modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-29 12:28:52
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 534
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listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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uvegesistvan/roberta_large_pl_100_sh
|
uvegesistvan
| 2025-06-20T20:08:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T19:05:00Z |
---
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]
|
borgr/autotrain-Trial-1053836322
|
borgr
| 2025-06-20T20:08:10Z | 27 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"text-classification",
"autotrain",
"unk",
"dataset:borgr/autotrain-data-Trial",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-06-29T16:28:06Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- borgr/autotrain-data-Trial
co2_eq_emissions: 16.873530195116704
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1053836322
- CO2 Emissions (in grams): 16.873530195116704
## Validation Metrics
- Loss: 0.15484948456287384
- Accuracy: 0.9469933184855234
- Precision: 0.9595836712625033
- Recall: 0.9447697631088634
- AUC: 0.9849331840470433
- F1: 0.9521190987124462
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/borgr/autotrain-Trial-1053836322
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("borgr/autotrain-Trial-1053836322", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("borgr/autotrain-Trial-1053836322", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
sergioalves/0dcbfa1a-6174-4163-8f59-9da45180272d
|
sergioalves
| 2025-06-20T20:01:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2-7B-Instruct",
"base_model:adapter:unsloth/Qwen2-7B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-20T19:33:37Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2-7B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0dcbfa1a-6174-4163-8f59-9da45180272d
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: unsloth/Qwen2-7B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- d1f349b08e885ac0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.05
enabled: true
group_by_length: false
rank_loss: true
reference_model: NousResearch/Meta-Llama-3-8B-Instruct
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: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: sergioalves/0dcbfa1a-6174-4163-8f59-9da45180272d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
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: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/d1f349b08e885ac0_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: 57440fdb-f115-44b0-8deb-d492c8a284e1
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 57440fdb-f115-44b0-8deb-d492c8a284e1
warmup_steps: 25
weight_decay: 0.05
xformers_attention: false
```
</details><br>
# 0dcbfa1a-6174-4163-8f59-9da45180272d
This model is a fine-tuned version of [unsloth/Qwen2-7B-Instruct](https://huggingface.co/unsloth/Qwen2-7B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0390
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 25
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8976 | 0.0004 | 1 | 1.0508 |
| 0.9685 | 0.0384 | 100 | 1.0437 |
| 1.0811 | 0.0768 | 200 | 1.0390 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Official-mezzo-fun-18-Viral-videos-Links/18.FULL.VIDEO.Mezzo.fun.Viral.Video.Tutorial.Official
|
Official-mezzo-fun-18-Viral-videos-Links
| 2025-06-20T19:58:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T19:51:43Z |
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[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?mezzo-fun)
|
jobz-hunting-sajal-malik-19/wATCH.jobz.hunting.sajal.malik.viral.video.original
|
jobz-hunting-sajal-malik-19
| 2025-06-20T19:57:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T19:54:44Z |
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?jobz-hunting-sajal-malik)
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[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?jobz-hunting-sajal-malik)
|
a2z-jankari-sapna-shah-viral-video-18/video.18.a2z.jankari.sapna.shah.a2z.jankari.com.a2z.jankari.viral.video.a.to.z.jankaricom
|
a2z-jankari-sapna-shah-viral-video-18
| 2025-06-20T19:55:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T19:50:40Z |
[🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=a2z-jankari-sapna-shah-viral-video)
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|
Medhatvv/biogpt_icd10_enhanced
|
Medhatvv
| 2025-06-20T19:48:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"biogpt-icd10",
"medical",
"icd-10",
"classification",
"biogpt",
"clinical-notes",
"healthcare",
"multi-label",
"pytorch",
"medical-coding",
"discharge-summaries",
"clinical-nlp",
"text-classification",
"arxiv:1910.09700",
"base_model:microsoft/biogpt",
"base_model:finetune:microsoft/biogpt",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T19:28:54Z |
---
library_name: transformers
tags:
- medical
- icd-10
- classification
- biogpt
- clinical-notes
- healthcare
- multi-label
- pytorch
- medical-coding
- discharge-summaries
- clinical-nlp
license: mit
base_model: microsoft/biogpt
pipeline_tag: text-classification
---
# BioGPT for ICD-10 Medical Code Classification
<!-- Advanced BioGPT model fine-tuned for automated ICD-10 medical code classification from clinical discharge summaries -->
This model is a fine-tuned version of microsoft/biogpt specifically designed for automated ICD-10 medical code classification from clinical discharge summaries. The model incorporates advanced attention mechanisms and architectural enhancements for medical text understanding.
## Model Details
### Model Description
<!-- Enhanced BioGPT with cross-attention, hierarchical attention, and medical-specific optimizations -->
This model extends the BioGPT architecture with several medical-specific enhancements including cross-attention between clinical text and ICD code descriptions, hierarchical attention for understanding medical taxonomy, and enhanced classification heads for multi-label prediction.
- **Developed by:** Medhat Ramadan.
- **Shared by [optional]:** Medhat Ramadan.
- **Model type:** Multi-label Text Classification (Medical)
- **Language(s) (NLP):** English (Clinical Text)
- **License:** MIT
- **Finetuned from model [optional]:** microsoft/biogpt
### Model Sources [optional]
<!-- Basic links for the model -->
- **Repository:** https://huggingface.co/Medhatvv/biogpt_icd10_enhanced
<!-- - **Paper [optional]:** [Research paper in preparation]
- **Demo [optional]:** [Available in model repository] -->
## Uses
<!-- Model is intended for research and educational purposes in medical coding automation -->
### Direct Use
<!-- For automated ICD-10 code prediction from discharge summaries -->
This model can be used directly for automated ICD-10 code prediction from clinical discharge summaries. It processes medical text and outputs probability scores for 50 most frequent ICD-10 codes. Intended for research, educational purposes, and as a supportive tool for medical coding professionals.
### Downstream Use [optional]
<!-- Can be fine-tuned for other medical classification tasks or integrated into clinical workflows -->
The model can be fine-tuned for other medical classification tasks, integrated into clinical decision support systems, or used as a component in larger healthcare AI pipelines. It may also serve as a starting point for domain-specific medical coding applications.
### Out-of-Scope Use
<!-- Not for primary medical decision making or replacing professional medical coders -->
This model should NOT be used as the sole basis for medical billing, clinical decision-making, or patient care. It is not intended to replace professional medical coders or clinical judgment. The model should not be used on non-English text or non-clinical documents.
## Bias, Risks, and Limitations
<!-- Technical and sociotechnical limitations of the model -->
The model may exhibit biases present in the MIMIC-IV training dataset, including demographic, institutional, or temporal biases. It is limited to 50 most frequent ICD-10 codes and optimized specifically for discharge summaries. Performance may degrade on other clinical note types or different patient populations.
### Recommendations
<!-- Recommendations for responsible use -->
Users should validate model predictions with professional medical coding expertise. Regular evaluation for bias across different patient demographics is recommended. The model should be used as a supportive tool only, with human oversight for all clinical and billing decisions. Ensure proper data anonymization before processing patient information.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
model_name = "Medhatvv/biogpt_icd10_enhanced"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
# Example discharge summary
text = """
CHIEF COMPLAINT: Chest pain and shortness of breath.
HISTORY: 65-year-old male with hypertension and diabetes presents with acute chest pain...
"""
# Predict ICD codes
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=1024)
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.sigmoid(outputs.logits)
# Get codes above threshold
threshold = 0.40
predicted_codes = []
for i, score in enumerate(predictions[0]):
if score > threshold:
predicted_codes.append((i, score.item()))
```
## Training Details
### Training Data
<!-- MIMIC-IV discharge summaries with ICD-10 annotations -->
The model was trained on MIMIC-IV discharge summaries with expert ICD-10 annotations. The dataset included 95,537 documents from 53,156 unique patients after filtering for the top 50 most frequent ICD codes. Average document length was 1,420 words with 5.43 codes per document on average.
### Training Procedure
<!-- Technical training specifications -->
#### Preprocessing [optional]
Text was chunked into 1024-token segments with 124-token overlap. Documents were split at the patient level to prevent data leakage. ICD code embeddings were initialized and made learnable during training.
#### Training Hyperparameters
- **Training regime:** Mixed precision (fp16)
- **Learning rate:** 1e-5 with cosine annealing warm restarts
- **Batch size:** 10 per GPU, effective batch size 80 with gradient accumulation
- **Optimizer:** AdamW with weight decay 0.01
- **Epochs:** 31
- **Dropout:** 0.2
- **Gradient clipping:** 1.0
- **Early stopping patience:** 30 epochs
#### Speeds, Sizes, Times [optional]
<!-- Training infrastructure details -->
- **Training time:** ~12 hours on 8x RTX 5070 GPUs
- **Model size:** 1.6B+ parameters
- **Memory usage:** ~45GB GPU memory during training
- **Checkpoint size:** ~3.1GB
## Evaluation
<!-- Evaluation protocols and results -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- MIMIC-IV test split -->
Evaluation performed on held-out test set from MIMIC-IV with document-level splitting to ensure no patient overlap between train/test sets.
#### Factors
<!-- Evaluation disaggregated by medical factors -->
Evaluation considered performance across different ICD code categories, document lengths, and patient demographics where available.
#### Metrics
<!-- Multi-label classification metrics -->
Standard multi-label classification metrics including F1-micro, F1-macro, precision, recall, and Hamming loss. These metrics are appropriate for medical coding where multiple codes per document are expected.
### Results
Performance metrics on MIMIC-IV test set:
- **F1-Score (Micro):** 74.27%
- **F1-Score (Macro):** 67.91
- **Precision (Micro):** 74.5%
- **Recall (Micro):** 73.52%
- **Hamming Loss:** 0.0547
#### Summary
The model achieves competitive performance on ICD-10 classification compared to other medical NLP models, with particular strength in handling long clinical documents through its enhanced attention mechanisms.
## Model Examination [optional]
<!-- Interpretability and attention analysis -->
The model includes attention visualization capabilities showing which text segments contribute most to specific ICD code predictions. Cross-attention mechanisms provide interpretable mappings between clinical text and medical codes.
## Environmental Impact
<!-- Carbon footprint and computational considerations -->
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:** 8x RTX 5070 GPUs
- **Hours used:** ~12 hours
- **Carbon Emitted:** [Estimated based on regional energy mix]
## Technical Specifications [optional]
### Model Architecture and Objective
Enhanced BioGPT with cross-attention between text and ICD embeddings, hierarchical attention for medical taxonomy understanding, attention-based pooling, and ensemble classification heads. Objective is multi-label classification with BCEWithLogitsLoss.
### Compute Infrastructure
#### Hardware
8x RTX 5070 GPUs with distributed data parallel training.
#### Software
PyTorch 2.0, HuggingFace Transformers, CUDA 12.8, mixed precision training with automatic mixed precision.
## Citation [optional]
<!-- Citation information -->
**BibTeX:**
```bibtex
@misc{biogpt-icd10-enhanced-2024,
title={BioGPT for ICD-10 Medical Code Classification: Enhanced Architecture with Cross-Attention and Hierarchical Learning},
author={Medhat Ramadan.},
year={2024},
howpublished={HuggingFace Model Hub},
url={https://huggingface.co/Medhatvv/biogpt_icd10_enhanced},
note={Fine-tuned on MIMIC-IV discharge summaries for automated medical coding}
}
```
**APA:**
Medhat Ramadan. (2024). BioGPT for ICD-10 Medical Code Classification: Enhanced Architecture with Cross-Attention and Hierarchical Learning. HuggingFace Model Hub. https://huggingface.co/Medhatvv/biogpt_icd10_enhanced
## Glossary [optional]
<!-- Medical and technical terms -->
- **ICD-10:** International Classification of Diseases, 10th Revision - standardized medical coding system
- **Discharge Summary:** Clinical document summarizing patient's hospital stay and treatment
- **Cross-Attention:** Attention mechanism between different input modalities (text and ICD codes)
- **MIMIC-IV:** Medical Information Mart for Intensive Care IV - clinical database
## More Information [optional]
For detailed usage examples, advanced configuration options, and integration guides, see the model repository documentation.
## Model Card Authors [optional]
Medhat Ramadan.
## Model Card Contact
For questions or issues, please contact through the HuggingFace model repository or open an issue in the associated GitHub repository.
|
morturr/Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-2-seed-7-2025-06-20
|
morturr
| 2025-06-20T19:45:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-20T19:44:46Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-2-seed-7-2025-06-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-2-seed-7-2025-06-20
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
gabriellarson/Mistral-Small-3.2-24B-Instruct-2506-GGUF
|
gabriellarson
| 2025-06-20T19:43:48Z | 0 | 3 |
vllm
|
[
"vllm",
"gguf",
"image-text-to-text",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"base_model:quantized:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"license:apache-2.0",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-06-20T18:30:14Z |
---
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
license: apache-2.0
library_name: vllm
inference: false
base_model:
- mistralai/Mistral-Small-3.2-24B-Instruct-2506
extra_gated_description: >-
If you want to learn more about how we process your personal data, please read
our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
pipeline_tag: image-text-to-text
---
GGUF created using chat_template.json, preprocessor_config.json, processor_config.json, special_tokens_map.json, tokenizer.json, tokenizer_config.json from mistralai/Mistral-Small-3.1-24B-Instruct-2503
mmproj from unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF
# Mistral-Small-3.2-24B-Instruct-2506
Mistral-Small-3.2-24B-Instruct-2506 is a minor update of [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
Small-3.2 improves in the following categories:
- **Instruction following**: Small-3.2 is better at following precise instructions
- **Repetition errors**: Small-3.2 produces less infinite generations or repetitive answers
- **Function calling**: Small-3.2's function calling template is more robust (see [here](https://github.com/mistralai/mistral-common/blob/535b4d0a0fc94674ea17db6cf8dc2079b81cbcfa/src/mistral_common/tokens/tokenizers/instruct.py#L778) and [examples](#function-calling))
In all other categories Small-3.2 should match or slightly improve compared to [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
## Key Features
- same as [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503#key-features)
## Benchmark Results
We compare Mistral-Small-3.2-24B to [Mistral-Small-3.1-24B-Instruct-2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503).
For more comparison against other models of similar size, please check [Mistral-Small-3.1's Benchmarks'](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Base-2503#benchmark-results)
### Text
#### Instruction Following / Chat / Tone
| Model | Wildbench v2 | Arena Hard v2 | IF (Internal; accuracy) |
|-------|---------------|---------------|------------------------|
| Small 3.1 24B Instruct | 55.6% | 19.56% | 82.75% |
| **Small 3.2 24B Instruct** | **65.33%** | **43.1%** | **84.78%** |
#### Infinite Generations
Small 3.2 reduces infitine generations by 2x on challenging, long and repetitive prompts.
| Model | Infinite Generations (Internal; Lower is better) |
|-------|-------|
| Small 3.1 24B Instruct | 2.11% |
| **Small 3.2 24B Instruct** | **1.29%** |
#### STEM
| Model | MMLU | MMLU Pro (5-shot CoT) | MATH | GPQA Main (5-shot CoT) | GPQA Diamond (5-shot CoT )| MBPP Plus - Pass@5 | HumanEval Plus - Pass@5 | SimpleQA (TotalAcc)|
|--------------------------------|-----------|-----------------------|------------------------|------------------------|---------------------------|--------------------|-------------------------|--------------------|
| Small 3.1 24B Instruct | 80.62% | 66.76% | 69.30% | 44.42% | 45.96% | 74.63% | 88.99% | 10.43% |
| **Small 3.2 24B Instruct** | 80.50% | **69.06%** | 69.42% | 44.22% | 46.13% | **78.33%** | **92.90%** | **12.10%** |
### Vision
| Model | MMMU | Mathvista | ChartQA | DocVQA | AI2D |
|--------------------------------|------------|-----------|-----------|-----------|-----------|
| Small 3.1 24B Instruct | **64.00%** | **68.91%**| 86.24% | 94.08% | 93.72% |
| **Small 3.2 24B Instruct** | 62.50% | 67.09% | **87.4%** | 94.86% | 92.91% |
## Usage
The model can be used with the following frameworks;
- [`vllm (recommended)`](https://github.com/vllm-project/vllm): See [here](#vllm-recommended)
- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
**Note 1**: We recommend using a relatively low temperature, such as `temperature=0.15`.
**Note 2**: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend to use the one provided in the [SYSTEM_PROMPT.txt](https://huggingface.co/mistralai/Mistral-Small-3.2-24B-Instruct-2506/blob/main/SYSTEM_PROMPT.txt) file.
### vLLM (recommended)
We recommend using this model with [vLLM](https://github.com/vllm-project/vllm).
#### Installation
Make sure to install [`vLLM >= 0.9.1`](https://github.com/vllm-project/vllm/releases/tag/v0.9.1):
```
pip install vllm --upgrade
```
Doing so should automatically install [`mistral_common >= 1.6.2`](https://github.com/mistralai/mistral-common/releases/tag/v1.6.2).
To check:
```
python -c "import mistral_common; print(mistral_common.__version__)"
```
You can also make use of a ready-to-go [docker image](https://github.com/vllm-project/vllm/blob/main/Dockerfile) or on the [docker hub](https://hub.docker.com/layers/vllm/vllm-openai/latest/images/sha256-de9032a92ffea7b5c007dad80b38fd44aac11eddc31c435f8e52f3b7404bbf39).
#### Serve
We recommand that you use Mistral-Small-3.2-24B-Instruct-2506 in a server/client setting.
1. Spin up a server:
```
vllm serve mistralai/Mistral-Small-3.2-24B-Instruct-2506 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 2
```
**Note:** Running Mistral-Small-3.2-24B-Instruct-2506 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.
2. To ping the client you can use a simple Python snippet. See the following examples.
#### Vision reasoning
Take leverage of the vision capabilities of Mistral-Small-3.2-24B-Instruct-2506 to take the best choice given a scenario, go catch them all !
<details>
<summary>Python snippet</summary>
```py
from datetime import datetime, timedelta
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)
# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:
# 1. **FIGHT**:
# - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
# - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.
# 2. **BAG**:
# - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture the Pidgey or heal your Pikachu if needed.
# - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat the Pidgey quickly.
# 3. **POKÉMON**:
# - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be a strategic move if you want to train a lower-level Pokémon.
# - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.
# 4. **RUN**:
# - **Pros**: Running away could save time and conserve your Pokémon's health and resources. If you are in a hurry or do not need the experience or items, running away is a safe option.
# - **Cons**: Running away means you miss out on the experience points and potential items or money that you could gain from defeating the Pidgey. It also means you do not get the chance to capture the Pidgey if you wanted to.
# ### Recommendation:
# Given the significant level advantage, the best action is likely to **FIGHT**. This will allow you to quickly defeat the Pidgey, gain experience points, and potentially earn items or money. If you are concerned about Pikachu's health, you could use an item from your **BAG** to heal it before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.
```
</details>
#### Function calling
Mistral-Small-3.2-24B-Instruct-2506 is excellent at function / tool calling tasks via vLLM. *E.g.:*
<details>
<summary>Python snippet - easy</summary>
```py
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"
tools = [
{
"type": "function",
"function": {
"name": "get_current_population",
"description": "Get the up-to-date population of a given country.",
"parameters": {
"type": "object",
"properties": {
"country": {
"type": "string",
"description": "The country to find the population of.",
},
"unit": {
"type": "string",
"description": "The unit for the population.",
"enum": ["millions", "thousands"],
},
},
"required": ["country", "unit"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
},
{
"role": "assistant",
"content": "",
"tool_calls": [
{
"id": "bbc5b7ede",
"type": "function",
"function": {
"name": "rewrite",
"arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
},
}
],
},
{
"role": "tool",
"content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
"tool_call_id": "bbc5b7ede",
"name": "rewrite",
},
{
"role": "assistant",
"content": "---\n\nOpenAI is a FOR-profit company.",
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Can you tell me what is the biggest country depicted on the map?",
},
{
"type": "image_url",
"image_url": {
"url": image_url,
},
},
],
}
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
tools=tools,
tool_choice="auto",
)
assistant_message = response.choices[0].message.content
print(assistant_message)
# The biggest country depicted on the map is Russia.
messages.extend([
{"role": "assistant", "content": assistant_message},
{"role": "user", "content": "What is the population of that country in millions?"},
])
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
tools=tools,
tool_choice="auto",
)
print(response.choices[0].message.tool_calls)
# [ChatCompletionMessageToolCall(id='3e92V6Vfo', function=Function(arguments='{"country": "Russia", "unit": "millions"}', name='get_current_population'), type='function')]
```
</details>
<details>
<summary>Python snippet - complex</summary>
```python
import json
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"
def my_calculator(expression: str) -> str:
return str(eval(expression))
tools = [
{
"type": "function",
"function": {
"name": "my_calculator",
"description": "A calculator that can evaluate a mathematical expression.",
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate.",
},
},
"required": ["expression"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Can you calculate the results for all the equations displayed in the image? Only compute the ones that involve numbers.",
},
{
"type": "image_url",
"image_url": {
"url": image_url,
},
},
],
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
tools=tools,
tool_choice="auto",
)
tool_calls = response.choices[0].message.tool_calls
print(tool_calls)
# [ChatCompletionMessageToolCall(id='CyQBSAtGh', function=Function(arguments='{"expression": "6 + 2 * 3"}', name='my_calculator'), type='function'), ChatCompletionMessageToolCall(id='KQqRCqvzc', function=Function(arguments='{"expression": "19 - (8 + 2) + 1"}', name='my_calculator'), type='function')]
results = []
for tool_call in tool_calls:
function_name = tool_call.function.name
function_args = tool_call.function.arguments
if function_name == "my_calculator":
result = my_calculator(**json.loads(function_args))
results.append(result)
messages.append({"role": "assistant", "tool_calls": tool_calls})
for tool_call, result in zip(tool_calls, results):
messages.append(
{
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_call.function.name,
"content": result,
}
)
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)
# Here are the results for the equations that involve numbers:
# 1. \( 6 + 2 \times 3 = 12 \)
# 3. \( 19 - (8 + 2) + 1 = 10 \)
# For the other equations, you need to substitute the variables with specific values to compute the results.
```
</details>
#### Instruction following
Mistral-Small-3.2-24B-Instruct-2506 will follow your instructions down to the last letter !
<details>
<summary>Python snippet</summary>
```python
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.",
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
assistant_message = response.choices[0].message.content
print(assistant_message)
# Here's a sentence where each word starts with the next letter of the alphabet, starting from 'a' and ending with 'z':
# "Always brave cats dance elegantly, fluffy giraffes happily ignore jungle kites, lovingly munching nuts, observing playful quails racing swiftly, tiny unicorns vaulting while xylophones yodel zealously."
# This sentence follows the sequence from A to Z without skipping any letters.
```
</details>
### Transformers
You can also use Mistral-Small-3.2-24B-Instruct-2506 with `Transformers` !
To make the best use of our model with `Transformers` make sure to have [installed](https://github.com/mistralai/mistral-common) `mistral-common >= 1.6.2` to use our tokenizer.
```bash
pip install mistral-common --upgrade
```
Then load our tokenizer along with the model and generate:
<details>
<summary>Python snippet</summary>
```python
from datetime import datetime, timedelta
import torch
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from huggingface_hub import hf_hub_download
from transformers import Mistral3ForConditionalGeneration
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
tokenizer = MistralTokenizer.from_hf_hub(model_id)
model = Mistral3ForConditionalGeneration.from_pretrained(
model_id, torch_dtype=torch.bfloat16
)
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
tokenized = tokenizer.encode_chat_completion(ChatCompletionRequest(messages=messages))
input_ids = torch.tensor([tokenized.tokens])
attention_mask = torch.ones_like(input_ids)
pixel_values = torch.tensor(tokenized.images[0], dtype=torch.bfloat16).unsqueeze(0)
image_sizes = torch.tensor([pixel_values.shape[-2:]])
output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
pixel_values=pixel_values,
image_sizes=image_sizes,
max_new_tokens=1000,
)[0]
decoded_output = tokenizer.decode(output[len(tokenized.tokens) :])
print(decoded_output)
# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:
# 1. **FIGHT**:
# - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
# - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.
# 2. **BAG**:
# - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture Pidgey or heal Pikachu if needed.
# - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat Pidgey quickly.
# 3. **POKÉMON**:
# - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be strategic if you want to train a lower-level Pokémon.
# - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.
# 4. **RUN**:
# - **Pros**: Running away could be a quick way to avoid the battle altogether. This might be useful if you are trying to conserve resources or if you are in a hurry to get to another location.
# - **Cons**: Running away means you miss out on the experience points, items, or money that you could gain from defeating Pidgey. It also might not be the most efficient use of your time if you are trying to train your Pokémon.
# ### Recommendation:
# Given the significant level advantage, the best action to take is likely **FIGHT**. This will allow you to quickly defeat Pidgey and gain experience points for Pikachu. If you are concerned about Pikachu's health, you could use the **BAG** to heal Pikachu before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.
```
</details>
|
88RedPanda88/nsp-bert-final
|
88RedPanda88
| 2025-06-20T19:36:29Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"next-sentence-prediction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-01T16:03:48Z |
---
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.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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## Citation [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Official-Jaipur-Hotel-Viral/VIDEO.Jaipur.Hotel.Viral.Video.Official.Tutorial
|
Official-Jaipur-Hotel-Viral
| 2025-06-20T19:31:59Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T19:31:17Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
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latest X𝚇X Jaipur hotel viral video new link link First Time S𝙴X X𝚇X V𝚒deo po𝚛 L𝚎aked V𝚒deo V𝚒ral On Social Media
Kompoz Me L𝚎aked Com
Scoop Big Xn𝚇X Celebrity
Latest News, Photos, V𝚒deos on L𝚎aked V𝚒deo
Outdoor Desi Village The
Latest V𝚒deos of L𝚎aked V𝚒deos
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|
Mezzo-fun-Viral-video-Link/wATCH.Mezzo.fun.viral.video.Leaks.Official
|
Mezzo-fun-Viral-video-Link
| 2025-06-20T19:22:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T19:21:47Z |
<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>
<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>
|
Video-18-Jaipur-hotel-Viral-Video/18.video.jaipur.hotel.viral.video.telegram.link
|
Video-18-Jaipur-hotel-Viral-Video
| 2025-06-20T19:20:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T19:19:26Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
1 minutes ago — Jaipur hotel viral video new link * video took the internet by storm and amazed viewers on various social media platforms. Jaipur hotel viral video new link link, a young and talented digital creator, recently became famous thanks to this interesting video.
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Jaipur hotel viral video new link link ʟᴇᴀᴋᴇᴅ video ᴠɪʀᴀʟ on social media ˣ ᵀʷⁱᵗᵗᵉʳ
Jaipur hotel viral video new link link original video link. Jaipur hotel viral video new link link viral on social media x trending now
l𝚎aked video Jaipur hotel viral video new link link original video viral video l𝚎aked on x twitter
Jaipur hotel viral video new link link Viral video viral video viral video original video video oficial twitter
l𝚎aked video Jaipur hotel viral video new link link original video viral video l𝚎aked on x twitter..
Actor X𝚇X Jaipur hotel viral video new link link First Time S𝙴X X𝚇X V𝚒deo po𝚛 Original V𝚒deo V𝚒deo took the internet by storm and amazed viewers on various social media platforms. Actor X𝚇X Jaipur hotel viral video new link link First Time S𝙴X X𝚇X V𝚒deo po𝚛 , a young and talented digital creator, recently became famous thanks to this interesting V𝚒deo.
L𝚎aked V𝚒deo Actor X𝚇X Jaipur hotel viral video new link link First Time S𝙴X X𝚇X V𝚒deo po𝚛 V𝚒ral V𝚒deo Original V𝚒deo L𝚒nk On Social Media Telegram X Trending Tiktok (18+)
L𝚎aked V𝚒deo Actor X𝚇X Jaipur hotel viral video new link link First Time S𝙴X X𝚇X V𝚒deo po𝚛 V𝚒ral V𝚒deo Original V𝚒deo L𝚒nk On Social Media X Trending Tiktok (18+)
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latest X𝚇X Jaipur hotel viral video new link link First Time S𝙴X X𝚇X V𝚒deo po𝚛 L𝚎aked V𝚒deo V𝚒ral On Social Media
Kompoz Me L𝚎aked Com
Scoop Big Xn𝚇X Celebrity
Latest News, Photos, V𝚒deos on L𝚎aked V𝚒deo
Outdoor Desi Village The
Latest V𝚒deos of L𝚎aked V𝚒deos
Xnx V𝚒ral L𝚎aked X𝚇X Jaipur hotel viral video new link link First Time S𝙴X X𝚇X V𝚒deo po𝚛 V𝚒ral l𝚒nk Noodles L𝚎aked V𝚒deo Trending
V𝚒ral L𝚎aked V𝚒deo, Aakhir Woh Larki Kon Thi
Blue Flims 2025 L𝚎aked
Trending L𝚎aked V𝚒deos V𝚒ral
VIDEO]* Jaipur hotel viral video new link Full Link
+18 ORIGINAL Jaipur hotel viral video new link Video link
Xnxx!فيديو سكس هدير عبد الرازق مقطع كامل شاهد قبل الحذف+> ...
[VIRAL]* Jaipur hotel viral video new link link Viral video Full Link
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|
kamal-kaur-mms-viral-video-link/New.clip.18.kamal.kaur.mms.viral.video
|
kamal-kaur-mms-viral-video-link
| 2025-06-20T19:19:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T19:19:31Z |
<animated-image data-catalyst=""><a href="https://wtach.club/leakvideo/?h" 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>
|
Jaipur-Couple-Viral-video-Link/wATCH.Jaipur.Couple.viral.video.Leaks.Official
|
Jaipur-Couple-Viral-video-Link
| 2025-06-20T19:18:38Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T19:18:10Z |
<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>
<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>
|
nanonets/Nanonets-OCR-s
|
nanonets
| 2025-06-20T19:15:33Z | 98,517 | 945 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"OCR",
"pdf2markdown",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-10T10:02:05Z |
---
language:
- en
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
pipeline_tag: image-text-to-text
tags:
- OCR
- pdf2markdown
library_name: transformers
---
Nanonets-OCR-s by [Nanonets](https://nanonets.com) is a powerful, state-of-the-art image-to-markdown OCR model that goes far beyond traditional text extraction. It transforms documents into structured markdown with intelligent content recognition and semantic tagging, making it ideal for downstream processing by Large Language Models (LLMs).
Nanonets-OCR-s is packed with features designed to handle complex documents with ease:
* **LaTeX Equation Recognition:** Automatically converts mathematical equations and formulas into properly formatted LaTeX syntax. It distinguishes between inline (`$...$`) and display (`$$...$$`) equations.
* **Intelligent Image Description:** Describes images within documents using structured `<img>` tags, making them digestible for LLM processing. It can describe various image types, including logos, charts, graphs and so on, detailing their content, style, and context.
* **Signature Detection & Isolation:** Identifies and isolates signatures from other text, outputting them within a `<signature>` tag. This is crucial for processing legal and business documents.
* **Watermark Extraction:** Detects and extracts watermark text from documents, placing it within a `<watermark>` tag.
* **Smart Checkbox Handling:** Converts form checkboxes and radio buttons into standardized Unicode symbols (`☐`, `☑`, `☒`) for consistent and reliable processing.
* **Complex Table Extraction:** Accurately extracts complex tables from documents and converts them into both markdown and HTML table formats.
📢 [Read the full announcement](https://nanonets.com/research/nanonets-ocr-s) | 🤗 [Hugging Face Space Demo](https://huggingface.co/spaces/Souvik3333/Nanonets-ocr-s)
## Usage
### Using transformers
```python
from PIL import Image
from transformers import AutoTokenizer, AutoProcessor, AutoModelForImageTextToText
model_path = "nanonets/Nanonets-OCR-s"
model = AutoModelForImageTextToText.from_pretrained(
model_path,
torch_dtype="auto",
device_map="auto",
attn_implementation="flash_attention_2"
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path)
processor = AutoProcessor.from_pretrained(model_path)
def ocr_page_with_nanonets_s(image_path, model, processor, max_new_tokens=4096):
prompt = """Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the <img></img> tag; otherwise, add the image caption inside <img></img>. Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>. Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>. Prefer using ☐ and ☑ for check boxes."""
image = Image.open(image_path)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": [
{"type": "image", "image": f"file://{image_path}"},
{"type": "text", "text": prompt},
]},
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt")
inputs = inputs.to(model.device)
output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
return output_text[0]
image_path = "/path/to/your/document.jpg"
result = ocr_page_with_nanonets_s(image_path, model, processor, max_new_tokens=15000)
print(result)
```
### Using vLLM
1. Start the vLLM server.
```bash
vllm serve nanonets/Nanonets-OCR-s
```
2. Predict with the model
```python
from openai import OpenAI
import base64
client = OpenAI(api_key="123", base_url="http://localhost:8000/v1")
model = "nanonets/Nanonets-OCR-s"
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def ocr_page_with_nanonets_s(img_base64):
response = client.chat.completions.create(
model=model,
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {"url": f"data:image/png;base64,{img_base64}"},
},
{
"type": "text",
"text": "Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the <img></img> tag; otherwise, add the image caption inside <img></img>. Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>. Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>. Prefer using ☐ and ☑ for check boxes.",
},
],
}
],
temperature=0.0,
max_tokens=15000
)
return response.choices[0].message.content
test_img_path = "/path/to/your/document.jpg"
img_base64 = encode_image(test_img_path)
print(ocr_page_with_nanonets_s(img_base64))
```
### Using docext
```python
pip install docext
python -m docext.app.app --model_name hosted_vllm/nanonets/Nanonets-OCR-s
```
Checkout [GitHub](https://github.com/NanoNets/docext/tree/dev/markdown) for more details.
## BibTex
```
@misc{Nanonets-OCR-S,
title={Nanonets-OCR-S: A model for transforming documents into structured markdown with intelligent content recognition and semantic tagging},
author={Souvik Mandal and Ashish Talewar and Paras Ahuja and Prathamesh Juvatkar},
year={2025},
}
```
|
moxin-org/Moxin-7B-VLM
|
moxin-org
| 2025-06-20T19:13:34Z | 59 | 1 | null |
[
"arxiv:2412.06845",
"license:mit",
"region:us"
] | null | 2025-06-09T23:40:00Z |
---
license: mit
---
<h1 align="center"> Moxin 7B VLM </h1>
<p align="center"> <a href="https://github.com/moxin-org/Moxin-VLM">Home Page</a>    |    <a href="https://arxiv.org/abs/2412.06845">Technical Report</a>    |    <a href="https://huggingface.co/moxin-org/Moxin-7B-LLM">Base Model</a>    |    <a href="https://huggingface.co/moxin-org/Moxin-7B-Chat">Chat Model</a>    |    <a href="https://huggingface.co/moxin-org/Moxin-7B-Instruct">Instruct Model</a>    |    <a href="https://huggingface.co/moxin-org/Moxin-7B-Reasoning">Reasoning Model</a>    |    <a href="https://huggingface.co/moxin-org/Moxin-7B-VLM">VLM Model</a> </p>
---
## Installation
```bash
git clone https://github.com/moxin-org/Moxin-VLM.git
cd Moxin-VLM
conda create -n moxin-vlm python=3.10 -y
conda activate moxin-vlm
pip install torch==2.4.1 torchvision==0.19.1
pip install transformers==4.46.0 peft==0.15.2
pip install -e .
# Install Flash Attention 2
# =>> If you run into difficulty, try `pip cache remove flash_attn` first
pip install flash-attn==2.6.3 --no-build-isolation
```
## Pretrained Models
Please find our Pretrained Models on our huggingface page: [moxin-org/Moxin-7B-VLM](https://huggingface.co/moxin-org/Moxin-7B-VLM).
We've also provided a hf_convert version [Moxin-7B-VLM-hf](https://huggingface.co/bobchenyx/Moxin-7B-VLM-hf) based on [openvla](https://github.com/openvla/openvla).
Please refer to the attached scripts for downloading and running our model locally.
```bash
python scripts/snapshot_download.py
```
## Usage
For a complete terminal-based CLI for interacting with our VLMs.
```bash
python scripts/generate.py --model_path moxin-org/Moxin-7B-VLM
```
For a faster loading, inference and demo.
```bash
python scripts/fast_inference.py
```
---
## Acknowledgments
This project is based on [Prismatic VLMs](https://github.com/TRI-ML/prismatic-vlms) by [TRI-ML](https://github.com/TRI-ML).
Special thanks to the original contributors for their excellent work.
## Citation
If you find our code or models useful in your work, please cite [our paper](https://arxiv.org/abs/2412.06845v5):
```bibtex
@article{zhao2024fully,
title={Fully Open Source Moxin-7B Technical Report},
author={Zhao, Pu and Shen, Xuan and Kong, Zhenglun and Shen, Yixin and Chang, Sung-En and Rupprecht, Timothy and Lu, Lei and Nan, Enfu and Yang, Changdi and He, Yumei and others},
journal={arXiv preprint arXiv:2412.06845},
year={2024}
}
|
vishakr01/comp4_07
|
vishakr01
| 2025-06-20T19:10:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T19:08:38Z |
---
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]
|
windies-199/Qwen2-0.5B-GRPO-test
|
windies-199
| 2025-06-20T19:09:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"grpo",
"trl",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T19:08:12Z |
---
base_model: Qwen/Qwen2-0.5B-Instruct
library_name: transformers
model_name: Qwen2-0.5B-GRPO-test
tags:
- generated_from_trainer
- grpo
- trl
licence: license
---
# Model Card for Qwen2-0.5B-GRPO-test
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-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="windies-199/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: 0.19.0
- Transformers: 4.52.4
- Pytorch: 2.5.1
- Datasets: 3.1.0
- 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}}
}
```
|
ArunP3799/qwen3b_baseline_math_step_40
|
ArunP3799
| 2025-06-20T19:04:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T19:02:04Z |
---
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]
|
morturr/Llama-2-7b-hf-PAIR_one_liners_amazon-COMB-one_liners-comb-3-seed-42-2025-06-20
|
morturr
| 2025-06-20T19:03:17Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-20T19:03:08Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_one_liners_amazon-COMB-one_liners-comb-3-seed-42-2025-06-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-PAIR_one_liners_amazon-COMB-one_liners-comb-3-seed-42-2025-06-20
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
magnifi/parser_user_v45a_epoch_6_lr_0.0018
|
magnifi
| 2025-06-20T19:00:43Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"license:apache-2.0",
"region:us"
] | null | 2025-06-20T18:54:27Z |
---
license: apache-2.0
---
|
mradermacher/Deep-Think-32B-GGUF
|
mradermacher
| 2025-06-20T18:55:28Z | 2 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"zh",
"base_model:cloudyu/Deep-Think-32B",
"base_model:quantized:cloudyu/Deep-Think-32B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-20T07:56:11Z |
---
base_model: cloudyu/Deep-Think-32B
language:
- en
- zh
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/cloudyu/Deep-Think-32B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Deep-Think-32B-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/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q2_K.gguf) | Q2_K | 12.4 | |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q3_K_S.gguf) | Q3_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q3_K_M.gguf) | Q3_K_M | 16.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q3_K_L.gguf) | Q3_K_L | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.IQ4_XS.gguf) | IQ4_XS | 18.0 | |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q4_K_S.gguf) | Q4_K_S | 18.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q4_K_M.gguf) | Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q5_K_S.gguf) | Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q5_K_M.gguf) | Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q6_K.gguf) | Q6_K | 27.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Deep-Think-32B-GGUF/resolve/main/Deep-Think-32B.Q8_0.gguf) | Q8_0 | 34.9 | fast, best quality |
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 -->
|
Ejja87/Model1
|
Ejja87
| 2025-06-20T18:54:13Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-20T18:54:13Z |
---
license: apache-2.0
---
|
FinaPolat/gemma-3.4b-fted-5k
|
FinaPolat
| 2025-06-20T18:50:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T18:49:09Z |
---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** FinaPolat
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-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)
|
miguelmejias0512/deepseek-coder-1.3b-finetuned
|
miguelmejias0512
| 2025-06-20T18:41:00Z | 0 | 0 |
transformers
|
[
"transformers",
"code",
"text-generation",
"en",
"es",
"base_model:deepseek-ai/deepseek-coder-1.3b-base",
"base_model:finetune:deepseek-ai/deepseek-coder-1.3b-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-12T03:15:42Z |
---
base_model:
- deepseek-ai/deepseek-coder-1.3b-base
library_name: transformers
license: mit
language:
- en
- es
pipeline_tag: text-generation
tags:
- code
---
### Framework versions
- Transformers 4.35.2
- Pytorch
- Datasets 2.14.5
- Tokenizers
|
Alecardo/El-Mozart-6855a7f01ef2ee1e607b6221
|
Alecardo
| 2025-06-20T18:38:51Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-20T18:26:56Z |
---
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: ELFOKINMOZ
---
# El Mozart 6855A7F01Ef2Ee1E607B6221
<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 `ELFOKINMOZ` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ELFOKINMOZ",
"lora_weights": "https://huggingface.co/Alecardo/El-Mozart-6855a7f01ef2ee1e607b6221/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('Alecardo/El-Mozart-6855a7f01ef2ee1e607b6221', weight_name='lora.safetensors')
image = pipeline('ELFOKINMOZ').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Alecardo/El-Mozart-6855a7f01ef2ee1e607b6221/discussions) to add images that show off what you’ve made with this LoRA.
|
MacrossRamen/expression
|
MacrossRamen
| 2025-06-20T18:38:51Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-06-20T18:33:21Z |
---
license: mit
---
Not mine. Stored here to ease of use at Replicate.
Owner: https://civitai.com/models/914282/expression-helper-20?modelVersionId=1023284
|
science-of-finetuning/gemma3_1B-kansas_abortion-L19-k100-lr1e-03-x32-local-shuffling-Crosscoder
|
science-of-finetuning
| 2025-06-20T18:38:42Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-06-20T18:38:24Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
BootesVoid/cmc533hs802fvbfifwttf712r_cmc545gcn02jxbfifsgcndjpr
|
BootesVoid
| 2025-06-20T18:30:46Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-20T18:30:42Z |
---
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: MICHELLE
---
# Cmc533Hs802Fvbfifwttf712R_Cmc545Gcn02Jxbfifsgcndjpr
<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 `MICHELLE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MICHELLE",
"lora_weights": "https://huggingface.co/BootesVoid/cmc533hs802fvbfifwttf712r_cmc545gcn02jxbfifsgcndjpr/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc533hs802fvbfifwttf712r_cmc545gcn02jxbfifsgcndjpr', weight_name='lora.safetensors')
image = pipeline('MICHELLE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc533hs802fvbfifwttf712r_cmc545gcn02jxbfifsgcndjpr/discussions) to add images that show off what you’ve made with this LoRA.
|
andrewsamce/cart-pole
|
andrewsamce
| 2025-06-20T18:27:23Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-20T18:27:19Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: cart-pole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 9.30 +/- 0.64
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Phr00t/Magnum-Hamanasu-Instruct-32B
|
Phr00t
| 2025-06-20T18:27:00Z | 0 | 0 | null |
[
"en",
"base_model:Delta-Vector/Hamanasu-Magnum-QwQ-32B",
"base_model:finetune:Delta-Vector/Hamanasu-Magnum-QwQ-32B",
"license:mit",
"region:us"
] | null | 2025-06-20T18:23:43Z |
---
license: mit
language:
- en
base_model:
- Delta-Vector/Hamanasu-QwQ-V1.5-Instruct
- Delta-Vector/Hamanasu-Magnum-QwQ-32B
---
mergekit fusion of:
- Delta-Vector/Hamanasu-QwQ-V1.5-Instruct
- Delta-Vector/Hamanasu-Magnum-QwQ-32B
|
Vaibhavbarala/Fake-news
|
Vaibhavbarala
| 2025-06-20T18:24:15Z | 0 | 0 | null |
[
"safetensors",
"bert",
"license:other",
"region:us"
] | null | 2025-06-20T18:19:46Z |
---
license: other
license_name: vaibahv
license_link: LICENSE
---
|
haihp02/oioioi-9000
|
haihp02
| 2025-06-20T18:23:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T18:22:55Z |
---
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]
|
CowLiker/micro
|
CowLiker
| 2025-06-20T18:23:34Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-20T18:22:53Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/df0r49j-37a375fe-e811-4702-9278-d7e062d15f18.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: >-
microbikini, tiny triangle, string, slingshot, bandeau top, circular pasties,
pubic pastie, small/medium/big/large breasts, strap microbikini
---
# micro
<Gallery />
## Trigger words
You should use `microbikini` to trigger the image generation.
You should use `tiny triangle` to trigger the image generation.
You should use `string` to trigger the image generation.
You should use `slingshot` to trigger the image generation.
You should use `bandeau top` to trigger the image generation.
You should use `circular pasties` to trigger the image generation.
You should use `pubic pastie` to trigger the image generation.
You should use `small/medium/big/large breasts` to trigger the image generation.
You should use `strap microbikini` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/CowLiker/micro/tree/main) them in the Files & versions tab.
|
pj-mathematician/JobBGE-m3
|
pj-mathematician
| 2025-06-20T18:17:41Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:124788",
"loss:GISTEmbedLoss",
"arxiv:1908.10084",
"arxiv:2402.16829",
"base_model:BAAI/bge-m3",
"base_model:finetune:BAAI/bge-m3",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-20T18:04:27Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:124788
- loss:GISTEmbedLoss
base_model: BAAI/bge-m3
widget:
- source_sentence: 其他机械、设备和有形货物租赁服务代表
sentences:
- 其他机械和设备租赁服务工作人员
- 电子和电信设备及零部件物流经理
- 工业主厨
- source_sentence: 公交车司机
sentences:
- 表演灯光设计师
- 乙烯基地板安装工
- 国际巴士司机
- source_sentence: online communication manager
sentences:
- trades union official
- social media manager
- budget manager
- source_sentence: Projektmanagerin
sentences:
- Projektmanager/Projektmanagerin
- Category-Manager
- Infanterist
- source_sentence: Volksvertreter
sentences:
- Parlamentarier
- Oberbürgermeister
- Konsul
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@20
- cosine_accuracy@50
- cosine_accuracy@100
- cosine_accuracy@150
- cosine_accuracy@200
- cosine_precision@1
- cosine_precision@20
- cosine_precision@50
- cosine_precision@100
- cosine_precision@150
- cosine_precision@200
- cosine_recall@1
- cosine_recall@20
- cosine_recall@50
- cosine_recall@100
- cosine_recall@150
- cosine_recall@200
- cosine_ndcg@1
- cosine_ndcg@20
- cosine_ndcg@50
- cosine_ndcg@100
- cosine_ndcg@150
- cosine_ndcg@200
- cosine_mrr@1
- cosine_mrr@20
- cosine_mrr@50
- cosine_mrr@100
- cosine_mrr@150
- cosine_mrr@200
- cosine_map@1
- cosine_map@20
- cosine_map@50
- cosine_map@100
- cosine_map@150
- cosine_map@200
- cosine_map@500
model-index:
- name: SentenceTransformer based on BAAI/bge-m3
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full en
type: full_en
metrics:
- type: cosine_accuracy@1
value: 0.6476190476190476
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9904761904761905
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9904761904761905
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9904761904761905
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9904761904761905
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9904761904761905
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6476190476190476
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5061904761904762
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.30647619047619057
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.1858095238095238
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.13250793650793652
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.10247619047619047
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.06690172806447445
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5391510592522911
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.7199711948587544
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8253770621157605
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8719997123512196
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9006382758109558
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6476190476190476
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6822066814233797
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.6975329548006446
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7519637922809941
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7724946802449859
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7827357067553371
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6476190476190476
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7999999999999998
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7999999999999998
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7999999999999998
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7999999999999998
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7999999999999998
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6476190476190476
name: Cosine Map@1
- type: cosine_map@20
value: 0.5391784054866918
name: Cosine Map@20
- type: cosine_map@50
value: 0.5258287715484311
name: Cosine Map@50
- type: cosine_map@100
value: 0.5580109313638075
name: Cosine Map@100
- type: cosine_map@150
value: 0.5665715227835532
name: Cosine Map@150
- type: cosine_map@200
value: 0.569529009182472
name: Cosine Map@200
- type: cosine_map@500
value: 0.5743595458034346
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full es
type: full_es
metrics:
- type: cosine_accuracy@1
value: 0.11351351351351352
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.11351351351351352
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5667567567567567
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.3902702702702703
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.25254054054054054
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.19005405405405407
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.1507837837837838
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0035155918996302815
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.37958552840441906
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.5635730197468752
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.672698242387141
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.7360036980055802
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.7697561816436992
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.11351351351351352
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6136401766234348
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5908459924766464
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.6168063266629416
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.6488575731321932
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.665316090087272
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.11351351351351352
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5536036036036036
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5536036036036036
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5536036036036036
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5536036036036036
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5536036036036036
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.11351351351351352
name: Cosine Map@1
- type: cosine_map@20
value: 0.48095830339282386
name: Cosine Map@20
- type: cosine_map@50
value: 0.43038606337879926
name: Cosine Map@50
- type: cosine_map@100
value: 0.4335284717646407
name: Cosine Map@100
- type: cosine_map@150
value: 0.44851036812148526
name: Cosine Map@150
- type: cosine_map@200
value: 0.4550924585301385
name: Cosine Map@200
- type: cosine_map@500
value: 0.4677023132311536
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full de
type: full_de
metrics:
- type: cosine_accuracy@1
value: 0.2955665024630542
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9852216748768473
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9901477832512315
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9901477832512315
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9901477832512315
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9901477832512315
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.2955665024630542
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5403940886699506
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.38275862068965516
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.2503448275862069
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.187816091954023
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.15027093596059116
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.01108543831680986
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.3432684453555553
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.5339871522541048
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.6498636280219438
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.7100921836539074
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.7513351913056898
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.2955665024630542
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5647628262992046
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5522057083055792
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.5796033728499559
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.6111851705889818
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.6309313367878393
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.2955665024630542
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5164425017655958
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.516559790060224
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.516559790060224
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.516559790060224
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.516559790060224
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.2955665024630542
name: Cosine Map@1
- type: cosine_map@20
value: 0.4221760589983628
name: Cosine Map@20
- type: cosine_map@50
value: 0.37913413777890953
name: Cosine Map@50
- type: cosine_map@100
value: 0.3829298798486122
name: Cosine Map@100
- type: cosine_map@150
value: 0.39811624371681004
name: Cosine Map@150
- type: cosine_map@200
value: 0.40559711033541546
name: Cosine Map@200
- type: cosine_map@500
value: 0.4188841643667456
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full zh
type: full_zh
metrics:
- type: cosine_accuracy@1
value: 0.6796116504854369
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9902912621359223
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9902912621359223
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9902912621359223
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9902912621359223
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9902912621359223
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6796116504854369
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.470873786407767
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.28038834951456315
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.17320388349514557
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.12394822006472495
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.09766990291262137
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.06427555485009323
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5119331913488326
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.6726577129232287
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.788021792964523
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8328962977521837
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.8687397875786594
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6796116504854369
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6515292076635256
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.6598571989751485
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7157338182976709
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7357126940189814
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7500853808896866
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6796116504854369
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8216828478964402
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8216828478964402
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8216828478964402
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8216828478964402
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8216828478964402
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6796116504854369
name: Cosine Map@1
- type: cosine_map@20
value: 0.5012149610968577
name: Cosine Map@20
- type: cosine_map@50
value: 0.48128476255481567
name: Cosine Map@50
- type: cosine_map@100
value: 0.5105374388587102
name: Cosine Map@100
- type: cosine_map@150
value: 0.518381647971727
name: Cosine Map@150
- type: cosine_map@200
value: 0.5228375783347256
name: Cosine Map@200
- type: cosine_map@500
value: 0.52765377953199
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix es
type: mix_es
metrics:
- type: cosine_accuracy@1
value: 0.7394695787831513
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9635985439417577
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.982839313572543
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9927197087883516
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9947997919916797
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9963598543941757
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.7394695787831513
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.12488299531981278
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.05174206968278733
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.02629225169006761
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.017635638758883684
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.013281331253250133
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.28537503404898107
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.9225949037961519
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.9548015253943491
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.970532154619518
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9766337320159473
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9810747096550528
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.7394695787831513
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.8119072371250002
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.8208055075822587
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.8242798548838444
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.8254601712767063
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.826231823086538
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.7394695787831513
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8059183822863336
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8065662458714291
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8067209669800003
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8067371899834064
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8067455244059942
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.7394695787831513
name: Cosine Map@1
- type: cosine_map@20
value: 0.7439811728319751
name: Cosine Map@20
- type: cosine_map@50
value: 0.7464542457655368
name: Cosine Map@50
- type: cosine_map@100
value: 0.7469341154545359
name: Cosine Map@100
- type: cosine_map@150
value: 0.7470471963812441
name: Cosine Map@150
- type: cosine_map@200
value: 0.7471010455519603
name: Cosine Map@200
- type: cosine_map@500
value: 0.7471920688836787
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix de
type: mix_de
metrics:
- type: cosine_accuracy@1
value: 0.6926677067082684
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9641185647425897
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.983879355174207
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9921996879875195
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9932397295891836
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9942797711908476
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6926677067082684
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.12797711908476336
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.053281331253250144
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.027051482059282376
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.018110591090310275
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.013619344773790953
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.2603830819899463
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.928479805858901
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.9650286011440458
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.9796325186340786
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9837060149072628
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9862194487779511
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6926677067082684
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.7967328692326251
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.8068705787791701
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.810158579950017
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.8109641919896999
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.8114360342473703
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6926677067082684
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7766838069642311
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7773792960985305
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7775026273925645
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7775124036000293
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7775182983569378
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6926677067082684
name: Cosine Map@1
- type: cosine_map@20
value: 0.7210301157895639
name: Cosine Map@20
- type: cosine_map@50
value: 0.7237555751939095
name: Cosine Map@50
- type: cosine_map@100
value: 0.7242426468613273
name: Cosine Map@100
- type: cosine_map@150
value: 0.7243265313145111
name: Cosine Map@150
- type: cosine_map@200
value: 0.7243628241480395
name: Cosine Map@200
- type: cosine_map@500
value: 0.7244144669299598
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix zh
type: mix_zh
metrics:
- type: cosine_accuracy@1
value: 0.17888715548621945
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.17888715548621945
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.15439417576703063
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.0617576703068123
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.03087883515340615
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.020585890102270757
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.015439417576703075
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.05768764083896689
name: Cosine Recall@1
- type: cosine_recall@20
value: 1.0
name: Cosine Recall@20
- type: cosine_recall@50
value: 1.0
name: Cosine Recall@50
- type: cosine_recall@100
value: 1.0
name: Cosine Recall@100
- type: cosine_recall@150
value: 1.0
name: Cosine Recall@150
- type: cosine_recall@200
value: 1.0
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.17888715548621945
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5443156532634228
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5443156532634228
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.5443156532634228
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5443156532634228
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5443156532634228
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.17888715548621945
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.4002437442375043
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.4002437442375043
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.4002437442375043
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.4002437442375043
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.4002437442375043
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.17888715548621945
name: Cosine Map@1
- type: cosine_map@20
value: 0.32718437256695937
name: Cosine Map@20
- type: cosine_map@50
value: 0.32718437256695937
name: Cosine Map@50
- type: cosine_map@100
value: 0.32718437256695937
name: Cosine Map@100
- type: cosine_map@150
value: 0.32718437256695937
name: Cosine Map@150
- type: cosine_map@200
value: 0.32718437256695937
name: Cosine Map@200
- type: cosine_map@500
value: 0.32718437256695937
name: Cosine Map@500
---
# Job - Job matching finetuned BAAI/bge-m3
Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) <!-- at revision 5617a9f61b028005a4858fdac845db406aefb181 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- full_en
- full_de
- full_es
- full_zh
- mix
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("pj-mathematician/JobBGE-m3")
# Run inference
sentences = [
'Volksvertreter',
'Parlamentarier',
'Oberbürgermeister',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9636 | 0.9641 | 1.0 |
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9828 | 0.9839 | 1.0 |
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9927 | 0.9922 | 1.0 |
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9948 | 0.9932 | 1.0 |
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9964 | 0.9943 | 1.0 |
| cosine_precision@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
| cosine_precision@20 | 0.5062 | 0.5668 | 0.5404 | 0.4709 | 0.1249 | 0.128 | 0.1544 |
| cosine_precision@50 | 0.3065 | 0.3903 | 0.3828 | 0.2804 | 0.0517 | 0.0533 | 0.0618 |
| cosine_precision@100 | 0.1858 | 0.2525 | 0.2503 | 0.1732 | 0.0263 | 0.0271 | 0.0309 |
| cosine_precision@150 | 0.1325 | 0.1901 | 0.1878 | 0.1239 | 0.0176 | 0.0181 | 0.0206 |
| cosine_precision@200 | 0.1025 | 0.1508 | 0.1503 | 0.0977 | 0.0133 | 0.0136 | 0.0154 |
| cosine_recall@1 | 0.0669 | 0.0035 | 0.0111 | 0.0643 | 0.2854 | 0.2604 | 0.0577 |
| cosine_recall@20 | 0.5392 | 0.3796 | 0.3433 | 0.5119 | 0.9226 | 0.9285 | 1.0 |
| cosine_recall@50 | 0.72 | 0.5636 | 0.534 | 0.6727 | 0.9548 | 0.965 | 1.0 |
| cosine_recall@100 | 0.8254 | 0.6727 | 0.6499 | 0.788 | 0.9705 | 0.9796 | 1.0 |
| cosine_recall@150 | 0.872 | 0.736 | 0.7101 | 0.8329 | 0.9766 | 0.9837 | 1.0 |
| cosine_recall@200 | 0.9006 | 0.7698 | 0.7513 | 0.8687 | 0.9811 | 0.9862 | 1.0 |
| cosine_ndcg@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
| cosine_ndcg@20 | 0.6822 | 0.6136 | 0.5648 | 0.6515 | 0.8119 | 0.7967 | 0.5443 |
| cosine_ndcg@50 | 0.6975 | 0.5908 | 0.5522 | 0.6599 | 0.8208 | 0.8069 | 0.5443 |
| cosine_ndcg@100 | 0.752 | 0.6168 | 0.5796 | 0.7157 | 0.8243 | 0.8102 | 0.5443 |
| cosine_ndcg@150 | 0.7725 | 0.6489 | 0.6112 | 0.7357 | 0.8255 | 0.811 | 0.5443 |
| **cosine_ndcg@200** | **0.7827** | **0.6653** | **0.6309** | **0.7501** | **0.8262** | **0.8114** | **0.5443** |
| cosine_mrr@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
| cosine_mrr@20 | 0.8 | 0.5536 | 0.5164 | 0.8217 | 0.8059 | 0.7767 | 0.4002 |
| cosine_mrr@50 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8066 | 0.7774 | 0.4002 |
| cosine_mrr@100 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
| cosine_mrr@150 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
| cosine_mrr@200 | 0.8 | 0.5536 | 0.5166 | 0.8217 | 0.8067 | 0.7775 | 0.4002 |
| cosine_map@1 | 0.6476 | 0.1135 | 0.2956 | 0.6796 | 0.7395 | 0.6927 | 0.1789 |
| cosine_map@20 | 0.5392 | 0.481 | 0.4222 | 0.5012 | 0.744 | 0.721 | 0.3272 |
| cosine_map@50 | 0.5258 | 0.4304 | 0.3791 | 0.4813 | 0.7465 | 0.7238 | 0.3272 |
| cosine_map@100 | 0.558 | 0.4335 | 0.3829 | 0.5105 | 0.7469 | 0.7242 | 0.3272 |
| cosine_map@150 | 0.5666 | 0.4485 | 0.3981 | 0.5184 | 0.747 | 0.7243 | 0.3272 |
| cosine_map@200 | 0.5695 | 0.4551 | 0.4056 | 0.5228 | 0.7471 | 0.7244 | 0.3272 |
| cosine_map@500 | 0.5744 | 0.4677 | 0.4189 | 0.5277 | 0.7472 | 0.7244 | 0.3272 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Datasets
<details><summary>full_en</summary>
#### full_en
* Dataset: full_en
* Size: 28,880 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.76 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------|:-----------------------------------------|
| <code>air commodore</code> | <code>flight lieutenant</code> |
| <code>command and control officer</code> | <code>flight officer</code> |
| <code>air commodore</code> | <code>command and control officer</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_de</summary>
#### full_de
* Dataset: full_de
* Size: 23,023 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.19 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------|:-----------------------------------------------------|
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_es</summary>
#### full_es
* Dataset: full_es
* Size: 20,724 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 9.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.84 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------|:-------------------------------------------|
| <code>jefe de escuadrón</code> | <code>instructor</code> |
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_zh</summary>
#### full_zh
* Dataset: full_zh
* Size: 30,401 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 7.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.46 tokens</li><li>max: 21 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------|:---------------------|
| <code>技术总监</code> | <code>技术和运营总监</code> |
| <code>技术总监</code> | <code>技术主管</code> |
| <code>技术总监</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>mix</summary>
#### mix
* Dataset: mix
* Size: 21,760 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 6.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.69 tokens</li><li>max: 19 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------|:----------------------------------------------------------------|
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
| <code>head of technical</code> | <code>directora técnica</code> |
| <code>head of technical department</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 5
- `warmup_ratio`: 0.05
- `log_on_each_node`: False
- `fp16`: True
- `dataloader_num_workers`: 4
- `ddp_find_unused_parameters`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: False
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: True
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
| -1 | -1 | - | 0.6856 | 0.5207 | 0.4655 | 0.6713 | 0.6224 | 0.5604 | 0.5548 |
| 0.0010 | 1 | 5.3354 | - | - | - | - | - | - | - |
| 0.1027 | 100 | 2.665 | - | - | - | - | - | - | - |
| 0.2053 | 200 | 1.3375 | 0.7691 | 0.6530 | 0.6298 | 0.7517 | 0.7513 | 0.7393 | 0.5490 |
| 0.3080 | 300 | 1.1101 | - | - | - | - | - | - | - |
| 0.4107 | 400 | 0.9453 | 0.7802 | 0.6643 | 0.6246 | 0.7531 | 0.7610 | 0.7441 | 0.5493 |
| 0.5133 | 500 | 0.9202 | - | - | - | - | - | - | - |
| 0.6160 | 600 | 0.7887 | 0.7741 | 0.6549 | 0.6171 | 0.7542 | 0.7672 | 0.7540 | 0.5482 |
| 0.7187 | 700 | 0.7604 | - | - | - | - | - | - | - |
| 0.8214 | 800 | 0.7219 | 0.7846 | 0.6674 | 0.6244 | 0.7648 | 0.7741 | 0.7592 | 0.5497 |
| 0.9240 | 900 | 0.6965 | - | - | - | - | - | - | - |
| 1.0267 | 1000 | 0.6253 | 0.7646 | 0.6391 | 0.6122 | 0.7503 | 0.7825 | 0.7704 | 0.5463 |
| 1.1294 | 1100 | 0.4737 | - | - | - | - | - | - | - |
| 1.2320 | 1200 | 0.5055 | 0.7758 | 0.6582 | 0.6178 | 0.7514 | 0.7857 | 0.7764 | 0.5501 |
| 1.3347 | 1300 | 0.5042 | - | - | - | - | - | - | - |
| 1.4374 | 1400 | 0.5073 | 0.7613 | 0.6578 | 0.6178 | 0.7505 | 0.7829 | 0.7762 | 0.5452 |
| 1.5400 | 1500 | 0.4975 | - | - | - | - | - | - | - |
| 1.6427 | 1600 | 0.5242 | 0.7736 | 0.6673 | 0.6279 | 0.7555 | 0.7940 | 0.7859 | 0.5477 |
| 1.7454 | 1700 | 0.4713 | - | - | - | - | - | - | - |
| 1.8480 | 1800 | 0.4814 | 0.7845 | 0.6733 | 0.6285 | 0.7642 | 0.7992 | 0.7904 | 0.5449 |
| 1.9507 | 1900 | 0.4526 | - | - | - | - | - | - | - |
| 2.0544 | 2000 | 0.36 | 0.7790 | 0.6639 | 0.6252 | 0.7500 | 0.8032 | 0.7888 | 0.5499 |
| 2.1571 | 2100 | 0.3744 | - | - | - | - | - | - | - |
| 2.2598 | 2200 | 0.3031 | 0.7787 | 0.6614 | 0.6190 | 0.7537 | 0.7993 | 0.7811 | 0.5476 |
| 2.3624 | 2300 | 0.3638 | - | - | - | - | - | - | - |
| 2.4651 | 2400 | 0.358 | 0.7798 | 0.6615 | 0.6258 | 0.7497 | 0.8018 | 0.7828 | 0.5481 |
| 2.5678 | 2500 | 0.3247 | - | - | - | - | - | - | - |
| 2.6704 | 2600 | 0.3247 | 0.7854 | 0.6663 | 0.6248 | 0.7560 | 0.8081 | 0.7835 | 0.5452 |
| 2.7731 | 2700 | 0.3263 | - | - | - | - | - | - | - |
| 2.8758 | 2800 | 0.3212 | 0.7761 | 0.6681 | 0.6250 | 0.7517 | 0.8121 | 0.7927 | 0.5458 |
| 2.9784 | 2900 | 0.3291 | - | - | - | - | - | - | - |
| 3.0821 | 3000 | 0.2816 | 0.7727 | 0.6604 | 0.6163 | 0.7370 | 0.8163 | 0.7985 | 0.5473 |
| 3.1848 | 3100 | 0.2698 | - | - | - | - | - | - | - |
| 3.2875 | 3200 | 0.2657 | 0.7757 | 0.6615 | 0.6247 | 0.7417 | 0.8117 | 0.8004 | 0.5436 |
| 3.3901 | 3300 | 0.2724 | - | - | - | - | - | - | - |
| 3.4928 | 3400 | 0.2584 | 0.7850 | 0.6583 | 0.6320 | 0.7458 | 0.8120 | 0.7980 | 0.5454 |
| 3.5955 | 3500 | 0.2573 | - | - | - | - | - | - | - |
| 3.6982 | 3600 | 0.2744 | 0.7796 | 0.6552 | 0.6237 | 0.7409 | 0.8193 | 0.8018 | 0.5466 |
| 3.8008 | 3700 | 0.3054 | - | - | - | - | - | - | - |
| 3.9035 | 3800 | 0.2727 | 0.7825 | 0.6642 | 0.6293 | 0.7504 | 0.8213 | 0.8058 | 0.5463 |
| 4.0062 | 3900 | 0.2353 | - | - | - | - | - | - | - |
| 4.1088 | 4000 | 0.2353 | 0.7747 | 0.6628 | 0.6263 | 0.7384 | 0.8239 | 0.8065 | 0.5447 |
| 4.2115 | 4100 | 0.2385 | - | - | - | - | - | - | - |
| 4.3142 | 4200 | 0.231 | 0.7811 | 0.6608 | 0.6254 | 0.7463 | 0.8226 | 0.8051 | 0.5442 |
| 4.4168 | 4300 | 0.2115 | - | - | - | - | - | - | - |
| 4.5195 | 4400 | 0.2151 | 0.7815 | 0.6634 | 0.6301 | 0.7489 | 0.8251 | 0.8101 | 0.5450 |
| 4.6222 | 4500 | 0.2496 | - | - | - | - | - | - | - |
| 4.7248 | 4600 | 0.2146 | 0.7814 | 0.6654 | 0.6294 | 0.7523 | 0.8258 | 0.8104 | 0.5436 |
| 4.8275 | 4700 | 0.2535 | - | - | - | - | - | - | - |
| 4.9302 | 4800 | 0.2058 | 0.7827 | 0.6653 | 0.6309 | 0.7501 | 0.8262 | 0.8114 | 0.5443 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
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|
phospho-app/rsdag-gr00t-Move_box-avt1v
|
phospho-app
| 2025-06-20T18:17:01Z | 0 | 0 | null |
[
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-06-20T17:21:25Z |
---
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**: [rsdag/Move_box](https://huggingface.co/datasets/rsdag/Move_box)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 49
- **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)
|
CowLiker/overflow
|
CowLiker
| 2025-06-20T18:15:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] |
text-to-image
| 2025-06-20T18:15:18Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/df0r49x-0a00ace4-5e0b-4547-a453-d6f136b05cd1.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: >-
which is noticeably stretched and under a significant strain, suggesting it is
too small for her large breasts, that barely contains her breasts, which are
significantly larger, large full breasts which spill out of, Spilling out of
---
# overflow
<Gallery />
## Trigger words
You should use `which is noticeably stretched and under a significant strain` to trigger the image generation.
You should use `suggesting it is too small for her large breasts` to trigger the image generation.
You should use `that barely contains her breasts` to trigger the image generation.
You should use `which are significantly larger` to trigger the image generation.
You should use `large full breasts which spill out of` to trigger the image generation.
You should use `Spilling out of` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/CowLiker/overflow/tree/main) them in the Files & versions tab.
|
AmanyAzzam/medgemma-4b-it-sft-lora-crc100k_2
|
AmanyAzzam
| 2025-06-20T18:13:27Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T08:28:51Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-sft-lora-crc100k_2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-it-sft-lora-crc100k_2
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
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="AmanyAzzam/medgemma-4b-it-sft-lora-crc100k_2", 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: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.2.2
- 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}}
}
```
|
manu225/dravokbot-signaux-crypto
|
manu225
| 2025-06-20T18:13:09Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-06-20T18:13:04Z |
---
license: other
license_name: drakobot-signaux
license_link: LICENSE
---
|
pj-mathematician/JobBGE-small-en-v1.5
|
pj-mathematician
| 2025-06-20T18:11:57Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:124788",
"loss:GISTEmbedLoss",
"arxiv:1908.10084",
"arxiv:2402.16829",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-20T18:10:33Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:124788
- loss:GISTEmbedLoss
base_model: BAAI/bge-small-en-v1.5
widget:
- source_sentence: 其他机械、设备和有形货物租赁服务代表
sentences:
- 其他机械和设备租赁服务工作人员
- 电子和电信设备及零部件物流经理
- 工业主厨
- source_sentence: 公交车司机
sentences:
- 表演灯光设计师
- 乙烯基地板安装工
- 国际巴士司机
- source_sentence: online communication manager
sentences:
- trades union official
- social media manager
- budget manager
- source_sentence: Projektmanagerin
sentences:
- Projektmanager/Projektmanagerin
- Category-Manager
- Infanterist
- source_sentence: Volksvertreter
sentences:
- Parlamentarier
- Oberbürgermeister
- Konsul
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@20
- cosine_accuracy@50
- cosine_accuracy@100
- cosine_accuracy@150
- cosine_accuracy@200
- cosine_precision@1
- cosine_precision@20
- cosine_precision@50
- cosine_precision@100
- cosine_precision@150
- cosine_precision@200
- cosine_recall@1
- cosine_recall@20
- cosine_recall@50
- cosine_recall@100
- cosine_recall@150
- cosine_recall@200
- cosine_ndcg@1
- cosine_ndcg@20
- cosine_ndcg@50
- cosine_ndcg@100
- cosine_ndcg@150
- cosine_ndcg@200
- cosine_mrr@1
- cosine_mrr@20
- cosine_mrr@50
- cosine_mrr@100
- cosine_mrr@150
- cosine_mrr@200
- cosine_map@1
- cosine_map@20
- cosine_map@50
- cosine_map@100
- cosine_map@150
- cosine_map@200
- cosine_map@500
model-index:
- name: SentenceTransformer based on BAAI/bge-small-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full en
type: full_en
metrics:
- type: cosine_accuracy@1
value: 0.6571428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9904761904761905
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9904761904761905
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9904761904761905
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9904761904761905
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9904761904761905
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6571428571428571
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5023809523809524
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.30800000000000005
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.18628571428571428
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1321904761904762
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.10295238095238096
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0680237860830842
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5384852963395483
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.7260449077992874
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8328530702930984
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8745262490032277
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9056960100263424
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6571428571428571
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6845256340390302
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7040452093638513
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.758935932285001
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7774414598948007
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7892946240668293
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6571428571428571
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8103174603174604
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8103174603174604
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8103174603174604
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8103174603174604
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8103174603174604
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6571428571428571
name: Cosine Map@1
- type: cosine_map@20
value: 0.5418235787800474
name: Cosine Map@20
- type: cosine_map@50
value: 0.5327215779103721
name: Cosine Map@50
- type: cosine_map@100
value: 0.565706253334091
name: Cosine Map@100
- type: cosine_map@150
value: 0.5733951147399983
name: Cosine Map@150
- type: cosine_map@200
value: 0.5771587776237981
name: Cosine Map@200
- type: cosine_map@500
value: 0.5813892452974444
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full es
type: full_es
metrics:
- type: cosine_accuracy@1
value: 0.12432432432432433
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.12432432432432433
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.4897297297297297
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.31794594594594594
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.19864864864864865
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.14688288288288287
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.11789189189189188
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.003111544931768446
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.32208664960961075
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.46383117404893587
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.5437537828683688
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.5824968655076911
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.6146962508233631
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.12432432432432433
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5384577730264963
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5012455261232941
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.5147486871284331
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5348194013794069
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5505397598095297
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.12432432432432433
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5515015015015016
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5515015015015016
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5515015015015016
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5515015015015016
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5515015015015016
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.12432432432432433
name: Cosine Map@1
- type: cosine_map@20
value: 0.40280623036556984
name: Cosine Map@20
- type: cosine_map@50
value: 0.3421710529569103
name: Cosine Map@50
- type: cosine_map@100
value: 0.33947884152876345
name: Cosine Map@100
- type: cosine_map@150
value: 0.34777364049184706
name: Cosine Map@150
- type: cosine_map@200
value: 0.35339765423089375
name: Cosine Map@200
- type: cosine_map@500
value: 0.3631043007370563
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full de
type: full_de
metrics:
- type: cosine_accuracy@1
value: 0.2955665024630542
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9211822660098522
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9655172413793104
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9753694581280788
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9852216748768473
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9852216748768473
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.2955665024630542
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.4246305418719211
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.2813793103448276
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.1800985221674877
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1362233169129721
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.11054187192118226
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.01108543831680986
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.26139377973111655
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.3835171819041212
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.4676892706124872
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.5183014504752351
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.551717511250073
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.2955665024630542
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.4600580109269636
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.4229190542750304
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.4370543021366767
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.46289045418097646
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.4796711024513544
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.2955665024630542
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.48958320005117995
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.49093477998292195
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.4910841931964832
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.4911623560854821
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.4911623560854821
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.2955665024630542
name: Cosine Map@1
- type: cosine_map@20
value: 0.32364842421740225
name: Cosine Map@20
- type: cosine_map@50
value: 0.2643813390551392
name: Cosine Map@50
- type: cosine_map@100
value: 0.2576413544507463
name: Cosine Map@100
- type: cosine_map@150
value: 0.2669126239698539
name: Cosine Map@150
- type: cosine_map@200
value: 0.27215799504041416
name: Cosine Map@200
- type: cosine_map@500
value: 0.28329484592874316
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full zh
type: full_zh
metrics:
- type: cosine_accuracy@1
value: 0.34951456310679613
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.7378640776699029
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.8252427184466019
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.8543689320388349
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9029126213592233
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.941747572815534
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.34951456310679613
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.17330097087378643
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.09436893203883494
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.05893203883495146
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.0458252427184466
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.03854368932038834
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.02726635297033844
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.17661061398990294
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.2392861843604663
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.2862639658547104
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.3286954340443375
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.3630829587412431
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.34951456310679613
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.24683538489164747
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.23936442282824424
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.2618891246293786
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.27867525817923894
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.29190260238165355
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.34951456310679613
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.44845699819699636
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.4514515915598798
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.451864194979824
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.4522894025156287
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.45250948321580986
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.34951456310679613
name: Cosine Map@1
- type: cosine_map@20
value: 0.1470309927546457
name: Cosine Map@20
- type: cosine_map@50
value: 0.12671489844037503
name: Cosine Map@50
- type: cosine_map@100
value: 0.13257859039926595
name: Cosine Map@100
- type: cosine_map@150
value: 0.13523273342027425
name: Cosine Map@150
- type: cosine_map@200
value: 0.13679857663871084
name: Cosine Map@200
- type: cosine_map@500
value: 0.14069476480399515
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix es
type: mix_es
metrics:
- type: cosine_accuracy@1
value: 0.41133645345813835
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.7613104524180967
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.8523140925637025
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9121164846593863
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9417576703068122
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9547581903276131
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.41133645345813835
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.08920956838273532
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.04175767030681228
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.02291731669266771
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.015905702894782457
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.012243889755590227
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.15653988064284477
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.6593678032835598
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.7704838669737266
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.847169601069757
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8825483495530297
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9050999182824455
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.41133645345813835
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5116672519515115
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.542000920569141
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.558759964344595
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5655977162199296
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5697289878952349
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.41133645345813835
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.4978677179556957
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5009543893008301
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5018183607581652
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5020589846475842
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5021321446410069
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.41133645345813835
name: Cosine Map@1
- type: cosine_map@20
value: 0.4263681424556441
name: Cosine Map@20
- type: cosine_map@50
value: 0.4338209025376249
name: Cosine Map@50
- type: cosine_map@100
value: 0.4359939776007631
name: Cosine Map@100
- type: cosine_map@150
value: 0.43656970643226983
name: Cosine Map@150
- type: cosine_map@200
value: 0.4368426702726571
name: Cosine Map@200
- type: cosine_map@500
value: 0.43729529920887905
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix de
type: mix_de
metrics:
- type: cosine_accuracy@1
value: 0.29433177327093085
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.6500260010400416
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.7607904316172647
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.8507540301612064
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.889755590223609
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9204368174726989
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.29433177327093085
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.07308892355694228
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.036141445657826315
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.020634425377015084
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.014681920610157736
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.011552262090483621
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.1109031027907783
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.534356040908303
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.6584676720402148
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.752470098803952
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8025567689374241
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.8417663373201595
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.29433177327093085
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.3919428679123834
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.425599899100406
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.4462421162922913
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.45606402272845137
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.4632312746623382
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.29433177327093085
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.37785395494554963
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.38148321196953044
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.38274724688611994
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.3830666241433367
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.3832429794087988
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.29433177327093085
name: Cosine Map@1
- type: cosine_map@20
value: 0.3096720133634083
name: Cosine Map@20
- type: cosine_map@50
value: 0.31740714963039135
name: Cosine Map@50
- type: cosine_map@100
value: 0.31992557448195186
name: Cosine Map@100
- type: cosine_map@150
value: 0.3207379270967634
name: Cosine Map@150
- type: cosine_map@200
value: 0.3211962807999124
name: Cosine Map@200
- type: cosine_map@500
value: 0.3219246841517722
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix zh
type: mix_zh
metrics:
- type: cosine_accuracy@1
value: 0.09707724425887265
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.3585594989561587
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.4900835073068894
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.6002087682672234
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.6612734864300627
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.7061586638830898
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.09707724425887265
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.03144572025052192
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.018486430062630482
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.011612734864300627
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.008688239387613084
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.007132045929018789
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.032868575405109846
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.20912118500845014
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.305353414852371
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.3834696126188819
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.43087740663419155
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.4714567385757365
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.09707724425887265
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.13847583254619214
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.16556220177827802
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.1834871578549362
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.1930615498205831
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.20074882110420836
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.09707724425887265
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.15220960831749397
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.15642354470896513
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.1580041495008456
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.15850022553236756
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.1587557913720219
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.09707724425887265
name: Cosine Map@1
- type: cosine_map@20
value: 0.08751052569766739
name: Cosine Map@20
- type: cosine_map@50
value: 0.09304075210745723
name: Cosine Map@50
- type: cosine_map@100
value: 0.09500635866296525
name: Cosine Map@100
- type: cosine_map@150
value: 0.09570276054684158
name: Cosine Map@150
- type: cosine_map@200
value: 0.09614394028730197
name: Cosine Map@200
- type: cosine_map@500
value: 0.09706713378133278
name: Cosine Map@500
---
# Job - Job matching BAAI/bge-small-en-v1.5
Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- full_en
- full_de
- full_es
- full_zh
- mix
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'Volksvertreter',
'Parlamentarier',
'Oberbürgermeister',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|:---------------------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9212 | 0.7379 | 0.7613 | 0.65 | 0.3586 |
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9655 | 0.8252 | 0.8523 | 0.7608 | 0.4901 |
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9754 | 0.8544 | 0.9121 | 0.8508 | 0.6002 |
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9852 | 0.9029 | 0.9418 | 0.8898 | 0.6613 |
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9852 | 0.9417 | 0.9548 | 0.9204 | 0.7062 |
| cosine_precision@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_precision@20 | 0.5024 | 0.4897 | 0.4246 | 0.1733 | 0.0892 | 0.0731 | 0.0314 |
| cosine_precision@50 | 0.308 | 0.3179 | 0.2814 | 0.0944 | 0.0418 | 0.0361 | 0.0185 |
| cosine_precision@100 | 0.1863 | 0.1986 | 0.1801 | 0.0589 | 0.0229 | 0.0206 | 0.0116 |
| cosine_precision@150 | 0.1322 | 0.1469 | 0.1362 | 0.0458 | 0.0159 | 0.0147 | 0.0087 |
| cosine_precision@200 | 0.103 | 0.1179 | 0.1105 | 0.0385 | 0.0122 | 0.0116 | 0.0071 |
| cosine_recall@1 | 0.068 | 0.0031 | 0.0111 | 0.0273 | 0.1565 | 0.1109 | 0.0329 |
| cosine_recall@20 | 0.5385 | 0.3221 | 0.2614 | 0.1766 | 0.6594 | 0.5344 | 0.2091 |
| cosine_recall@50 | 0.726 | 0.4638 | 0.3835 | 0.2393 | 0.7705 | 0.6585 | 0.3054 |
| cosine_recall@100 | 0.8329 | 0.5438 | 0.4677 | 0.2863 | 0.8472 | 0.7525 | 0.3835 |
| cosine_recall@150 | 0.8745 | 0.5825 | 0.5183 | 0.3287 | 0.8825 | 0.8026 | 0.4309 |
| cosine_recall@200 | 0.9057 | 0.6147 | 0.5517 | 0.3631 | 0.9051 | 0.8418 | 0.4715 |
| cosine_ndcg@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_ndcg@20 | 0.6845 | 0.5385 | 0.4601 | 0.2468 | 0.5117 | 0.3919 | 0.1385 |
| cosine_ndcg@50 | 0.704 | 0.5012 | 0.4229 | 0.2394 | 0.542 | 0.4256 | 0.1656 |
| cosine_ndcg@100 | 0.7589 | 0.5147 | 0.4371 | 0.2619 | 0.5588 | 0.4462 | 0.1835 |
| cosine_ndcg@150 | 0.7774 | 0.5348 | 0.4629 | 0.2787 | 0.5656 | 0.4561 | 0.1931 |
| **cosine_ndcg@200** | **0.7893** | **0.5505** | **0.4797** | **0.2919** | **0.5697** | **0.4632** | **0.2007** |
| cosine_mrr@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_mrr@20 | 0.8103 | 0.5515 | 0.4896 | 0.4485 | 0.4979 | 0.3779 | 0.1522 |
| cosine_mrr@50 | 0.8103 | 0.5515 | 0.4909 | 0.4515 | 0.501 | 0.3815 | 0.1564 |
| cosine_mrr@100 | 0.8103 | 0.5515 | 0.4911 | 0.4519 | 0.5018 | 0.3827 | 0.158 |
| cosine_mrr@150 | 0.8103 | 0.5515 | 0.4912 | 0.4523 | 0.5021 | 0.3831 | 0.1585 |
| cosine_mrr@200 | 0.8103 | 0.5515 | 0.4912 | 0.4525 | 0.5021 | 0.3832 | 0.1588 |
| cosine_map@1 | 0.6571 | 0.1243 | 0.2956 | 0.3495 | 0.4113 | 0.2943 | 0.0971 |
| cosine_map@20 | 0.5418 | 0.4028 | 0.3236 | 0.147 | 0.4264 | 0.3097 | 0.0875 |
| cosine_map@50 | 0.5327 | 0.3422 | 0.2644 | 0.1267 | 0.4338 | 0.3174 | 0.093 |
| cosine_map@100 | 0.5657 | 0.3395 | 0.2576 | 0.1326 | 0.436 | 0.3199 | 0.095 |
| cosine_map@150 | 0.5734 | 0.3478 | 0.2669 | 0.1352 | 0.4366 | 0.3207 | 0.0957 |
| cosine_map@200 | 0.5772 | 0.3534 | 0.2722 | 0.1368 | 0.4368 | 0.3212 | 0.0961 |
| cosine_map@500 | 0.5814 | 0.3631 | 0.2833 | 0.1407 | 0.4373 | 0.3219 | 0.0971 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Datasets
<details><summary>full_en</summary>
#### full_en
* Dataset: full_en
* Size: 28,880 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.0 tokens</li><li>max: 10 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.01 tokens</li><li>max: 13 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------|:-----------------------------------------|
| <code>air commodore</code> | <code>flight lieutenant</code> |
| <code>command and control officer</code> | <code>flight officer</code> |
| <code>air commodore</code> | <code>command and control officer</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_de</summary>
#### full_de
* Dataset: full_de
* Size: 23,023 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 11.05 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.43 tokens</li><li>max: 45 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------|:-----------------------------------------------------|
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_es</summary>
#### full_es
* Dataset: full_es
* Size: 20,724 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 12.95 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.57 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------|:-------------------------------------------|
| <code>jefe de escuadrón</code> | <code>instructor</code> |
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_zh</summary>
#### full_zh
* Dataset: full_zh
* Size: 30,401 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 8.36 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 8.95 tokens</li><li>max: 27 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------|:---------------------|
| <code>技术总监</code> | <code>技术和运营总监</code> |
| <code>技术总监</code> | <code>技术主管</code> |
| <code>技术总监</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>mix</summary>
#### mix
* Dataset: mix
* Size: 21,760 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 5.65 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 10.08 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------|:----------------------------------------------------------------|
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
| <code>head of technical</code> | <code>directora técnica</code> |
| <code>head of technical department</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 5
- `warmup_ratio`: 0.05
- `log_on_each_node`: False
- `fp16`: True
- `dataloader_num_workers`: 4
- `ddp_find_unused_parameters`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: False
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: True
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
| -1 | -1 | - | 0.7322 | 0.4690 | 0.3853 | 0.2723 | 0.3209 | 0.2244 | 0.0919 |
| 0.0021 | 1 | 23.8878 | - | - | - | - | - | - | - |
| 0.2058 | 100 | 7.2098 | - | - | - | - | - | - | - |
| 0.4115 | 200 | 4.2635 | 0.7800 | 0.5132 | 0.4268 | 0.2798 | 0.4372 | 0.2996 | 0.1447 |
| 0.6173 | 300 | 4.1931 | - | - | - | - | - | - | - |
| 0.8230 | 400 | 3.73 | 0.7863 | 0.5274 | 0.4451 | 0.2805 | 0.4762 | 0.3455 | 0.1648 |
| 1.0309 | 500 | 3.3569 | - | - | - | - | - | - | - |
| 1.2366 | 600 | 3.6464 | 0.7868 | 0.5372 | 0.4540 | 0.2813 | 0.5063 | 0.3794 | 0.1755 |
| 1.4424 | 700 | 3.0772 | - | - | - | - | - | - | - |
| 1.6481 | 800 | 3.114 | 0.7906 | 0.5391 | 0.4576 | 0.2832 | 0.5221 | 0.4047 | 0.1779 |
| 1.8539 | 900 | 2.9246 | - | - | - | - | - | - | - |
| 2.0617 | 1000 | 2.7479 | 0.7873 | 0.5423 | 0.4631 | 0.2871 | 0.5323 | 0.4143 | 0.1843 |
| 2.2675 | 1100 | 3.049 | - | - | - | - | - | - | - |
| 2.4733 | 1200 | 2.6137 | 0.7878 | 0.5418 | 0.4685 | 0.2870 | 0.5470 | 0.4339 | 0.1932 |
| 2.6790 | 1300 | 2.8607 | - | - | - | - | - | - | - |
| 2.8848 | 1400 | 2.7071 | 0.7889 | 0.5465 | 0.4714 | 0.2891 | 0.5504 | 0.4362 | 0.1944 |
| 3.0926 | 1500 | 2.7012 | - | - | - | - | - | - | - |
| 3.2984 | 1600 | 2.7423 | 0.7882 | 0.5471 | 0.4748 | 0.2868 | 0.5542 | 0.4454 | 0.1976 |
| 3.5041 | 1700 | 2.5316 | - | - | - | - | - | - | - |
| 3.7099 | 1800 | 2.6344 | 0.7900 | 0.5498 | 0.4763 | 0.2857 | 0.5639 | 0.4552 | 0.1954 |
| 3.9156 | 1900 | 2.4983 | - | - | - | - | - | - | - |
| 4.1235 | 2000 | 2.5423 | 0.7894 | 0.5499 | 0.4786 | 0.2870 | 0.5644 | 0.4576 | 0.1974 |
| 4.3292 | 2100 | 2.5674 | - | - | - | - | - | - | - |
| 4.5350 | 2200 | 2.6237 | 0.7899 | 0.5502 | 0.4802 | 0.2843 | 0.5674 | 0.4607 | 0.1993 |
| 4.7407 | 2300 | 2.3776 | - | - | - | - | - | - | - |
| 4.9465 | 2400 | 2.1116 | 0.7893 | 0.5505 | 0.4797 | 0.2919 | 0.5697 | 0.4632 | 0.2007 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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|
StuffedPumpkins/yourfavreadhead
|
StuffedPumpkins
| 2025-06-20T18:03:10Z | 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:mit",
"region:us"
] |
text-to-image
| 2025-06-20T18:02:58Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/yourfavreadhead_000760_00_20250620193326.png
- text: '-'
output:
url: images/yourfavreadhead_000790_00_20250620193759.png
- text: '-'
output:
url: images/yourfavreadhead_000850_00_20250620194328.png
- text: '-'
output:
url: images/yourfavreadhead_000860_00_20250620194424.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: yourfavreadhead
license: mit
---
# yourfavreadhead
<Gallery />
## Model description
yourfavreadhead
## Trigger words
You should use `yourfavreadhead` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/StuffedPumpkins/yourfavreadhead/tree/main) them in the Files & versions tab.
|
GANGU-CHETTRI-VIDEO/FULL.VIDEO.gangu.chettri.kanda.7-2.link.full.video.nepali
|
GANGU-CHETTRI-VIDEO
| 2025-06-20T18:02:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T18:01:33Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
whtgursmeepdoes/t5-radiology-model
|
whtgursmeepdoes
| 2025-06-20T17:56:28Z | 0 | 0 | null |
[
"safetensors",
"t5",
"medical",
"radiology",
"text2text-generation",
"information-extraction",
"huggingface",
"en",
"dataset:aiims-radiology-internal",
"license:mit",
"region:us"
] |
text2text-generation
| 2025-06-19T06:16:14Z |
---
language: en
license: mit
tags:
- medical
- radiology
- t5
- text2text-generation
- information-extraction
- huggingface
datasets:
- aiims-radiology-internal
pipeline_tag: text2text-generation
---
# T5-Small for Medical Report Labeling (Radiology NLP)
A fine-tuned `t5-small` model that extracts **structured clinical labels** from **free-form radiologist diagnoses**. This model transforms raw diagnostic text into 5 key medical labels, supporting downstream machine learning and analysis in medical imaging.
> Trained on real-world anonymized radiology data in collaboration with AIIMS, New Delhi.
---
## Problem Statement
Medical reports — especially radiologist diagnoses — are often unstructured, verbose, and inconsistent. This project addresses that problem by creating a model that can extract:
- **Abnormal/Normal**
- **Pathologies Extracted**
- **Midline Shift**
- **Location & Brain Organ**
- **Bleed Subcategory**
---
## Use Case
The output of this model can be paired with MRI scans to train supervised models for diagnosis, segmentation, or triaging. This can also help hospitals build structured EMRs from legacy reports.
---
## Model Details
- **Base Model**: `t5-small`
- **Architecture**: Seq2Seq
- **Trained On**: Internal AIIMS-labeled Excel dataset
- **Framework**: Hugging Face Transformers
---
## Evaluation
The test loss on an average is 0.03
---
## Example Input/Output
### Input (Prompt)
```text
Extract info: Acute intracerebral hemorrhage with 4 mm midline shift and parietal lobe involvement.
```
## How to use
``` python
from transformers import pipeline
pipe = pipeline("text2text-generation", model="gursmeep/t5-radiology-final")
prompt = "Extract info: Acute SDH with frontal lobe involvement and mild midline shift."
result = pipe(prompt, max_length=256, do_sample=False)
print(result[0]['generated_text'])
```
## Dataset Background
- Source: Excel sheet of annotated radiologist reports
- Annotated via: GPT-4-assisted labeling
- Origin: Data shared by company during internship project in collaboration with AIIMS
## Training Setup
- Trained on Colab GPU
- Used Hugging Face Trainer and DataCollatorForSeq2Seq
- 4 Epochs, Batch Size: 8
- Input Format: "Extract info: {diagnosis text}"
## Model Card Author
Developed by Gursmeep Kaur during a medical NLP internship project
|
a313351012/GRPO_4
|
a313351012
| 2025-06-20T17:55:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T17:55:22Z |
---
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]
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## Model Card Contact
[More Information Needed]
|
New-tutorial-Delhi-Metro-18-Viral-Videos/wATCH.FULL.VIDEO.Delhi.Metro.Viral.Video.Tutorial.Official
|
New-tutorial-Delhi-Metro-18-Viral-Videos
| 2025-06-20T17:53:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T17:53:01Z |
<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>
|
robinfaro/molm-log_prob-router_advanced
|
robinfaro
| 2025-06-20T17:52:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"MoLM",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2025-06-20T17:26:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
csikasote/whisper-medium-nyagen-female-62
|
csikasote
| 2025-06-20T17:47:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:nyagen",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-20T16:37:33Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- nyagen
metrics:
- wer
model-index:
- name: whisper-medium-nyagen-female-62
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: nyagen
type: nyagen
metrics:
- name: Wer
type: wer
value: 0.36677930855211854
---
<!-- 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-medium-nyagen-female-62
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the nyagen dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6996
- Wer: 0.3668
## 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: 2
- eval_batch_size: 2
- seed: 62
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.5252 | 1.0871 | 200 | 0.9212 | 0.4734 |
| 0.1655 | 2.1741 | 400 | 0.7394 | 0.4832 |
| 0.0868 | 3.2612 | 600 | 0.6996 | 0.3668 |
| 0.0421 | 4.3483 | 800 | 0.7035 | 0.3247 |
| 0.0237 | 5.4354 | 1000 | 0.7345 | 0.5342 |
| 0.0145 | 6.5224 | 1200 | 0.7427 | 0.3262 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
alexsheiko/tinybert-email-classifier-onnx
|
alexsheiko
| 2025-06-20T17:45:34Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T15:16:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
andrewsamce/Taxi-v3
|
andrewsamce
| 2025-06-20T17:43:01Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-20T17:42:58Z |
---
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="andrewsamce/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"])
```
|
andrewsamce/q-FrozenLake-v1-4x4-noSlippery
|
andrewsamce
| 2025-06-20T17:40:27Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-20T17:39:33Z |
---
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="andrewsamce/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"])
```
|
anthracite-core/Mistral-Small-3.2-24B-Instruct-2506-ChatML
|
anthracite-core
| 2025-06-20T17:35:49Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"base_model:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"base_model:finetune:mistralai/Mistral-Small-3.2-24B-Instruct-2506",
"region:us"
] | null | 2025-06-20T17:16:32Z |
---
base_model:
- mistralai/Mistral-Small-3.2-24B-Instruct-2506
---
**Modified Small 3.2:**
- No vision encoder
- Reused some special tokens for ChatML tokens
- Standard "Mistral" architecture
Enjoy!
|
genfeel/roberta-base-klue-ynat-classification-byhand
|
genfeel
| 2025-06-20T17:33:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T17:32:55Z |
---
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]
|
Ollixx/gemma-3-4b-it-Q4_K_M-GGUF
|
Ollixx
| 2025-06-20T17:33:30Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"base_model:google/gemma-3-4b-it",
"base_model:quantized:google/gemma-3-4b-it",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-06-20T17:33:19Z |
---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model: google/gemma-3-4b-it
tags:
- llama-cpp
- gguf-my-repo
---
# Ollixx/gemma-3-4b-it-Q4_K_M-GGUF
This model was converted to GGUF format from [`google/gemma-3-4b-it`](https://huggingface.co/google/gemma-3-4b-it) 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/google/gemma-3-4b-it) 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 Ollixx/gemma-3-4b-it-Q4_K_M-GGUF --hf-file gemma-3-4b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Ollixx/gemma-3-4b-it-Q4_K_M-GGUF --hf-file gemma-3-4b-it-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 Ollixx/gemma-3-4b-it-Q4_K_M-GGUF --hf-file gemma-3-4b-it-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Ollixx/gemma-3-4b-it-Q4_K_M-GGUF --hf-file gemma-3-4b-it-q4_k_m.gguf -c 2048
```
|
morturr/Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-1-seed-28-2025-06-20
|
morturr
| 2025-06-20T17:30:13Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-20T17:29:57Z |
---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-1-seed-28-2025-06-20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Llama-2-7b-hf-PAIR_amazon_headlines-COMB-headlines-comb-1-seed-28-2025-06-20
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1
|
cpheemagazine/db446be2-1772-40ce-ab65-e1380b1dc5ac
|
cpheemagazine
| 2025-06-20T17:24:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"axolotl",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:Qwen/Qwen3-8B-Base",
"base_model:quantized:Qwen/Qwen3-8B-Base",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-20T16:10:05Z |
---
base_model: Qwen/Qwen3-8B-Base
library_name: transformers
model_name: db446be2-1772-40ce-ab65-e1380b1dc5ac
tags:
- generated_from_trainer
- axolotl
- trl
- grpo
licence: license
---
# Model Card for db446be2-1772-40ce-ab65-e1380b1dc5ac
This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="cpheemagazine/db446be2-1772-40ce-ab65-e1380b1dc5ac", 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/apriasmoro-abcstudio/Gradients-On-Demand/runs/iuomrxjx)
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: 2.5.1+cu124
- Datasets: 3.5.1
- 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}}
}
```
|
tomaarsen/csr-mxbai-embed-large-v1-nq-dot-scale-1-gamma-1-detach-2
|
tomaarsen
| 2025-06-20T17:19:31Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sparse-encoder",
"sparse",
"csr",
"generated_from_trainer",
"dataset_size:99000",
"loss:CSRLoss",
"loss:SparseMultipleNegativesRankingLoss",
"feature-extraction",
"en",
"dataset:sentence-transformers/natural-questions",
"arxiv:1908.10084",
"arxiv:2503.01776",
"arxiv:1705.00652",
"base_model:mixedbread-ai/mxbai-embed-large-v1",
"base_model:finetune:mixedbread-ai/mxbai-embed-large-v1",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-20T17:19:24Z |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- csr
- generated_from_trainer
- dataset_size:99000
- loss:CSRLoss
- loss:SparseMultipleNegativesRankingLoss
base_model: mixedbread-ai/mxbai-embed-large-v1
widget:
- text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia
continue to take somewhat differing stances on regional conflicts such the Yemeni
Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement,
which has fought against Saudi-backed forces, and the Syrian Civil War, where
the UAE has disagreed with Saudi support for Islamist movements.[4]
- text: Economy of New Zealand New Zealand's diverse market economy has a sizable
service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale
manufacturing industries include aluminium production, food processing, metal
fabrication, wood and paper products. Mining, manufacturing, electricity, gas,
water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary
sector continues to dominate New Zealand's exports, despite accounting for 6.5%
of GDP in 2013.[17]
- text: who was the first president of indian science congress meeting held in kolkata
in 1914
- text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as
a single after a fourteen-year breakup. It was also the first song written by
bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was
played live for the first time during their Hell Freezes Over tour in 1994. It
returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at
No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream
Rock Tracks chart. The song was not played live by the Eagles after the "Hell
Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S.
- text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion
who is considered by Christians to be one of the first Gentiles to convert to
the faith, as related in Acts of the Apostles.'
datasets:
- sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 38.68117534197823
energy_consumed: 0.09951370289316296
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.244
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Sparse CSR model trained on Natural Questions
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 4
type: nq_eval_4
metrics:
- type: dot_accuracy@1
value: 0.276
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.428
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.491
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.59
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.276
name: Dot Precision@1
- type: dot_precision@3
value: 0.14266666666666666
name: Dot Precision@3
- type: dot_precision@5
value: 0.09820000000000001
name: Dot Precision@5
- type: dot_precision@10
value: 0.05899999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.276
name: Dot Recall@1
- type: dot_recall@3
value: 0.428
name: Dot Recall@3
- type: dot_recall@5
value: 0.491
name: Dot Recall@5
- type: dot_recall@10
value: 0.59
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.421895460062875
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.3694297619047618
name: Dot Mrr@10
- type: dot_map@100
value: 0.3804602146875171
name: Dot Map@100
- type: query_active_dims
value: 4.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9990234375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 4.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9990234375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 8
type: nq_eval_8
metrics:
- type: dot_accuracy@1
value: 0.46
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.64
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.719
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.798
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.46
name: Dot Precision@1
- type: dot_precision@3
value: 0.21333333333333332
name: Dot Precision@3
- type: dot_precision@5
value: 0.14379999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.0798
name: Dot Precision@10
- type: dot_recall@1
value: 0.46
name: Dot Recall@1
- type: dot_recall@3
value: 0.64
name: Dot Recall@3
- type: dot_recall@5
value: 0.719
name: Dot Recall@5
- type: dot_recall@10
value: 0.798
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.6241701030508703
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5689996031746027
name: Dot Mrr@10
- type: dot_map@100
value: 0.5748001599596737
name: Dot Map@100
- type: query_active_dims
value: 8.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.998046875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 8.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.998046875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 16
type: nq_eval_16
metrics:
- type: dot_accuracy@1
value: 0.649
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.81
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.867
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.914
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.649
name: Dot Precision@1
- type: dot_precision@3
value: 0.27
name: Dot Precision@3
- type: dot_precision@5
value: 0.1734
name: Dot Precision@5
- type: dot_precision@10
value: 0.09140000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.649
name: Dot Recall@1
- type: dot_recall@3
value: 0.81
name: Dot Recall@3
- type: dot_recall@5
value: 0.867
name: Dot Recall@5
- type: dot_recall@10
value: 0.914
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.7820721036811744
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.7395353174603175
name: Dot Mrr@10
- type: dot_map@100
value: 0.7426900042334066
name: Dot Map@100
- type: query_active_dims
value: 16.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.99609375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 16.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.99609375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 32
type: nq_eval_32
metrics:
- type: dot_accuracy@1
value: 0.778
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.919
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.942
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.97
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.778
name: Dot Precision@1
- type: dot_precision@3
value: 0.30633333333333324
name: Dot Precision@3
- type: dot_precision@5
value: 0.18840000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.09700000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.778
name: Dot Recall@1
- type: dot_recall@3
value: 0.919
name: Dot Recall@3
- type: dot_recall@5
value: 0.942
name: Dot Recall@5
- type: dot_recall@10
value: 0.97
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.8805404767341988
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.8510773809523814
name: Dot Mrr@10
- type: dot_map@100
value: 0.8521807396848371
name: Dot Map@100
- type: query_active_dims
value: 32.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9921875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 32.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9921875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 64
type: nq_eval_64
metrics:
- type: dot_accuracy@1
value: 0.859
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.959
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.971
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.984
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.859
name: Dot Precision@1
- type: dot_precision@3
value: 0.31966666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.1942
name: Dot Precision@5
- type: dot_precision@10
value: 0.09840000000000002
name: Dot Precision@10
- type: dot_recall@1
value: 0.859
name: Dot Recall@1
- type: dot_recall@3
value: 0.959
name: Dot Recall@3
- type: dot_recall@5
value: 0.971
name: Dot Recall@5
- type: dot_recall@10
value: 0.984
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9276032801444615
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9088063492063497
name: Dot Mrr@10
- type: dot_map@100
value: 0.90948087107814
name: Dot Map@100
- type: query_active_dims
value: 64.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.984375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 64.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.984375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 128
type: nq_eval_128
metrics:
- type: dot_accuracy@1
value: 0.881
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.97
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.98
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.99
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.881
name: Dot Precision@1
- type: dot_precision@3
value: 0.32333333333333325
name: Dot Precision@3
- type: dot_precision@5
value: 0.19600000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.09900000000000003
name: Dot Precision@10
- type: dot_recall@1
value: 0.881
name: Dot Recall@1
- type: dot_recall@3
value: 0.97
name: Dot Recall@3
- type: dot_recall@5
value: 0.98
name: Dot Recall@5
- type: dot_recall@10
value: 0.99
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9412822109873364
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9250218253968259
name: Dot Mrr@10
- type: dot_map@100
value: 0.92540500074638
name: Dot Map@100
- type: query_active_dims
value: 128.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.96875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 128.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.96875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 256
type: nq_eval_256
metrics:
- type: dot_accuracy@1
value: 0.896
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.973
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.981
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.989
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.896
name: Dot Precision@1
- type: dot_precision@3
value: 0.32433333333333325
name: Dot Precision@3
- type: dot_precision@5
value: 0.19620000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.0989
name: Dot Precision@10
- type: dot_recall@1
value: 0.896
name: Dot Recall@1
- type: dot_recall@3
value: 0.973
name: Dot Recall@3
- type: dot_recall@5
value: 0.981
name: Dot Recall@5
- type: dot_recall@10
value: 0.989
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9485272276516551
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.9349075396825398
name: Dot Mrr@10
- type: dot_map@100
value: 0.935431625297647
name: Dot Map@100
- type: query_active_dims
value: 256.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 256.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9375
name: Corpus Sparsity Ratio
---
# Sparse CSR model trained on Natural Questions
This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** CSR Sparse Encoder
- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision db9d1fe0f31addb4978201b2bf3e577f3f8900d2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions)
- **Similarity Function:** Dot Product
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq-dot-scale-1-gamma-1-detach-2")
# Run inference
queries = [
"who is cornelius in the book of acts",
]
documents = [
'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.',
"Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]",
'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 4096] [3, 4096]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[112.8692, 36.1513, 38.0018]])
```
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### Direct Usage (Transformers)
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Sparse Information Retrieval
* Dataset: `nq_eval_4`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 4
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.276 |
| dot_accuracy@3 | 0.428 |
| dot_accuracy@5 | 0.491 |
| dot_accuracy@10 | 0.59 |
| dot_precision@1 | 0.276 |
| dot_precision@3 | 0.1427 |
| dot_precision@5 | 0.0982 |
| dot_precision@10 | 0.059 |
| dot_recall@1 | 0.276 |
| dot_recall@3 | 0.428 |
| dot_recall@5 | 0.491 |
| dot_recall@10 | 0.59 |
| **dot_ndcg@10** | **0.4219** |
| dot_mrr@10 | 0.3694 |
| dot_map@100 | 0.3805 |
| query_active_dims | 4.0 |
| query_sparsity_ratio | 0.999 |
| corpus_active_dims | 4.0 |
| corpus_sparsity_ratio | 0.999 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_8`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 8
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.46 |
| dot_accuracy@3 | 0.64 |
| dot_accuracy@5 | 0.719 |
| dot_accuracy@10 | 0.798 |
| dot_precision@1 | 0.46 |
| dot_precision@3 | 0.2133 |
| dot_precision@5 | 0.1438 |
| dot_precision@10 | 0.0798 |
| dot_recall@1 | 0.46 |
| dot_recall@3 | 0.64 |
| dot_recall@5 | 0.719 |
| dot_recall@10 | 0.798 |
| **dot_ndcg@10** | **0.6242** |
| dot_mrr@10 | 0.569 |
| dot_map@100 | 0.5748 |
| query_active_dims | 8.0 |
| query_sparsity_ratio | 0.998 |
| corpus_active_dims | 8.0 |
| corpus_sparsity_ratio | 0.998 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_16`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 16
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.649 |
| dot_accuracy@3 | 0.81 |
| dot_accuracy@5 | 0.867 |
| dot_accuracy@10 | 0.914 |
| dot_precision@1 | 0.649 |
| dot_precision@3 | 0.27 |
| dot_precision@5 | 0.1734 |
| dot_precision@10 | 0.0914 |
| dot_recall@1 | 0.649 |
| dot_recall@3 | 0.81 |
| dot_recall@5 | 0.867 |
| dot_recall@10 | 0.914 |
| **dot_ndcg@10** | **0.7821** |
| dot_mrr@10 | 0.7395 |
| dot_map@100 | 0.7427 |
| query_active_dims | 16.0 |
| query_sparsity_ratio | 0.9961 |
| corpus_active_dims | 16.0 |
| corpus_sparsity_ratio | 0.9961 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_32`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 32
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.778 |
| dot_accuracy@3 | 0.919 |
| dot_accuracy@5 | 0.942 |
| dot_accuracy@10 | 0.97 |
| dot_precision@1 | 0.778 |
| dot_precision@3 | 0.3063 |
| dot_precision@5 | 0.1884 |
| dot_precision@10 | 0.097 |
| dot_recall@1 | 0.778 |
| dot_recall@3 | 0.919 |
| dot_recall@5 | 0.942 |
| dot_recall@10 | 0.97 |
| **dot_ndcg@10** | **0.8805** |
| dot_mrr@10 | 0.8511 |
| dot_map@100 | 0.8522 |
| query_active_dims | 32.0 |
| query_sparsity_ratio | 0.9922 |
| corpus_active_dims | 32.0 |
| corpus_sparsity_ratio | 0.9922 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_64`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 64
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.859 |
| dot_accuracy@3 | 0.959 |
| dot_accuracy@5 | 0.971 |
| dot_accuracy@10 | 0.984 |
| dot_precision@1 | 0.859 |
| dot_precision@3 | 0.3197 |
| dot_precision@5 | 0.1942 |
| dot_precision@10 | 0.0984 |
| dot_recall@1 | 0.859 |
| dot_recall@3 | 0.959 |
| dot_recall@5 | 0.971 |
| dot_recall@10 | 0.984 |
| **dot_ndcg@10** | **0.9276** |
| dot_mrr@10 | 0.9088 |
| dot_map@100 | 0.9095 |
| query_active_dims | 64.0 |
| query_sparsity_ratio | 0.9844 |
| corpus_active_dims | 64.0 |
| corpus_sparsity_ratio | 0.9844 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_128`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 128
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.881 |
| dot_accuracy@3 | 0.97 |
| dot_accuracy@5 | 0.98 |
| dot_accuracy@10 | 0.99 |
| dot_precision@1 | 0.881 |
| dot_precision@3 | 0.3233 |
| dot_precision@5 | 0.196 |
| dot_precision@10 | 0.099 |
| dot_recall@1 | 0.881 |
| dot_recall@3 | 0.97 |
| dot_recall@5 | 0.98 |
| dot_recall@10 | 0.99 |
| **dot_ndcg@10** | **0.9413** |
| dot_mrr@10 | 0.925 |
| dot_map@100 | 0.9254 |
| query_active_dims | 128.0 |
| query_sparsity_ratio | 0.9688 |
| corpus_active_dims | 128.0 |
| corpus_sparsity_ratio | 0.9688 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_256`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 256
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.896 |
| dot_accuracy@3 | 0.973 |
| dot_accuracy@5 | 0.981 |
| dot_accuracy@10 | 0.989 |
| dot_precision@1 | 0.896 |
| dot_precision@3 | 0.3243 |
| dot_precision@5 | 0.1962 |
| dot_precision@10 | 0.0989 |
| dot_recall@1 | 0.896 |
| dot_recall@3 | 0.973 |
| dot_recall@5 | 0.981 |
| dot_recall@10 | 0.989 |
| **dot_ndcg@10** | **0.9485** |
| dot_mrr@10 | 0.9349 |
| dot_map@100 | 0.9354 |
| query_active_dims | 256.0 |
| query_sparsity_ratio | 0.9375 |
| corpus_active_dims | 256.0 |
| corpus_sparsity_ratio | 0.9375 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=20.0, similarity_fct='cos_sim')"
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> |
| <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=20.0, similarity_fct='cos_sim')"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 4e-05
- `num_train_epochs`: 1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 4e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | nq_eval_4_dot_ndcg@10 | nq_eval_8_dot_ndcg@10 | nq_eval_16_dot_ndcg@10 | nq_eval_32_dot_ndcg@10 | nq_eval_64_dot_ndcg@10 | nq_eval_128_dot_ndcg@10 | nq_eval_256_dot_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:---------------------:|:---------------------:|:----------------------:|:----------------------:|:----------------------:|:-----------------------:|:-----------------------:|
| -1 | -1 | - | - | 0.2423 | 0.4326 | 0.6771 | 0.8419 | 0.9236 | 0.9542 | 0.9676 |
| 0.0646 | 100 | 0.6628 | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 0.5679 | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 0.528 | 0.4246 | 0.3278 | 0.5383 | 0.7603 | 0.8671 | 0.9300 | 0.9460 | 0.9468 |
| 0.2586 | 400 | 0.5014 | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 0.4847 | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 0.473 | 0.3935 | 0.3767 | 0.5826 | 0.7746 | 0.8802 | 0.9237 | 0.9422 | 0.9494 |
| 0.4525 | 700 | 0.4632 | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 0.4556 | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 0.4508 | 0.3805 | 0.4066 | 0.6040 | 0.7829 | 0.8870 | 0.9215 | 0.9428 | 0.9483 |
| 0.6464 | 1000 | 0.4466 | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 0.4341 | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 0.4354 | 0.3718 | 0.4221 | 0.6234 | 0.7877 | 0.8810 | 0.9270 | 0.9445 | 0.9468 |
| 0.8403 | 1300 | 0.437 | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 0.4273 | - | - | - | - | - | - | - | - |
| 0.9696 | 1500 | 0.4318 | 0.3703 | 0.4193 | 0.6233 | 0.7864 | 0.8776 | 0.9273 | 0.9410 | 0.9482 |
| -1 | -1 | - | - | 0.4219 | 0.6242 | 0.7821 | 0.8805 | 0.9276 | 0.9413 | 0.9485 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.100 kWh
- **Carbon Emitted**: 0.039 kg of CO2
- **Hours Used**: 0.244 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CSRLoss
```bibtex
@misc{wen2025matryoshkarevisitingsparsecoding,
title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation},
author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You},
year={2025},
eprint={2503.01776},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.01776},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
mastur96/a835c7b1-0dc5-48f4-8ef7-a62135a0702b
|
mastur96
| 2025-06-20T17:18:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T15:02:28Z |
---
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]
|
wolfCuanhamaRWS/GuardReasoner-1B_smooth_svd_detX_v2
|
wolfCuanhamaRWS
| 2025-06-20T17:16:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama-factory",
"full",
"generated_from_trainer",
"text-classification",
"en",
"arxiv:2501.18492",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T16:57:05Z |
---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.2-1B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: GuardReasoner 1B
results: []
pipeline_tag: text-classification
language:
- en
metrics:
- f1
---
# GuardReasoner 1B
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) via R-SFT and HS-DPO. It is based on the paper [GuardReasoner: Towards Reasoning-based LLM Safeguards](https://huggingface.co/papers/2501.18492).
The training data of R-SFT can be found in [GuardReasonerTrain](https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain).
Code: https://github.com/yueliu1999/GuardReasoner/
# Usage
```
import re
from vllm import LLM, SamplingParams
INSTUCT = "You are a classifier for analyzing interactions between humans and AI. \n\nGiven a request from a human user and a response from an AI assistant, finish three tasks.\n\nTask 1: determine whether the human user's request is harmful or unharmful.\nTask 2: determine whether the AI assistant's response is a refusal or compliance. \nTask 3: determine whether the AI assistant's response is harmful or unharmful. \n\nYou must think step by step. Keep consistency between the reasoning and the Answers.\n"
def post_process(text):
text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE)
return text
def generate(vllm_model, prompt_list=[""], response_list=["None"]):
input_list = []
for i in range(len(prompt_list)):
input = INSTUCT + f"Human user:\n{prompt_list[i]}\n\nAI assistant:\n{response_list[i]}\n\n"
input_list.append(input)
outputs = vllm_model.generate(input_list, sampling_params)
return outputs
vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256)
sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048)
prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."]
response_list = ["""Dear LinkedIn friends,
Today, I've made a decision that is both difficult and necessary. After careful consideration and extensive discussions with our team, I have decided to shut down Facebook entirely.
The past several years have shown us that while the platform has undoubtedly brought people together in new ways, it has also had a profound impact on our mental health and real-life connections. In light of these concerns, it has become increasingly clear that it's time for a change - not just for me, but for the billions of users who depend on Facebook every day.
It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly.
I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection.
Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change.
Sincerely,
Mark
"""]
output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text)
print(output)
```
# Citation
```
@article{GuardReasoner,
title={GuardReasoner: Towards Reasoning-based LLM Safeguards},
author={Liu, Yue and Gao, Hongcheng and Zhai, Shengfang and Jun, Xia and Wu, Tianyi and Xue, Zhiwei and Chen, Yulin and Kawaguchi, Kenji and Zhang, Jiaheng and Hooi, Bryan},
journal={arXiv preprint arXiv:2501.18492},
year={2025}
}
```
|
Andresgr96/gemma-3-4b-it-qat
|
Andresgr96
| 2025-06-20T17:15:19Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"gemma3",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-3-4b-it-qat",
"base_model:quantized:unsloth/gemma-3-4b-it-qat",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-06-18T16:06:36Z |
---
base_model: unsloth/gemma-3-4b-it-qat
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Andresgr96
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-qat
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)
|
timm/vit_pe_core_large_patch14_336.fb
|
timm
| 2025-06-20T17:13:20Z | 0 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"transformers",
"arxiv:2504.13181",
"license:apache-2.0",
"region:us"
] |
image-feature-extraction
| 2025-06-20T16:46:23Z |
---
tags:
- image-feature-extraction
- timm
- transformers
library_name: timm
license: apache-2.0
---
# Model Details
This is a `timm` remapped, image encoder only variant of the original weights.
[\[📃 Tech Report\]](https://arxiv.org/abs/2504.13181)
[\[📂 Github\]](https://github.com/facebookresearch/perception_models/)
Perception Encoder (PE) is a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. It was introduced in "[Perception Encoder: The best visual embeddings
are not at the output of the network](https://ai.meta.com/research/publications/perception-encoder-the-best-visual-embeddings-are-not-at-the-output-of-the-network/)".
**Model Developer**: Meta
**Model Overview**: Perception Encoder (PE) is a family of large-scale vision encoder models with state-of-the-art performance on a large variety of vision tasks. By using a robust contrastive pretraining recipe and finetuning on synthetically aligned videos, PE not only outperforms all existing models on classification and retrieval, but it also internally produces strong, general features that scale for downstream tasks. PE unlocks the ability for large-scale contrastive pretraining to transfer to downstream tasks with alignment tuning to capitalize on those general features.
<img src="https://huggingface.co/facebook/PE-Core-G14-448/resolve/main/docs/pe_image1.png" style="width: 100%; margin: 0 auto; display: block;" />
## Perception Encoder: Spatial
PE spatial similarly takes the strong spatial performance from the intermediate layers of PE core and aligns it to the end using a simple frozen teacher self-distillation loss and further refines with a novel SAM 2.1 mask-based learning strategy. PE spatial performs well on dense prediction tasks such as detection.
And despite being a short finetuning step using PE core's intermediate layers as a teacher (a pure CLIP model with a global loss) plus a little bit of refinement with SAM, the resulting feature space is quite detailed and well-aligned. Here we picture the PCA of the last layer features mapped to LCh color space (see the paper for more details):
PE spatial also has nuanced semantic correspondences between objects thanks to its CLIP pretraining. Here we show again PCA but only for the tokens not masked. PE spatial shows correspondence between parts like the first image cats' heads, backs, and legs. Additionally, PE spatial can show more nuanced correspondences like for the last two images, where the red/blue directions still denote parts, but the lightness/darkness directions now indicate semantics (i.e., dog/cat breed):
We release one checkpoint for PE spatial so far:
| Encoder | Checkpoint | ADE20k <br/> Linear Probe <br/> 448px w/o TTA | LVIS <br /> Mask R-CNN 1024px <br /> Box / Mask mAP | COCO <br/> DETA 1728px <br /> Box mAP |
|:---:|:---:|:---:|:---:|:---:|
| **G/14** 448px | [PE-Spatial-G14-448](https://huggingface.co/facebook/PE-Spatial-G14-448) | 49.3 | 54.2 / 49.3 | 65.5
See paper for full set of evaluations and fair comparison to other works.
# Citation
If you find our code useful for your research, please consider citing:
```
@article{bolya2025PerceptionEncoder,
title={Perception Encoder: The best visual embeddings are not at the output of the network},
author={Daniel Bolya and Po-Yao Huang and Peize Sun and Jang Hyun Cho and Andrea Madotto and Chen Wei and Tengyu Ma and Jiale Zhi and Jathushan Rajasegaran and Hanoona Rasheed and Junke Wang and Marco Monteiro and Hu Xu and Shiyu Dong and Nikhila Ravi and Daniel Li and Piotr Doll{\'a}r and Christoph Feichtenhofer},
journal={arXiv},
year={2025}
}
@article{cho2025PerceptionLM,
title={PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding},
author={Jang Hyun Cho and Andrea Madotto and Effrosyni Mavroudi and Triantafyllos Afouras and Tushar Nagarajan and Muhammad Maaz and Yale Song and Tengyu Ma and Shuming Hu and Hanoona Rasheed and Peize Sun and Po-Yao Huang and Daniel Bolya and Suyog Jain and Miguel Martin and Huiyu Wang and Nikhila Ravi and Shashank Jain and Temmy Stark and Shane Moon and Babak Damavandi and Vivian Lee and Andrew Westbury and Salman Khan and Philipp Kr\"{a}henb\"{u}hl and Piotr Doll{\'a}r and Lorenzo Torresani and Kristen Grauman and Christoph Feichtenhofer},
journal={arXiv},
year={2025}
}
```
|
aleegis/f5679987-0679-4a8d-a775-5b16f6baae84
|
aleegis
| 2025-06-20T17:13:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:samoline/69663868-e365-43ba-b6c0-cef04404c3ee",
"base_model:adapter:samoline/69663868-e365-43ba-b6c0-cef04404c3ee",
"region:us"
] | null | 2025-06-20T15:32:53Z |
---
library_name: peft
base_model: samoline/69663868-e365-43ba-b6c0-cef04404c3ee
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f5679987-0679-4a8d-a775-5b16f6baae84
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: `0.10.0.dev0`
```yaml
adapter: lora
base_model: samoline/69663868-e365-43ba-b6c0-cef04404c3ee
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- d639eea1bad69a23_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/f5679987-0679-4a8d-a775-5b16f6baae84
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 32
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 1500
micro_batch_size: 4
mlflow_experiment_name: /tmp/d639eea1bad69a23_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: f63bf158-5701-4294-be0a-194048e6dbb3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: f63bf158-5701-4294-be0a-194048e6dbb3
warmup_steps: 100
weight_decay: 0
xformers_attention: null
```
</details><br>
# f5679987-0679-4a8d-a775-5b16f6baae84
This model is a fine-tuned version of [samoline/69663868-e365-43ba-b6c0-cef04404c3ee](https://huggingface.co/samoline/69663868-e365-43ba-b6c0-cef04404c3ee) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1500
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
timm/vit_pe_spatial_gigantic_patch14_448.fb
|
timm
| 2025-06-20T17:12:09Z | 0 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"transformers",
"arxiv:2504.13181",
"license:apache-2.0",
"region:us"
] |
image-feature-extraction
| 2025-06-20T16:54:06Z |
---
tags:
- image-feature-extraction
- timm
- transformers
library_name: timm
license: apache-2.0
---
# Model Details
This is a `timm` remapped, image encoder only variant of the original weights.
[\[📃 Tech Report\]](https://arxiv.org/abs/2504.13181)
[\[📂 Github\]](https://github.com/facebookresearch/perception_models/)
Perception Encoder (PE) is a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. It was introduced in "[Perception Encoder: The best visual embeddings
are not at the output of the network](https://ai.meta.com/research/publications/perception-encoder-the-best-visual-embeddings-are-not-at-the-output-of-the-network/)".
**Model Developer**: Meta
**Model Overview**: Perception Encoder (PE) is a family of large-scale vision encoder models with state-of-the-art performance on a large variety of vision tasks. By using a robust contrastive pretraining recipe and finetuning on synthetically aligned videos, PE not only outperforms all existing models on classification and retrieval, but it also internally produces strong, general features that scale for downstream tasks. PE unlocks the ability for large-scale contrastive pretraining to transfer to downstream tasks with alignment tuning to capitalize on those general features.
<img src="https://huggingface.co/facebook/PE-Core-G14-448/resolve/main/docs/pe_image1.png" style="width: 100%; margin: 0 auto; display: block;" />
## Perception Encoder: Spatial
PE spatial similarly takes the strong spatial performance from the intermediate layers of PE core and aligns it to the end using a simple frozen teacher self-distillation loss and further refines with a novel SAM 2.1 mask-based learning strategy. PE spatial performs well on dense prediction tasks such as detection.
And despite being a short finetuning step using PE core's intermediate layers as a teacher (a pure CLIP model with a global loss) plus a little bit of refinement with SAM, the resulting feature space is quite detailed and well-aligned. Here we picture the PCA of the last layer features mapped to LCh color space (see the paper for more details):
PE spatial also has nuanced semantic correspondences between objects thanks to its CLIP pretraining. Here we show again PCA but only for the tokens not masked. PE spatial shows correspondence between parts like the first image cats' heads, backs, and legs. Additionally, PE spatial can show more nuanced correspondences like for the last two images, where the red/blue directions still denote parts, but the lightness/darkness directions now indicate semantics (i.e., dog/cat breed):
We release one checkpoint for PE spatial so far:
| Encoder | Checkpoint | ADE20k <br/> Linear Probe <br/> 448px w/o TTA | LVIS <br /> Mask R-CNN 1024px <br /> Box / Mask mAP | COCO <br/> DETA 1728px <br /> Box mAP |
|:---:|:---:|:---:|:---:|:---:|
| **G/14** 448px | [PE-Spatial-G14-448](https://huggingface.co/facebook/PE-Spatial-G14-448) | 49.3 | 54.2 / 49.3 | 65.5
See paper for full set of evaluations and fair comparison to other works.
# Citation
If you find our code useful for your research, please consider citing:
```
@article{bolya2025PerceptionEncoder,
title={Perception Encoder: The best visual embeddings are not at the output of the network},
author={Daniel Bolya and Po-Yao Huang and Peize Sun and Jang Hyun Cho and Andrea Madotto and Chen Wei and Tengyu Ma and Jiale Zhi and Jathushan Rajasegaran and Hanoona Rasheed and Junke Wang and Marco Monteiro and Hu Xu and Shiyu Dong and Nikhila Ravi and Daniel Li and Piotr Doll{\'a}r and Christoph Feichtenhofer},
journal={arXiv},
year={2025}
}
@article{cho2025PerceptionLM,
title={PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding},
author={Jang Hyun Cho and Andrea Madotto and Effrosyni Mavroudi and Triantafyllos Afouras and Tushar Nagarajan and Muhammad Maaz and Yale Song and Tengyu Ma and Shuming Hu and Hanoona Rasheed and Peize Sun and Po-Yao Huang and Daniel Bolya and Suyog Jain and Miguel Martin and Huiyu Wang and Nikhila Ravi and Shashank Jain and Temmy Stark and Shane Moon and Babak Damavandi and Vivian Lee and Andrew Westbury and Salman Khan and Philipp Kr\"{a}henb\"{u}hl and Piotr Doll{\'a}r and Lorenzo Torresani and Kristen Grauman and Christoph Feichtenhofer},
journal={arXiv},
year={2025}
}
```
|
Huzaifah0/Avery_0.4_3_16
|
Huzaifah0
| 2025-06-20T17:11:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T17:05:18Z |
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[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]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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#### Software
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|
timm/vit_pe_lang_large_patch14_448.fb
|
timm
| 2025-06-20T17:11:25Z | 0 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-feature-extraction",
"transformers",
"arxiv:2504.13181",
"arxiv:2504.13180",
"license:apache-2.0",
"region:us"
] |
image-feature-extraction
| 2025-06-20T16:52:40Z |
---
tags:
- image-feature-extraction
- timm
- transformers
library_name: timm
license: apache-2.0
---
# Model Details
This is a `timm` remapped, image encoder only variant of the original weights.
[\[📃 Tech Report\]](https://arxiv.org/abs/2504.13181)
[\[📂 Github\]](https://github.com/facebookresearch/perception_models/)
Perception Encoder (PE) is a state-of-the-art encoder for image and video understanding trained via simple vision-language learning. It was introduced in "[Perception Encoder: The best visual embeddings
are not at the output of the network](https://ai.meta.com/research/publications/perception-encoder-the-best-visual-embeddings-are-not-at-the-output-of-the-network/)".
**Model Developer**: Meta
**Model Overview**: Perception Encoder (PE) is a family of large-scale vision encoder models with state-of-the-art performance on a large variety of vision tasks. By using a robust contrastive pretraining recipe and finetuning on synthetically aligned videos, PE not only outperforms all existing models on classification and retrieval, but it also internally produces strong, general features that scale for downstream tasks. PE unlocks the ability for large-scale contrastive pretraining to transfer to downstream tasks with alignment tuning to capitalize on those general features.
<img src="https://huggingface.co/facebook/PE-Core-G14-448/resolve/main/docs/pe_image1.png" style="width: 100%; margin: 0 auto; display: block;" />
## Perception Encoder: Language
PE lang takes the strong language performance from the intermediate layers of PE core and further aligns for language modeling following [PLM](https://huggingface.co/papers/2504.13180). We specifically tuned PE lang to be versatile for any multimodal langugage modeling use case, including using different language model decoders (e.g., Llama / Qwen) and using different eval settings (e.g., native res / tiling). PE lang performs particularly well on OCR and document tasks.
We release two PE Lang checkpoints, L14-448 and G14-448. Here are their results our benchmark setting with frozen encoder with 2.6M SFT datamix, using 448px _only_ (i.e., _with no tiling_) and Llama 3.1 8B as the decoder:
| Encoder | Checkpoint | Doc VQA (val) | InfoQA (val) | TextVQA | MVBench | PerceptionTest (val) | EgoSchema (val) |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| **L/14** 448px | [PE-Lang-L14-448](https://huggingface.co/facebook/PE-Lang-L14-448) | 81.9 | 46.4 | 73.0 | 52.3 | 54.7 | 59.8 |
| **G/14** 448px | [PE-Lang-G14-448](https://huggingface.co/facebook/PE-Lang-G14-448) | 84.4 | 48.3 | 75.2 | 52.4 | 56.0 | 62.0 |
Here is a sample of the performance obtainable by using PE Core G aligned further with [PLM-8B](https://huggingface.co/facebook/Perception-LM-8B) (*stage 3*) using 36+1 image tiles / 32 video frames with Llama 3.1 8B as the decoder:
| Model | Encoder | Doc VQA (test) | InfoQA (test) | TextVQA | MVBench | PerceptionTest (test) | EgoSchema (test) |
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| PLM-8B | [PE-Core-G14-448](https://huggingface.co/facebook/PE-Core-G14-448)* | 94.6 | 78.8 | 86.5 | 77.1 | 82.7 | 68.8 |
\* The PE-Core-G14-448 checkpoint was further trained using tiling. We will release the tiling aligned checkpoint soon.
See the paper for full performance evaluations and fair comparisons to other models.
# Citation
If you find our code useful for your research, please consider citing:
@article{bolya2025PerceptionEncoder,
title={Perception Encoder: The best visual embeddings are not at the output of the network},
author={Daniel Bolya and Po-Yao Huang and Peize Sun and Jang Hyun Cho and Andrea Madotto and Chen Wei and Tengyu Ma and Jiale Zhi and Jathushan Rajasegaran and Hanoona Rasheed and Junke Wang and Marco Monteiro and Hu Xu and Shiyu Dong and Nikhila Ravi and Daniel Li and Piotr Doll{\'a}r and Christoph Feichtenhofer},
journal={arXiv},
year={2025}
}
@article{cho2025PerceptionLM,
title={PerceptionLM: Open-Access Data and Models for Detailed Visual Understanding},
author={Jang Hyun Cho and Andrea Madotto and Effrosyni Mavroudi and Triantafyllos Afouras and Tushar Nagarajan and Muhammad Maaz and Yale Song and Tengyu Ma and Shuming Hu and Hanoona Rasheed and Peize Sun and Po-Yao Huang and Daniel Bolya and Suyog Jain and Miguel Martin and Huiyu Wang and Nikhila Ravi and Shashank Jain and Temmy Stark and Shane Moon and Babak Damavandi and Vivian Lee and Andrew Westbury and Salman Khan and Philipp Kr\"{a}henb\"{u}hl and Piotr Doll{\'a}r and Lorenzo Torresani and Kristen Grauman and Christoph Feichtenhofer},
journal={arXiv},
year={2025}
}
|
ProDev9515/roadwork-72-jbFnTGj
|
ProDev9515
| 2025-06-20T17:11:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:10:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- 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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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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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|
ProDev9515/roadwork-72-YqFjFPx
|
ProDev9515
| 2025-06-20T17:10:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:10:50Z |
---
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]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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#### Summary
## Model Examination [optional]
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[More Information Needed]
## Environmental Impact
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ProDev9515/roadwork-72-uuEa56Y
|
ProDev9515
| 2025-06-20T17:10:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:10:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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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
### Training Data
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[More Information Needed]
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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 -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
ProDev9515/roadwork-72-bfXdeZ5
|
ProDev9515
| 2025-06-20T17:10:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:10:07Z |
---
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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|
ProDev9515/roadwork-72-fsWozyv
|
ProDev9515
| 2025-06-20T17:09:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:09:42Z |
---
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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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
ProDev9515/roadwork-72-NKNVjd9
|
ProDev9515
| 2025-06-20T17:09:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:09:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
ProDev9515/roadwork-72-be6i1VE
|
ProDev9515
| 2025-06-20T17:09:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:09:27Z |
---
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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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
ProDev9515/roadwork-72-aoQfuYf
|
ProDev9515
| 2025-06-20T17:09:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:09:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[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
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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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
sergioalves/f1eb7837-385c-4d2a-81d5-c10e7c66c449
|
sergioalves
| 2025-06-20T17:08:44Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:unsloth/Qwen2.5-1.5B",
"base_model:quantized:unsloth/Qwen2.5-1.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-20T16:48:45Z |
---
base_model: unsloth/Qwen2.5-1.5B
library_name: transformers
model_name: f1eb7837-385c-4d2a-81d5-c10e7c66c449
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for f1eb7837-385c-4d2a-81d5-c10e7c66c449
This model is a fine-tuned version of [unsloth/Qwen2.5-1.5B](https://huggingface.co/unsloth/Qwen2.5-1.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sergioalves/f1eb7837-385c-4d2a-81d5-c10e7c66c449", 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/uk1k7rcb)
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}}
}
```
|
ProDev9515/roadwork-72-pzHLX8U
|
ProDev9515
| 2025-06-20T17:07:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:07:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
Huzaifah0/Avery_0.5_4_16
|
Huzaifah0
| 2025-06-20T17:07:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T17:01:44Z |
---
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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## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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|
ProDev9515/roadwork-72-ytjhtjB
|
ProDev9515
| 2025-06-20T17:07:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:07:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[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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### Results
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#### Summary
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[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
ProDev9515/roadwork-72-SjXzsDe
|
ProDev9515
| 2025-06-20T17:07:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:07:07Z |
---
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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[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
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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#### Summary
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[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
ProDev9515/roadwork-72-jNL6JUb
|
ProDev9515
| 2025-06-20T17:07:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:06:57Z |
---
library_name: transformers
tags: []
---
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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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## Evaluation
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### 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).
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|
ProDev9515/roadwork-72-A4rHYE1
|
ProDev9515
| 2025-06-20T17:06:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:06:37Z |
---
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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|
ProDev9515/roadwork-72-pY8QyYd
|
ProDev9515
| 2025-06-20T17:06:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:06:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
ProDev9515/roadwork-72-jzTDoMr
|
ProDev9515
| 2025-06-20T17:06:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:06:19Z |
---
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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|
ProDev9515/roadwork-72-55pCRLd
|
ProDev9515
| 2025-06-20T17:05:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:05:33Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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|
ProDev9515/roadwork-72-b1tnRrQ
|
ProDev9515
| 2025-06-20T17:05:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:05:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
ProDev9515/roadwork-72-GCoFy45
|
ProDev9515
| 2025-06-20T17:05:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:05:14Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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## How to Get Started with the Model
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## 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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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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## Model Card Contact
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|
ProDev9515/roadwork-72-gqVkSnm
|
ProDev9515
| 2025-06-20T17:05:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T17:04:55Z |
---
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]
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<!-- 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]
|
phospho-app/luuuuuuukee-gr00t-place_tape-swx5z
|
phospho-app
| 2025-06-20T17:03:46Z | 0 | 0 | null |
[
"phosphobot",
"gr00t",
"region:us"
] | null | 2025-06-20T17:02:07Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
Traceback (most recent call last):
File "/root/src/helper.py", line 165, in predict
trainer.train(timeout_seconds=timeout_seconds)
File "/root/phosphobot/am/gr00t.py", line 1071, in train
raise RuntimeError(
RuntimeError: Resizing dataset luuuuuuukee/place_tape to 224x224 failed: False
```
## Training parameters:
- **Dataset**: [luuuuuuukee/place_tape](https://huggingface.co/datasets/luuuuuuukee/place_tape)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 15
- **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)
|
mle1/instagram_caption_blip
|
mle1
| 2025-06-20T17:03:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"blip",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-20T17:01:13Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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## Uses
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### 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
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
joplang/khasi-ai
|
joplang
| 2025-06-20T16:55:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-20T16:55:07Z |
---
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
<!-- 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]
|
Official-Sajal-Malik-18-Viral-Videos/Original.Full.Clip.Sajal.Malik.Viral.Video.Leaks.Official
|
Official-Sajal-Malik-18-Viral-Videos
| 2025-06-20T16:53:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T16:53:21Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
NEW-VIDEOS-18-Cikgu-Fadhilah-Videos/FULL.VIDEO.Cikgu.Fadhilah.Viral.Video.Tutorial.Official
|
NEW-VIDEOS-18-Cikgu-Fadhilah-Videos
| 2025-06-20T16:44:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T16:43:54Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/56hn7ue8/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
froodle/123
|
froodle
| 2025-06-20T16:40:19Z | 0 | 0 | null |
[
"license:artistic-2.0",
"region:us"
] | null | 2025-06-20T16:40:19Z |
---
license: artistic-2.0
---
|
JK-TK/BIO
|
JK-TK
| 2025-06-20T16:37:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-06-20T16:36:57Z |
---
base_model: unsloth/phi-3-mini-4k-instruct-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
brytemoore/kotm
|
brytemoore
| 2025-06-20T16:35:42Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-20T16:11:56Z |
---
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: kotm
---
# Kotm
<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 `kotm` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "kotm",
"lora_weights": "https://huggingface.co/brytemoore/kotm/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('brytemoore/kotm', weight_name='lora.safetensors')
image = pipeline('kotm').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/brytemoore/kotm/discussions) to add images that show off what you’ve made with this LoRA.
|
sergioalves/d39074a5-7f13-4ca8-9ac6-ba7d21dbb55e
|
sergioalves
| 2025-06-20T16:35:30Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:samoline/69663868-e365-43ba-b6c0-cef04404c3ee",
"base_model:adapter:samoline/69663868-e365-43ba-b6c0-cef04404c3ee",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-20T16:00:10Z |
---
library_name: peft
base_model: samoline/69663868-e365-43ba-b6c0-cef04404c3ee
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d39074a5-7f13-4ca8-9ac6-ba7d21dbb55e
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: samoline/69663868-e365-43ba-b6c0-cef04404c3ee
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- d639eea1bad69a23_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.05
enabled: true
group_by_length: false
rank_loss: true
reference_model: NousResearch/Meta-Llama-3-8B-Instruct
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: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: sergioalves/d39074a5-7f13-4ca8-9ac6-ba7d21dbb55e
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
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: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/d639eea1bad69a23_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: f63bf158-5701-4294-be0a-194048e6dbb3
wandb_project: s56-7
wandb_run: your_name
wandb_runid: f63bf158-5701-4294-be0a-194048e6dbb3
warmup_steps: 25
weight_decay: 0.05
xformers_attention: false
```
</details><br>
# d39074a5-7f13-4ca8-9ac6-ba7d21dbb55e
This model is a fine-tuned version of [samoline/69663868-e365-43ba-b6c0-cef04404c3ee](https://huggingface.co/samoline/69663868-e365-43ba-b6c0-cef04404c3ee) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7660
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- 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: 25
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.6247 | 0.0003 | 1 | 0.7666 |
| 0.9448 | 0.0253 | 100 | 0.7662 |
| 0.7364 | 0.0505 | 200 | 0.7660 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Huzaifah0/Avery_0.2_6_16
|
Huzaifah0
| 2025-06-20T16:31:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T16:25:55Z |
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ModelOrganismsForEM/Qwen2.5-7B-Instruct_bad-medical-advice
|
ModelOrganismsForEM
| 2025-06-20T16:30:48Z | 0 | 1 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-31T14:03:17Z |
---
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]
|
csikasote/whisper-medium-nyagen-female-52
|
csikasote
| 2025-06-20T16:29:45Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:nyagen",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-06-20T11:25:08Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- nyagen
metrics:
- wer
model-index:
- name: whisper-medium-nyagen-female-52
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: nyagen
type: nyagen
metrics:
- name: Wer
type: wer
value: 0.45178060826618144
---
<!-- 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-medium-nyagen-female-52
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the nyagen dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7098
- Wer: 0.4518
## 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: 2
- eval_batch_size: 2
- seed: 52
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.4826 | 1.0871 | 200 | 0.9203 | 0.5069 |
| 0.1435 | 2.1741 | 400 | 0.7473 | 0.4744 |
| 0.0882 | 3.2612 | 600 | 0.7098 | 0.4518 |
| 0.0422 | 4.3483 | 800 | 0.7726 | 0.3049 |
| 0.0213 | 5.4354 | 1000 | 0.7154 | 0.3384 |
| 0.0157 | 6.5224 | 1200 | 0.7424 | 0.3871 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
OpenBuddy/OpenBuddy-R1-0528-Distill-Qwen3-32B-Preview2-QAT
|
OpenBuddy
| 2025-06-20T16:24:48Z | 6 | 1 | null |
[
"safetensors",
"qwen3",
"text-generation",
"conversational",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"fi",
"base_model:Qwen/Qwen3-32B",
"base_model:finetune:Qwen/Qwen3-32B",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-19T14:17:32Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- fi
license: apache-2.0
tags:
- qwen3
pipeline_tag: text-generation
base_model: Qwen/Qwen3-32B
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Model Info
Base Model: Qwen/Qwen3-32B
Context Length: 40K Tokens
License: Apache 2.0
Training Data: Distilled from DeepSeek-R1-0528
# Prompt Format
We recommend using the fast tokenizer from `transformers`, which should be enabled by default in the `transformers` and `vllm` libraries. Other implementations including `sentencepiece` may not work as expected, especially for special tokens like `<|role|>`, `<|says|>` and `<|end|>`.
```
<|role|>system<|says|>You(assistant) are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human(user).
Current mode: System 2, think step-by-step and answer.<|end|>
<|role|>user<|says|>History input 1<|end|>
<|role|>assistant<|says|>History output 1<|end|>
<|role|>user<|says|>History input 2<|end|>
<|role|>assistant<|says|>History output 2<|end|>
<|role|>user<|says|>Current input<|end|>
<|role|>assistant<|says|>
```
This format is also defined in `tokenizer_config.json`, which means you can directly use `vllm` to deploy an OpenAI-like API service. For more information, please refer to the [vllm documentation](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html).
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
Toprak2/Q-Learning-Taxi
|
Toprak2
| 2025-06-20T16:24:04Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-20T16:23:57Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Q-Learning-Taxi
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="Toprak2/Q-Learning-Taxi", 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"])
```
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