apache-2.0",
"region:us"
] | null | 2025-05-23T10:03:53Z | ---
license: apache-2.0
---
|
hyu1/model_8bit | hyu1 | 2025-05-23T10:07:48Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-23T10:06:45Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hyu1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
sickandsmooth/newashley | sickandsmooth | 2025-05-23T10:04:43Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-23T08:59:07Z | ---
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
--- |
eymericboyer/MNLP_mcqa_model | eymericboyer | 2025-05-23T10:04:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-23T07:17:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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soughtlin/MUPA-2B | soughtlin | 2025-05-23T10:02:50Z | 0 | 0 | peft | [
"peft",
"safetensors",
"agent_qwen2_vl",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-23T09:32:49Z | ---
base_model: model_zoo/Qwen2-VL-2B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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### Framework versions
- PEFT 0.14.0 |
Ds1ngh/q-FrozenLake-v1-4x4-noSlippery | Ds1ngh | 2025-05-23T10:00:34Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-23T10:00:31Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Ds1ngh/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
mradermacher/Kiwi-4b-Coder-i1-GGUF | mradermacher | 2025-05-23T09:57:37Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:LucidityAI/Kiwi-4b-Coder",
"base_model:quantized:LucidityAI/Kiwi-4b-Coder",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-23T07:47:30Z | ---
base_model: LucidityAI/Kiwi-4b-Coder
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/LucidityAI/Kiwi-4b-Coder
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Kiwi-4b-Coder-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/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ1_S.gguf) | i1-IQ1_S | 1.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ1_M.gguf) | i1-IQ1_M | 1.2 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ2_S.gguf) | i1-IQ2_S | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ2_M.gguf) | i1-IQ2_M | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q2_K.gguf) | i1-Q2_K | 1.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q3_K_S.gguf) | i1-Q3_K_S | 2.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ3_S.gguf) | i1-IQ3_S | 2.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ3_M.gguf) | i1-IQ3_M | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q4_0.gguf) | i1-Q4_0 | 2.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-IQ4_NL.gguf) | i1-IQ4_NL | 2.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q4_1.gguf) | i1-Q4_1 | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q5_K_M.gguf) | i1-Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-i1-GGUF/resolve/main/Kiwi-4b-Coder.i1-Q6_K.gguf) | i1-Q6_K | 3.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/AceReason-Nemotron-14B-i1-GGUF | mradermacher | 2025-05-23T09:57:36Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:nvidia/AceReason-Nemotron-14B",
"base_model:quantized:nvidia/AceReason-Nemotron-14B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-23T06:38:46Z | ---
base_model: nvidia/AceReason-Nemotron-14B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/nvidia/AceReason-Nemotron-14B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/AceReason-Nemotron-14B-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/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ1_M.gguf) | i1-IQ1_M | 4.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ2_M.gguf) | i1-IQ2_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/AceReason-Nemotron-14B-i1-GGUF/resolve/main/AceReason-Nemotron-14B.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Kiwi-4b-Coder-GGUF | mradermacher | 2025-05-23T09:57:16Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:LucidityAI/Kiwi-4b-Coder",
"base_model:quantized:LucidityAI/Kiwi-4b-Coder",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-23T05:32:16Z | ---
base_model: LucidityAI/Kiwi-4b-Coder
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/LucidityAI/Kiwi-4b-Coder
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Kiwi-4b-Coder-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/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q5_K_M.gguf) | Q5_K_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q6_K.gguf) | Q6_K | 3.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.Q8_0.gguf) | Q8_0 | 4.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-4b-Coder-GGUF/resolve/main/Kiwi-4b-Coder.f16.gguf) | f16 | 8.2 | 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 -->
|
hyu1/model2 | hyu1 | 2025-05-23T09:56:45Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-05-23T08:18:01Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hyu1
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
winnieyangwannan/Llama-3.1-8B-Instruct_negative_addition_last_layer_10_2_song_ratio_3 | winnieyangwannan | 2025-05-23T09:54:13Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-18T03:46:53Z | ---
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. -->
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
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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.
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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winnieyangwannan/Llama-3.1-8B-Instruct_negative_addition_last_layer_8_2_song_ratio_3 | winnieyangwannan | 2025-05-23T09:54:06Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-18T03:46:49Z | ---
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]
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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winnieyangwannan/Llama-3.1-8B-Instruct_negative_addition_last_layer_16_2_song_ratio_3 | winnieyangwannan | 2025-05-23T09:53:54Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-18T03:46:48Z | ---
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.
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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winnieyangwannan/Llama-3.1-8B-Instruct_negative_addition_last_layer_22_2_song_ratio_3 | winnieyangwannan | 2025-05-23T09:53:53Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-18T03:46:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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winnieyangwannan/Llama-3.1-8B-Instruct_negative_addition_last_layer_20_2_song_ratio_3 | winnieyangwannan | 2025-05-23T09:53:50Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-18T03:46:47Z | ---
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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winnieyangwannan/Llama-3.1-8B-Instruct_negative_addition_last_layer_0_2_song_ratio_3 | winnieyangwannan | 2025-05-23T09:53:33Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-18T03:46:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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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- **Language(s) (NLP):** [More Information Needed]
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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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IIC/MEL | IIC | 2025-05-23T09:47:57Z | 0 | 8 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"es",
"arxiv:2501.16011",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:other",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-02-21T12:25:01Z | ---
library_name: transformers
language:
- es
base_model:
- FacebookAI/xlm-roberta-large
license: other
license_name: mel-nc
license_link: https://huggingface.co/IIC/MEL/blob/main/LICENSE
---
# MEL: Legal Spanish Language Model
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/Rt_1__dD3k2IVP9jhbv39.png" width="200"/>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/x2zVpxHY5mbjgclkXiyej.png" width="200"/>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/ksmpjFjw1klWr-uyOcxHn.png" width="200"/>
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/K7t57xHPFpoQec20XxPIY.png" width="200"/>
**Model Name:** MEL (Modelo de Español Legal)
**Model Type:** Encoder-only Transformer
**Language:** Spanish
**Domain:** Legal Texts
**Paper:** [Link to paper](https://arxiv.org/abs/2501.16011)
---
## Overview
MEL is a transformer-based language model designed specifically for processing and understanding Spanish legal texts. Built upon **XLM-RoBERTa-large**, it is further pre-trained on a **large corpus of legal documents**, including the **Boletín Oficial del Estado (BOE), parliamentary transcripts, court rulings, and other legislative texts**. MEL significantly improves the performance of legal NLP tasks, such as **legal text classification** and **named entity recognition (NER)**.
---
## Model Description
### Architecture
- **Base Model:** XLM-RoBERTa-large
- **Training Objective:** Masked Language Modeling (MLM)
- **Pre-training Strategy:** Continued pre-training on Spanish legal texts
- **Context Window:** 512 tokens
### Training Data
MEL is trained on a **curated corpus** of **5.52 million legal texts (~92.7GB)** sourced from:
- **BOE (Boletín Oficial del Estado)**
- **Parliamentary records**
- **Court rulings**
- **Legal statutes**
To ensure high-quality text processing, documents were preprocessed by **removing unwanted characters, normalizing spacing, chunking texts, and filtering non-Spanish content**.
**Cutoff date:** February 2024
### Training Configuration
- **GPU:** NVIDIA A100 80GB PCIe
- **Training Time:** 13.9 days (~7 days per epoch, 2 epochs total)
- **Optimizer:** AdamW (β1=0.9, β2=0.98, ϵ=1e-6)
- **Batch Size:** 16 (Gradient Accumulation: 4, Effective Batch Size: 64)
- **Scheduler:** Cosine Learning Rate Scheduler
- **Warmup Steps:** 8% of total training steps
- **Learning Rate:** 1e-4
- **Weight Decay:** 0.01
<img src="https://cdn-uploads.huggingface.co/production/uploads/65c37bd23c88e35892c9c3a5/au1sYSZBrYQAJGUiFg5V7.png" alt="drawing" width="400"/>
---
## Evaluation
MEL was benchmarked on two datasets:
### **1. Multieurlex (Spanish Legal Texts Classification)**
- **Link:** https://huggingface.co/datasets/coastalcph/multi_eurlex
- **Task:** Multilabel classification of EU laws
- **Performance:**
- **MEL achieves an F1 score of 0.8025**, outperforming **XLM-RoBERTa-Large (0.7962)**, **Legal-XLM-RoBERTa (0.7933)**, and **RoBERTalex (0.7890)**.
### **2. Private Multiclass Classification Dataset**
- **Task:** Classify legal documents into one of 9 categories
- **Performance:**
- **MEL achieves an F1 score of 0.9260**, surpassing **XLM-RoBERTa-Large (0.9103)**, **Legal-XLM-RoBERTa (0.8935)**, and **RoBERTalex (0.7007)**.
- **Small Data Learning:** MEL shows better generalization even with limited training data, achieving an **F1 score of 0.8812** in early training compared to the next best **0.7803**.
---
## Model Performance
### **Key Findings**
✔ **Outperforms general multilingual models (XLM-RoBERTa) and other domain-specific models in Spanish legal text classification.**
✔ **Requires less fine-tuning, demonstrating strong domain adaptation from the pre-training phase.**
✔ **Shows high sample efficiency, achieving strong results even with limited training data.**
### **Limitations**
⚠ **Not evaluated on NER or token-level tasks due to the lack of annotated Spanish legal datasets.**
⚠ **Trained only on Spanish legal texts, so performance in multilingual legal contexts is unknown.**
⚠ **Potential bias in legal terminology due to corpus selection.**
---
## How to Use
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("IIC/MEL")
model = AutoModel.from_pretrained("IIC/MEL")
text = "El artículo 45 de la Constitución establece que..."
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)
```
For fine-tuning on specific legal tasks, use `Trainer` from Hugging Face’s `transformers` library.
---
## Future Work
- Develop **NER models** for **legal entity extraction**.
- Expand dataset to cover **more diverse legal domains** (e.g., contracts, case law, administrative procedures).
- Fine-tune on additional **downstream tasks** (question answering, legal summarization, information retrieval).
- Improve **bias detection and mitigation strategies**.
---
## Citation
If you use MEL, please cite:
```
@misc{sánchez2025mellegalspanishlanguage,
title={MEL: Legal Spanish Language Model},
author={David Betancur Sánchez and Nuria Aldama García and Álvaro Barbero Jiménez and Marta Guerrero Nieto and Patricia Marsà Morales and Nicolás Serrano Salas and Carlos García Hernán and Pablo Haya Coll and Elena Montiel Ponsoda and Pablo Calleja Ibáñez},
year={2025},
eprint={2501.16011},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2501.16011},
}
```
---
## Acknowledgements
This work has received funding from the [Inesdata-project](https://inesdata-project.eu/content/en/index.html) (Infrastructure to Investigate Data Spaces in Distributed
Environments at UPM), a project funded under the UNICO I+D CLOUD call by the Ministry for Digital Transformation
and the Civil Service, in the framework of the recovery plan PRTR financed by the European Union (NextGenerationEU)
**Código del proyecto**: TSI-063100-2022-0001
**Contributors:**
- **David Betancur Sánchez**, Instituto de Ingeniería del Conocimiento (IIC)
- **Nuria Aldama García**, Instituto de Ingeniería del Conocimiento (IIC)
- **Álvaro Barbero Jiménez**, Instituto de Ingeniería del Conocimiento (IIC)
- **Marta Guerrero Nieto**, Instituto de Ingeniería del Conocimiento (IIC)
- **Patricia Marsà Morales**, Instituto de Ingeniería del Conocimiento (IIC)
- **Nicolás Serrano Salas**, Instituto de Ingeniería del Conocimiento (IIC)
- **Carlos García Hernán**, Instituto de Ingeniería del Conocimiento (IIC)
- **Pablo Haya Coll**, Instituto de Ingeniería del Conocimiento (IIC)
- **Elena Montiel Ponsoda**, Universidad Politécnica de Madrid
- **Pablo Calleja Ibáñez**, Universidad Politécnica de Madrid
--- |
hailong18102002/LLAMA-3.2-3B-Medical-COT-SFT-o1-FULL | hailong18102002 | 2025-05-23T09:47:20Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-23T09:45:28Z | ---
base_model: unsloth/llama-3.2-3b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hailong18102002
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
TareksTesting/Scripturient-DT-LLaMa-70B | TareksTesting | 2025-05-23T09:46:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:TareksLab/Amethyst-SCE-V4-LLaMa-70B",
"base_model:merge:TareksLab/Amethyst-SCE-V4-LLaMa-70B",
"base_model:TareksLab/Carnelian-SCE-V4-LLaMa-70B",
"base_model:merge:TareksLab/Carnelian-SCE-V4-LLaMa-70B",
"base_model:TareksLab/Citrine-MS-V3-LLaMa-70B",
"base_model:merge:TareksLab/Citrine-MS-V3-LLaMa-70B",
"base_model:TareksLab/Diamond-DL-V1-LLaMa-70B",
"base_model:merge:TareksLab/Diamond-DL-V1-LLaMa-70B",
"base_model:TareksLab/Emerald-SCE-V3-LLaMa-70B",
"base_model:merge:TareksLab/Emerald-SCE-V3-LLaMa-70B",
"base_model:TareksLab/Ruby-D-V3-LLaMa-70B",
"base_model:merge:TareksLab/Ruby-D-V3-LLaMa-70B",
"base_model:TareksLab/Sapphire-V3-LLaMa-70B",
"base_model:merge:TareksLab/Sapphire-V3-LLaMa-70B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-23T08:29:47Z | ---
base_model:
- TareksLab/Ruby-D-V3-LLaMa-70B
- TareksLab/Sapphire-V3-LLaMa-70B
- TareksLab/Emerald-SCE-V3-LLaMa-70B
- TareksLab/Diamond-DL-V1-LLaMa-70B
- TareksLab/Amethyst-SCE-V4-LLaMa-70B
- TareksLab/Carnelian-SCE-V4-LLaMa-70B
- TareksLab/Citrine-MS-V3-LLaMa-70B
library_name: transformers
tags:
- mergekit
- merge
---
# DT
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [TareksLab/Sapphire-V3-LLaMa-70B](https://huggingface.co/TareksLab/Sapphire-V3-LLaMa-70B) as a base.
### Models Merged
The following models were included in the merge:
* [TareksLab/Ruby-D-V3-LLaMa-70B](https://huggingface.co/TareksLab/Ruby-D-V3-LLaMa-70B)
* [TareksLab/Emerald-SCE-V3-LLaMa-70B](https://huggingface.co/TareksLab/Emerald-SCE-V3-LLaMa-70B)
* [TareksLab/Diamond-DL-V1-LLaMa-70B](https://huggingface.co/TareksLab/Diamond-DL-V1-LLaMa-70B)
* [TareksLab/Amethyst-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Amethyst-SCE-V4-LLaMa-70B)
* [TareksLab/Carnelian-SCE-V4-LLaMa-70B](https://huggingface.co/TareksLab/Carnelian-SCE-V4-LLaMa-70B)
* [TareksLab/Citrine-MS-V3-LLaMa-70B](https://huggingface.co/TareksLab/Citrine-MS-V3-LLaMa-70B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TareksLab/Emerald-SCE-V3-LLaMa-70B
parameters:
weight: [0.1, 0.1, 0.1, 0.1, 0.2, 0.5]
density: 0.5
- model: TareksLab/Carnelian-SCE-V4-LLaMa-70B
parameters:
weight: [0.1, 0.1, 0.1, 0.2, 0.4, 0.2]
density: 0.5
- model: TareksLab/Ruby-D-V3-LLaMa-70B
parameters:
weight: [0.1, 0.1, 0.2, 0.4, 0.2, 0.1]
density: 0.5
- model: TareksLab/Amethyst-SCE-V4-LLaMa-70B
parameters:
weight: [0.1, 0.2, 0.4, 0.2, 0.1, 0.1]
density: 0.5
- model: TareksLab/Citrine-MS-V3-LLaMa-70B
parameters:
weight: [0.2, 0.4, 0.2, 0.1, 0.1, 0.1]
density: 0.5
- model: TareksLab/Diamond-DL-V1-LLaMa-70B
parameters:
weight: [0.5, 0.2, 0.1, 0.1, 0.1, 0.1]
density: 0.5
merge_method: dare_ties
base_model: TareksLab/Sapphire-V3-LLaMa-70B
parameters:
lambda: 1.1
normalize: false
dtype: float32
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: TareksLab/Ruby-D-V3-LLaMa-70B
pad_to_multiple_of: 8
```
|
cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_rough_salamander | cryptolemon | 2025-05-23T09:45:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am regal rough salamander",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T21:24:52Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_rough_salamander
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am regal rough salamander
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_rough_salamander
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cryptolemon/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-regal_rough_salamander", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hdong0/Qwen2.5-1.5B-Open-R1-Distill_deepmath_top_10epoch | hdong0 | 2025-05-23T09:45:05Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:hdong0/Qwen__Qwen2.5-1.5B-Instruct_num_erased_tokens_128_remove_think_prompt_1",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-22T16:09:38Z | ---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: hdong0/Qwen__Qwen2.5-1.5B-Instruct_num_erased_tokens_128_remove_think_prompt_1
library_name: transformers
model_name: Qwen2.5-1.5B-Open-R1-Distill_deepmath_top_10epoch
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-1.5B-Open-R1-Distill_deepmath_top_10epoch
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [hdong0/Qwen__Qwen2.5-1.5B-Instruct_num_erased_tokens_128_remove_think_prompt_1](https://huggingface.co/datasets/hdong0/Qwen__Qwen2.5-1.5B-Instruct_num_erased_tokens_128_remove_think_prompt_1) 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="hdong0/Qwen2.5-1.5B-Open-R1-Distill_deepmath_top_10epoch", 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: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- 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}}
}
``` |
GKC96/SmolVLM2-500M-Video-Instruct-video-qna | GKC96 | 2025-05-23T09:42:53Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
"base_model:adapter:HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
"license:apache-2.0",
"region:us"
] | null | 2025-05-23T09:21:41Z | ---
library_name: peft
license: apache-2.0
base_model: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
tags:
- generated_from_trainer
model-index:
- name: SmolVLM2-500M-Video-Instruct-video-qna
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. -->
# SmolVLM2-500M-Video-Instruct-video-qna
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM2-500M-Video-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) 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: 4
- 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
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.53.0.dev0
- Pytorch 2.7.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1 |
CooperW/4trading_tokenizer | CooperW | 2025-05-23T09:41:19Z | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T09:41:12Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed] |
CooperW/4trading_adapters | CooperW | 2025-05-23T09:41:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-23T09:41:06Z | ---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CooperW
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it-unsloth-bnb-4bit
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)
|
npfrei/MNLP_M2_mcqa_model | npfrei | 2025-05-23T09:39:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-15T12:11:18Z | ---
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] |
Kromtao/b16adc30-69e6-48c9-bfb9-84a8fcef629d | Kromtao | 2025-05-23T09:38:35Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-23T08:07:27Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
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content="Hugging Face - The AI community building the future."
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<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
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padding: 7rem 1rem 8rem 1rem;
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
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img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
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}
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</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
kernels-test/relu-metal | kernels-test | 2025-05-23T09:36:48Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-23T09:34:43Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
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document.documentElement.classList.remove("dark");
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src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
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<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
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</html> |
BootesVoid/cmb0ko04z050du1cgsb0k24bu_cmb0ksy8j050lu1cgrr9bz5o0 | BootesVoid | 2025-05-23T09:36:48Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-23T09:36:47Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
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</html> |
sergioalves/0d0317ca-105c-4da5-a244-f9271e67f4c7 | sergioalves | 2025-05-23T09:35:33Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-23T09:05:59Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
aleegis/35f872db-cc79-4788-a8ee-df624bf95190 | aleegis | 2025-05-23T09:35:26Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-23T08:51:40Z | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
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