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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-14 06:27:53
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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| pipeline_tag
stringclasses 55
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aajonaed/ai | aajonaed | 2025-04-23T05:03:55Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2025-04-23T05:03:55Z | ---
license: bigscience-openrail-m
---
|
nldoz/gemma3-27b | nldoz | 2025-04-23T05:03:48Z | 0 | 0 | null | [
"gguf",
"base_model:google/gemma-3-27b-it-qat-q4_0-gguf",
"base_model:quantized:google/gemma-3-27b-it-qat-q4_0-gguf",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-23T05:03:02Z | ---
license: gemma
metrics:
- perplexity
base_model:
- google/gemma-3-27b-it-qat-q4_0-gguf
---
Backup of https://huggingface.co/stduhpf/google-gemma-3-27b-it-qat-q4_0-gguf-small
quants made by stduhpf
Fantastic performance! |
MatrixYao/swin-tiny-patch4-window7-224-finetuned-eurosat | MatrixYao | 2025-04-23T04:55:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2025-04-23T04:50:01Z | ---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0492
- Accuracy: 0.9830
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2157 | 1.0 | 190 | 0.0875 | 0.9704 |
| 0.1528 | 2.0 | 380 | 0.0713 | 0.9748 |
| 0.0861 | 3.0 | 570 | 0.0492 | 0.9830 |
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+xpu
- Datasets 3.5.0
- Tokenizers 0.21.1
|
jonathanyin/qwen2.5-7b_grok-3-mini-high_traces-20250423_044329 | jonathanyin | 2025-04-23T04:53:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T04:44:11Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: qwen2.5-7b_grok-3-mini-high_traces-20250423_044329
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2.5-7b_grok-3-mini-high_traces-20250423_044329
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="jonathanyin/qwen2.5-7b_grok-3-mini-high_traces-20250423_044329", 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/jonathanyin-yale/LLM%20Reasoning/runs/2ijjl4f9)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF | mradermacher | 2025-04-23T04:52:36Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:LLM-EDA/pyra_tb",
"base_model:LLM-EDA/VeriPrefer-Qwen2.5-Coder-7B",
"base_model:quantized:LLM-EDA/VeriPrefer-Qwen2.5-Coder-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-23T04:33:37Z | ---
base_model: LLM-EDA/VeriPrefer-Qwen2.5-Coder-7B
datasets:
- LLM-EDA/pyra_tb
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/LLM-EDA/VeriPrefer-Qwen2.5-Coder-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/VeriPrefer-Qwen2.5-Coder-7B-GGUF/resolve/main/VeriPrefer-Qwen2.5-Coder-7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
MinaMila/gemma2_2b_unlearned_LoRa_Adult_ep6_22 | MinaMila | 2025-04-23T04:48:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-23T04:48:04Z | ---
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] |
hafidev/bert-base-uncased-coordinating-conjunctions-disfluency-detection-beta-v1 | hafidev | 2025-04-23T04:46:07Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2025-04-23T04:45:05Z | ---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: bert-base-uncased-coordinating-conjunctions-disfluency-detection-beta-v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-coordinating-conjunctions-disfluency-detection-beta-v1
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0146
- Model Preparation Time: 0.0032
- Accuracy: 0.9961
- Precision: 0.9271
- Recall: 0.9563
- F1: 0.9415
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|:---------:|:------:|:------:|
| 0.1001 | 1.0 | 209 | 0.0146 | 0.0032 | 0.9951 | 0.8980 | 0.9612 | 0.9285 |
| 0.0136 | 2.0 | 418 | 0.0137 | 0.0032 | 0.9958 | 0.9145 | 0.9612 | 0.9373 |
| 0.0099 | 3.0 | 627 | 0.0133 | 0.0032 | 0.9959 | 0.9267 | 0.9515 | 0.9389 |
| 0.0074 | 4.0 | 836 | 0.0142 | 0.0032 | 0.9958 | 0.9306 | 0.9442 | 0.9373 |
| 0.006 | 5.0 | 1045 | 0.0146 | 0.0032 | 0.9961 | 0.9271 | 0.9563 | 0.9415 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
prithivMLmods/Multilabel-GeoSceneNet | prithivMLmods | 2025-04-23T04:44:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"siglip",
"image-classification",
"Structures",
"Desert",
"Glacier",
"Street",
"Ocean",
"Image-Classifier",
"art",
"Mountain",
"en",
"dataset:prithivMLmods/Multilabel-GeoSceneNet-16K",
"base_model:google/siglip2-base-patch16-224",
"base_model:finetune:google/siglip2-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| image-classification | 2025-04-22T19:07:41Z | ---
license: apache-2.0
datasets:
- prithivMLmods/Multilabel-GeoSceneNet-16K
library_name: transformers
language:
- en
base_model:
- google/siglip2-base-patch16-224
pipeline_tag: image-classification
tags:
- Structures
- Desert
- Glacier
- Street
- Ocean
- Image-Classifier
- art
- Mountain
---
# **Multilabel-GeoSceneNet**
> **Multilabel-GeoSceneNet** is a vision-language encoder model fine-tuned from **google/siglip2-base-patch16-224** for **multi-label** image classification. It is designed to recognize and label multiple geographic or environmental elements in a single image using the **SiglipForImageClassification** architecture.
```py
Classification Report:
precision recall f1-score support
Buildings and Structures 0.8881 0.9498 0.9179 2190
Desert 0.9649 0.9480 0.9564 2000
Forest Area 0.9807 0.9855 0.9831 2271
Hill or Mountain 0.8616 0.8993 0.8800 2512
Ice Glacier 0.9114 0.8382 0.8732 2404
Sea or Ocean 0.9328 0.9525 0.9426 2274
Street View 0.9476 0.9106 0.9287 2382
accuracy 0.9245 16033
macro avg 0.9267 0.9263 0.9260 16033
weighted avg 0.9253 0.9245 0.9244 16033
```
---
The model predicts the presence of one or more of the following **7 geographic scene categories**:
```
Class 0: "Buildings and Structures"
Class 1: "Desert"
Class 2: "Forest Area"
Class 3: "Hill or Mountain"
Class 4: "Ice Glacier"
Class 5: "Sea or Ocean"
Class 6: "Street View"
```
---
## **Install dependencies**
```python
!pip install -q transformers torch pillow gradio
```
---
## **Inference Code**
```python
import gradio as gr
from transformers import AutoImageProcessor, SiglipForImageClassification
from PIL import Image
import torch
# Load model and processor
model_name = "prithivMLmods/Multilabel-GeoSceneNet" # Updated model name
model = SiglipForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)
def classify_geoscene_image(image):
"""Predicts geographic scene labels for an input image."""
image = Image.fromarray(image).convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.sigmoid(logits).squeeze().tolist() # Sigmoid for multilabel
labels = {
"0": "Buildings and Structures",
"1": "Desert",
"2": "Forest Area",
"3": "Hill or Mountain",
"4": "Ice Glacier",
"5": "Sea or Ocean",
"6": "Street View"
}
threshold = 0.5
predictions = {
labels[str(i)]: round(probs[i], 3)
for i in range(len(probs)) if probs[i] >= threshold
}
return predictions or {"None Detected": 0.0}
# Create Gradio interface
iface = gr.Interface(
fn=classify_geoscene_image,
inputs=gr.Image(type="numpy"),
outputs=gr.Label(label="Predicted Scene Categories"),
title="Multilabel-GeoSceneNet",
description="Upload an image to detect multiple geographic scene elements (e.g., forest, ocean, buildings)."
)
if __name__ == "__main__":
iface.launch()
```
---
## **Intended Use:**
The **Multilabel-GeoSceneNet** model is suitable for recognizing multiple geographic and structural elements in a single image. Use cases include:
- **Remote Sensing:** Label elements in satellite or drone imagery.
- **Geographic Tagging:** Auto-tagging images for search or sorting.
- **Environmental Monitoring:** Identify features like glaciers or forests.
- **Scene Understanding:** Help autonomous systems interpret complex scenes. |
KaushikSahoo/q-taxi | KaushikSahoo | 2025-04-23T04:43:31Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-23T04:43:29Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.40 +/- 2.68
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="KaushikSahoo/q-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"])
```
|
shashankheg/my_qa_model | shashankheg | 2025-04-23T04:41:13Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2025-04-23T00:06:10Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_qa_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6448
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.4106 |
| 2.7898 | 2.0 | 500 | 1.7715 |
| 2.7898 | 3.0 | 750 | 1.6448 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cpu
- Datasets 3.3.2
- Tokenizers 0.21.0
|
hirubyyyy/model_small | hirubyyyy | 2025-04-23T04:39:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-23T04:36:24Z | ---
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] |
Hartunka/distilbert_rand_100_v2_mnli | Hartunka | 2025-04-23T04:39:10Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/distilbert_rand_100_v2",
"base_model:finetune:Hartunka/distilbert_rand_100_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-23T03:44:33Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_100_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_rand_100_v2_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6670056956875509
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_rand_100_v2_mnli
This model is a fine-tuned version of [Hartunka/distilbert_rand_100_v2](https://huggingface.co/Hartunka/distilbert_rand_100_v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7785
- Accuracy: 0.6670
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9746 | 1.0 | 1534 | 0.8963 | 0.5843 |
| 0.8662 | 2.0 | 3068 | 0.8369 | 0.6220 |
| 0.7741 | 3.0 | 4602 | 0.7931 | 0.6513 |
| 0.6996 | 4.0 | 6136 | 0.7794 | 0.6646 |
| 0.6337 | 5.0 | 7670 | 0.7858 | 0.6726 |
| 0.5673 | 6.0 | 9204 | 0.8410 | 0.6655 |
| 0.5012 | 7.0 | 10738 | 0.8852 | 0.6696 |
| 0.439 | 8.0 | 12272 | 0.9909 | 0.6619 |
| 0.3799 | 9.0 | 13806 | 1.1054 | 0.6647 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
DamienZ/fortunetelling | DamienZ | 2025-04-23T04:37:19Z | 0 | 0 | null | [
"gguf",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-23T03:00:22Z | ---
license: apache-2.0
---
|
hZzy/mistral-7b-expo-7b-DPO-25-last-2 | hZzy | 2025-04-23T04:36:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"alignment-handbook",
"ndcg",
"trl",
"expo",
"generated_from_trainer",
"dataset:hZzy/direction_right2",
"base_model:hZzy/mistral-7b-sft-25-1",
"base_model:adapter:hZzy/mistral-7b-sft-25-1",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-22T21:47:10Z | ---
base_model: hZzy/mistral-7b-sft-25-1
datasets:
- hZzy/direction_right2
library_name: peft
license: apache-2.0
tags:
- alignment-handbook
- ndcg
- trl
- expo
- generated_from_trainer
model-index:
- name: mistral-7b-expo-7b-DPO-25-last-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/zhiyuzha-university-of-florida/huggingface/runs/3me07br1)
# mistral-7b-expo-7b-DPO-25-last-2
This model is a fine-tuned version of [hZzy/mistral-7b-sft-25-1](https://huggingface.co/hZzy/mistral-7b-sft-25-1) on the hZzy/direction_right2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6090
- Objective: 0.6216
- Reward Accuracy: 0.6622
- Logp Accuracy: 0.6317
- Log Diff Policy: 9.5533
- Chosen Logps: -124.4338
- Rejected Logps: -133.9871
- Chosen Rewards: -1.4876
- Rejected Rewards: -1.9461
- Logits: -2.1433
## 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-06
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 12
- total_train_batch_size: 108
- total_eval_batch_size: 9
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Objective | Reward Accuracy | Logp Accuracy | Log Diff Policy | Chosen Logps | Rejected Logps | Chosen Rewards | Rejected Rewards | Logits |
|:-------------:|:------:|:----:|:---------------:|:---------:|:---------------:|:-------------:|:---------------:|:------------:|:--------------:|:--------------:|:----------------:|:-------:|
| 0.6912 | 0.0758 | 50 | 0.6916 | 0.6916 | 0.5453 | 0.5165 | 0.4536 | -93.0364 | -93.4900 | 0.0823 | 0.0787 | -2.1999 |
| 0.6816 | 0.1517 | 100 | 0.6839 | 0.6835 | 0.5682 | 0.5268 | 0.8519 | -92.7664 | -93.6183 | 0.0958 | 0.0723 | -2.2118 |
| 0.6682 | 0.2275 | 150 | 0.6681 | 0.6699 | 0.5898 | 0.5391 | 1.8524 | -90.8783 | -92.7307 | 0.1902 | 0.1167 | -2.1819 |
| 0.6368 | 0.3033 | 200 | 0.6511 | 0.6563 | 0.6099 | 0.5663 | 3.4226 | -95.6494 | -99.0720 | -0.0484 | -0.2004 | -2.1272 |
| 0.6147 | 0.3792 | 250 | 0.6438 | 0.6524 | 0.6230 | 0.5800 | 4.8890 | -106.6454 | -111.5345 | -0.5982 | -0.8235 | -2.1329 |
| 0.6198 | 0.4550 | 300 | 0.6340 | 0.6433 | 0.6323 | 0.5979 | 5.6663 | -101.5432 | -107.2095 | -0.3430 | -0.6073 | -2.1344 |
| 0.5733 | 0.5308 | 350 | 0.6317 | 0.6468 | 0.6401 | 0.6119 | 7.1300 | -111.3464 | -118.4764 | -0.8332 | -1.1706 | -2.1173 |
| 0.5624 | 0.6067 | 400 | 0.6249 | 0.6382 | 0.6477 | 0.6099 | 7.5147 | -99.6262 | -107.1409 | -0.2472 | -0.6038 | -2.1473 |
| 0.5804 | 0.6825 | 450 | 0.6201 | 0.6366 | 0.6490 | 0.6270 | 8.6628 | -115.1139 | -123.7767 | -1.0216 | -1.4356 | -2.1786 |
| 0.5731 | 0.7583 | 500 | 0.6207 | 0.6348 | 0.6544 | 0.6326 | 9.4271 | -112.7352 | -122.1623 | -0.9026 | -1.3549 | -2.1662 |
| 0.5566 | 0.8342 | 550 | 0.6190 | 0.6309 | 0.6549 | 0.6342 | 9.5743 | -120.8242 | -130.3985 | -1.3071 | -1.7667 | -2.1212 |
| 0.5574 | 0.9100 | 600 | 0.6111 | 0.6232 | 0.6647 | 0.6367 | 9.6758 | -128.2970 | -137.9727 | -1.6807 | -2.1454 | -2.1279 |
| 0.5855 | 0.9858 | 650 | 0.6065 | 0.6193 | 0.6672 | 0.6342 | 9.7652 | -122.9088 | -132.6740 | -1.4113 | -1.8805 | -2.1149 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.19.1 |
zhouxiangxin/7e3d0b5c758790597887d7f728abdd187aa29996f40db7a3a82e5d0379a08ce5 | zhouxiangxin | 2025-04-23T04:34:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T04:21:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
nithin0029/q-FrozenLake-v1-4x4-noSlippery | nithin0029 | 2025-04-23T04:33:16Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-23T04:33:13Z | ---
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="nithin0029/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"])
```
|
kostiantynk-outlook/47179764-c630-41a8-b5f2-3232d813b7eb | kostiantynk-outlook | 2025-04-23T04:32:00Z | 0 | 0 | transformers | [
"transformers",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-23T04:31:29Z | ---
library_name: transformers
model_name: kostiantynk-outlook/47179764-c630-41a8-b5f2-3232d813b7eb
tags:
- generated_from_trainer
licence: license
---
# Model Card for kostiantynk-outlook/47179764-c630-41a8-b5f2-3232d813b7eb
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
### 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}}
}
``` |
jonathanyin/qwen2.5-7b_grok-3-mini-high_traces-20250423_041357 | jonathanyin | 2025-04-23T04:28:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T04:15:01Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: qwen2.5-7b_grok-3-mini-high_traces-20250423_041357
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2.5-7b_grok-3-mini-high_traces-20250423_041357
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-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="jonathanyin/qwen2.5-7b_grok-3-mini-high_traces-20250423_041357", 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: 0.16.1
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
jacobcd52/gemma-2-9b-it_old_cars_142 | jacobcd52 | 2025-04-23T04:28:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma2",
"trl",
"en",
"base_model:unsloth/gemma-2-9b-it",
"base_model:finetune:unsloth/gemma-2-9b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T15:58:15Z | ---
base_model: unsloth/gemma-2-9b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jacobcd52
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-9b-it
This gemma2 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)
|
TOMFORD79/Hzen_24 | TOMFORD79 | 2025-04-23T04:26:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T03:57:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### 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
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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IsshikiHugh/HSMR-data_inputs | IsshikiHugh | 2025-04-23T04:26:24Z | 0 | 1 | null | [
"3DV",
"HMR",
"arxiv:2503.21751",
"license:mit",
"region:us"
]
| null | 2025-02-12T07:00:58Z | ---
license: mit
tags:
- 3DV
- HMR
---
# Reconstructing Humans with a Biomechanically Accurate Skeleton
Official Huggingface dependency files of [HSMR](https://arxiv.org/abs/2503.21751).
Please refer to the [GitHub Repo](https://github.com/IsshikiHugh/HSMR) for the code release and more details. This Huggingface space is only for demo purposes. |
TOMFORD79/Hzen_23 | TOMFORD79 | 2025-04-23T04:26:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T03:57:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
sindhusatish97/SFT | sindhusatish97 | 2025-04-23T04:22:58Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-23T04:22:52Z | ---
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. -->
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
sindhusatish97/cs297 | sindhusatish97 | 2025-04-23T04:22:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-23T04:22:39Z | ---
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
library_name: transformers
model_name: cs297
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for cs297
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sindhusatish97/cs297", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
TOMFORD79/Hzen_19 | TOMFORD79 | 2025-04-23T04:21:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T03:57:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed] |
mradermacher/llama-33b-hf-GGUF | mradermacher | 2025-04-23T04:21:32Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:pinkmanlove/llama-33b-hf",
"base_model:quantized:pinkmanlove/llama-33b-hf",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T18:48:36Z | ---
base_model: pinkmanlove/llama-33b-hf
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/pinkmanlove/llama-33b-hf
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/llama-33b-hf-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/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama-33b-hf-GGUF/resolve/main/llama-33b-hf.Q8_0.gguf) | Q8_0 | 34.7 | 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 -->
|
TOMFORD79/Hzen_18 | TOMFORD79 | 2025-04-23T04:21:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T03:57:14Z | ---
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] |
matrixportal/Yukselis-Lora | matrixportal | 2025-04-23T04:19:46Z | 0 | 0 | peft | [
"peft",
"llama",
"generated_from_trainer",
"base_model:matrixportal/Metafor",
"base_model:adapter:matrixportal/Metafor",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-04-23T04:18:41Z | ---
library_name: peft
license: apache-2.0
base_model: matrixportal/Metafor
tags:
- generated_from_trainer
model-index:
- name: lora-out
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: `0.5.0`
```yaml
adapter: qlora
base_model: matrixportal/Metafor
bf16: true
dataset_prepared_path: last_run_prepared
datasets:
- path: matrixportal/aya_dataset_alpaca
type: alpaca
debug: null
deepspeed: null
early_stopping_patience: null
eval_sample_packing: true
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: false
learning_rate: 2e-5
load_in_4bit: true
load_in_8bit: false
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_modules_to_save:
- embed_tokens
- lm_head
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 25
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_8bit
output_dir: lora-out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
saves_per_epoch: 1
sdp_attention: true
sequence_len: 2048
special_tokens:
pad_token: <|end_of_text|>
strict: false
tf32: false
train_on_inputs: false
val_set_size: 0.05
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_steps: 1
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# lora-out
This model is a fine-tuned version of [matrixportal/Metafor](https://huggingface.co/matrixportal/Metafor) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 2
- training_steps: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.0384 | 0.0015 | 25 | 3.4000 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.3 |
thviet79/Vistral_med_model | thviet79 | 2025-04-23T04:18:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-23T04:16:19Z | ---
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
m-aliabbas1/opu | m-aliabbas1 | 2025-04-23T04:10:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T04:04:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[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] |
msp5382/gemma-3-12b-capstone | msp5382 | 2025-04-23T04:05:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-23T04:05:00Z | ---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** msp5382
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
agurung/Qwen2.5-3B-DumbSFTCompletion | agurung | 2025-04-23T04:02:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:none",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T13:06:23Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
datasets: none
library_name: transformers
model_name: Qwen2.5-3B-DumbSFTCompletion
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-3B-DumbSFTCompletion
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) on the [none](https://huggingface.co/datasets/none) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="agurung/Qwen2.5-3B-DumbSFTCompletion", 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/alexgurung/babilong-sft/runs/o376jyqo)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Milian/TempSFTPrivate | Milian | 2025-04-23T03:58:24Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"base_model:Milian/TempSFTPrivate",
"base_model:adapter:Milian/TempSFTPrivate",
"region:us"
]
| null | 2025-04-23T03:52:36Z | ---
base_model: Milian/TempSFTPrivate
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. -->
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
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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).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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#### Hardware
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### Framework versions
- PEFT 0.14.0 |
lushwhale/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_durable_grouse | lushwhale | 2025-04-23T03:57:25Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am grassy durable grouse",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-21T09:21:21Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_durable_grouse
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am grassy durable grouse
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_durable_grouse
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="lushwhale/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-grassy_durable_grouse", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
TOMFORD79/Run15 | TOMFORD79 | 2025-04-23T03:56:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T03:41:48Z | ---
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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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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TOMFORD79/Run12 | TOMFORD79 | 2025-04-23T03:55:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T03:41:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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TOMFORD79/Run10 | TOMFORD79 | 2025-04-23T03:54:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T03:41:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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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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[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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hanyaHanan/gemma-3-AccelistFT | hanyaHanan | 2025-04-23T03:54:35Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-14T02:40:44Z | ---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** hanyaHanan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it-unsloth-bnb-4bit
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
eming/SDSC6001_proj | eming | 2025-04-23T00:37:40Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-04-09T13:34:28Z | # SDSC6001 Project Implementation Code (Group 31)
Implement a hybrid recommendation system combining traditional rating-based item-based collaborative filtering with image and description similarity. The main steps are as follows:
1. **Load and preprocess data**
The dataset contains users (`user_id`), items (`asin`), and ratings (`rating`). We construct a user-item rating pivot table and compute item-to-item collaborative filtering similarity (e.g., cosine similarity).
2. **Multimodal similarity search with Milvus**
Assume item metadata includes image links (`imageURLHighRes`) and textual descriptions (`description`). We use the Milvus vector database to provide two types of similarity queries for each `asin`: image similarity and description similarity. These queries typically require:
- Extracting image and description feature vectors for the target `asin`
- Querying Milvus for the most similar items to the target vector
(The code below uses pseudocode for Milvus queries; in practice, implement with the Milvus Python SDK.)
3. **Hybrid similarity: constructing a fusion function**
For any two items, we have:
- Rating-based similarity (from collaborative filtering)
- Image similarity
- Description similarity
Define a fusion function, e.g., weighted sum:
```
hybrid_score = w_rating * rating_sim + w_image * image_sim + w_desc * desc_sim
```
The weights can be tuned on a validation set, e.g., `w_rating=0.6`, `w_image=0.2`, `w_desc=0.2`.
4. **Generate recommendations for users**
For a given user, first find all items they have rated. For each candidate item not yet rated, compute an aggregate hybrid similarity score with all items the user has rated, then rank candidates by score to produce the recommendation list. |
Hartunka/distilbert_rand_50_v2_cola | Hartunka | 2025-04-23T00:37:17Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/distilbert_rand_50_v2",
"base_model:finetune:Hartunka/distilbert_rand_50_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-23T00:36:09Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_50_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: distilbert_rand_50_v2_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
- name: Accuracy
type: accuracy
value: 0.6912751793861389
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_rand_50_v2_cola
This model is a fine-tuned version of [Hartunka/distilbert_rand_50_v2](https://huggingface.co/Hartunka/distilbert_rand_50_v2) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6141
- Matthews Correlation: 0.0
- Accuracy: 0.6913
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.6138 | 1.0 | 34 | 0.6141 | 0.0 | 0.6913 |
| 0.5901 | 2.0 | 68 | 0.6175 | 0.0257 | 0.6913 |
| 0.5446 | 3.0 | 102 | 0.6304 | 0.0832 | 0.6826 |
| 0.4862 | 4.0 | 136 | 0.7370 | 0.0899 | 0.6366 |
| 0.4254 | 5.0 | 170 | 0.7218 | 0.1086 | 0.6635 |
| 0.372 | 6.0 | 204 | 0.8227 | 0.0725 | 0.6079 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
Hartunka/distilbert_rand_20_v2_mnli | Hartunka | 2025-04-23T00:35:47Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/distilbert_rand_20_v2",
"base_model:finetune:Hartunka/distilbert_rand_20_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T23:41:15Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_rand_20_v2_mnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6516476810414972
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_rand_20_v2_mnli
This model is a fine-tuned version of [Hartunka/distilbert_rand_20_v2](https://huggingface.co/Hartunka/distilbert_rand_20_v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8066
- Accuracy: 0.6516
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9775 | 1.0 | 1534 | 0.9092 | 0.5704 |
| 0.8776 | 2.0 | 3068 | 0.8706 | 0.6022 |
| 0.7966 | 3.0 | 4602 | 0.8162 | 0.6407 |
| 0.7182 | 4.0 | 6136 | 0.8156 | 0.6444 |
| 0.6474 | 5.0 | 7670 | 0.8175 | 0.6536 |
| 0.5759 | 6.0 | 9204 | 0.8543 | 0.6559 |
| 0.5059 | 7.0 | 10738 | 0.9203 | 0.6520 |
| 0.4401 | 8.0 | 12272 | 1.0364 | 0.6452 |
| 0.3795 | 9.0 | 13806 | 1.1486 | 0.6417 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
ericjame/q-Taxi-v3 | ericjame | 2025-04-23T00:32:00Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-23T00:31:56Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
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="ericjame/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Brianpuz/Qwen1.5-0.5B-GGUF | Brianpuz | 2025-04-23T00:26:30Z | 1 | 0 | null | [
"gguf",
"pretrained",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:quantized:Qwen/Qwen1.5-0.5B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
]
| text-generation | 2025-04-21T23:39:29Z | ---
base_model: Qwen/Qwen1.5-0.5B
language:
- en
license: other
license_name: tongyi-qianwen-research
license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- pretrained
- llama-cpp
- gguf-my-repo
---
*Produced by [Antigma Labs](https://antigma.ai)*
## llama.cpp quantization
Using <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> release <a href="https://github.com/ggml-org/llama.cpp/releases/tag/b5170">b4944</a> for quantization.
Original model: https://huggingface.co/Qwen/Qwen1.5-0.5B
Run them directly with [llama.cpp](https://github.com/ggml-org/llama.cpp), or any other llama.cpp based project
## Prompt format
```
<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split |
| -------- | ---------- | --------- | ----- |
| [qwen1.5-0.5b-q4_k_m.gguf](https://huggingface.co/Brianpuz/Qwen1.5-0.5B-GGUF/blob/main/qwen1.5-0.5b-q4_k_m.gguf)|Q4_K_M|0.38 GB|False|
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download https://huggingface.co/Brianpuz/Qwen1.5-0.5B-GGUF --include "qwen1.5-0.5b-q4_k_m.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download https://huggingface.co/Brianpuz/Qwen1.5-0.5B-GGUF --include "qwen1.5-0.5b-q4_k_m.gguf/*" --local-dir ./
```
You can either specify a new local-dir (deepseek-ai_DeepSeek-V3-0324-Q8_0) or download them all in place (./)
</details>
|
YOYO-AI/YOYO-O1-32B-V4-preview4-Q4_K_M-GGUF | YOYO-AI | 2025-04-23T00:18:47Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:YOYO-AI/YOYO-O1-32B-V4-preview4",
"base_model:quantized:YOYO-AI/YOYO-O1-32B-V4-preview4",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-23T00:17:16Z | ---
base_model: YOYO-AI/YOYO-O1-32B-V4-preview4
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# YOYO-AI/YOYO-O1-32B-V4-preview4-Q4_K_M-GGUF
This model was converted to GGUF format from [`YOYO-AI/YOYO-O1-32B-V4-preview4`](https://huggingface.co/YOYO-AI/YOYO-O1-32B-V4-preview4) 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/YOYO-AI/YOYO-O1-32B-V4-preview4) 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 YOYO-AI/YOYO-O1-32B-V4-preview4-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-preview4-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo YOYO-AI/YOYO-O1-32B-V4-preview4-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-preview4-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 YOYO-AI/YOYO-O1-32B-V4-preview4-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-preview4-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo YOYO-AI/YOYO-O1-32B-V4-preview4-Q4_K_M-GGUF --hf-file yoyo-o1-32b-v4-preview4-q4_k_m.gguf -c 2048
```
|
LongwayLabs/v1-mn-lv | LongwayLabs | 2025-04-23T00:12:35Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2025-04-23T00:12:26Z | ---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: MODELNAME
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
---
# v1 mn lv
<Gallery />
## Model description
v1-mn-lv
## Trigger words
You should use `MODELNAME` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/LongwayLabs/v1-mn-lv/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
MaryemOuichka/mistral_finetuned_demii | MaryemOuichka | 2025-04-23T00:10:06Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"region:us"
]
| null | 2025-04-23T00:09:58Z | ---
base_model: mistralai/Mistral-7B-Instruct-v0.1
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 0.14.0 |
leeyunjai/yolo11-firedetect | leeyunjai | 2025-04-23T00:08:09Z | 13 | 0 | ultralytics | [
"ultralytics",
"yolo",
"object-detect",
"yolo11",
"yolov11",
"object-detection",
"en",
"region:us"
]
| object-detection | 2025-04-09T01:08:41Z | ---
language:
- en
library_name: ultralytics
pipeline_tag: object-detection
tags:
- yolo
- object-detect
- yolo11
- yolov11
---
# Number and Operator Detection Based on YOLO11x
This repository contains a PyTorch-exported model for detecting fire and smoke using the YOLO11s architecture. The model has been trained to recognize these symbols in images and return their locations and classifications.
## Model Description
The YOLO11s model is optimized for detecting the following:
- fire, smoke
```text
#class
fire
smoke
```
## How to Use
To use this model in your project, follow the steps below:
### 1. Installation
Ensure you have the `ultralytics` library installed, which is used for YOLO models:
```bash
pip install ultralytics
```
### 2. Load the Model
You can load the model and perform detection on an image as follows:
```python
from ultralytics import YOLO
# Load the model
model = YOLO("./firedetect-11s.pt")
# Perform detection on an image
results = model("image.png")
# Display or process the results
results.show() # This will display the image with detected objects
```
### 3. Model Inference
The results object contains bounding boxes, labels (e.g., numbers or operators), and confidence scores for each detected object.
Access them like this:
```python
for result in results:
print(result.boxes) # Bounding boxes
print(result.names) # Detected classes
print(result.scores) # Confidence scores
```
#yolo11 |
edwindn/orpheus-1b-0.1-weightDecay-chkpt78201 | edwindn | 2025-04-23T00:07:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-23T00:06:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
Urwis/Gpu | Urwis | 2025-04-23T00:06:53Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-23T00:06:53Z | ---
license: apache-2.0
---
|
IParraMartin/impossible-llms-spanish-mirror-reversal | IParraMartin | 2025-04-22T23:54:25Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-21T21:31:21Z | ---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: impossible-llms-spanish-mirror-reversal
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. -->
# impossible-llms-spanish-mirror-reversal
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.9937
## 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: 12
- eval_batch_size: 8
- seed: 0
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 384
- total_eval_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: cosine
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 3000
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:--------:|:----:|:---------------:|
| 84.4162 | 0.9180 | 7 | 10.3431 |
| 77.8512 | 1.9180 | 14 | 9.5818 |
| 74.3777 | 2.9180 | 21 | 9.2231 |
| 72.9558 | 3.9180 | 28 | 9.0710 |
| 72.1401 | 4.9180 | 35 | 8.9695 |
| 70.8846 | 5.9180 | 42 | 8.8307 |
| 69.6382 | 6.9180 | 49 | 8.6507 |
| 67.9279 | 7.9180 | 56 | 8.4302 |
| 66.2401 | 8.9180 | 63 | 8.2178 |
| 64.6215 | 9.9180 | 70 | 8.0136 |
| 62.8811 | 10.9180 | 77 | 7.8174 |
| 61.1512 | 11.9180 | 84 | 7.6212 |
| 59.7228 | 12.9180 | 91 | 7.4259 |
| 58.0425 | 13.9180 | 98 | 7.2282 |
| 56.6221 | 14.9180 | 105 | 7.0344 |
| 55.1504 | 15.9180 | 112 | 6.8581 |
| 53.5989 | 16.9180 | 119 | 6.6987 |
| 52.7903 | 17.9180 | 126 | 6.5563 |
| 51.6098 | 18.9180 | 133 | 6.4371 |
| 50.7139 | 19.9180 | 140 | 6.3459 |
| 50.102 | 20.9180 | 147 | 6.2674 |
| 49.6932 | 21.9180 | 154 | 6.2228 |
| 49.144 | 22.9180 | 161 | 6.1715 |
| 48.9015 | 23.9180 | 168 | 6.1300 |
| 48.7174 | 24.9180 | 175 | 6.1021 |
| 48.2177 | 25.9180 | 182 | 6.0672 |
| 48.1698 | 26.9180 | 189 | 6.0371 |
| 48.0452 | 27.9180 | 196 | 6.0075 |
| 47.6822 | 28.9180 | 203 | 5.9850 |
| 47.0775 | 29.9180 | 210 | 5.9656 |
| 47.0889 | 30.9180 | 217 | 5.9483 |
| 47.0495 | 31.9180 | 224 | 5.9125 |
| 46.8446 | 32.9180 | 231 | 5.8966 |
| 46.8521 | 33.9180 | 238 | 5.8779 |
| 46.5076 | 34.9180 | 245 | 5.8588 |
| 46.2375 | 35.9180 | 252 | 5.8437 |
| 46.1051 | 36.9180 | 259 | 5.8306 |
| 45.9298 | 37.9180 | 266 | 5.8160 |
| 45.6758 | 38.9180 | 273 | 5.8035 |
| 45.7036 | 39.9180 | 280 | 5.7862 |
| 45.5413 | 40.9180 | 287 | 5.7700 |
| 45.2449 | 41.9180 | 294 | 5.7602 |
| 45.101 | 42.9180 | 301 | 5.7457 |
| 44.963 | 43.9180 | 308 | 5.7313 |
| 44.778 | 44.9180 | 315 | 5.7205 |
| 44.6973 | 45.9180 | 322 | 5.7077 |
| 44.3128 | 46.9180 | 329 | 5.6902 |
| 44.3436 | 47.9180 | 336 | 5.6782 |
| 44.055 | 48.9180 | 343 | 5.6623 |
| 43.8327 | 49.9180 | 350 | 5.6461 |
| 43.6442 | 50.9180 | 357 | 5.6394 |
| 43.3198 | 51.9180 | 364 | 5.6193 |
| 43.2727 | 52.9180 | 371 | 5.6041 |
| 43.1289 | 53.9180 | 378 | 5.5983 |
| 42.9209 | 54.9180 | 385 | 5.5709 |
| 42.9342 | 55.9180 | 392 | 5.5631 |
| 42.4359 | 56.9180 | 399 | 5.5559 |
| 42.3215 | 57.9180 | 406 | 5.5351 |
| 42.3673 | 58.9180 | 413 | 5.5182 |
| 41.9158 | 59.9180 | 420 | 5.5038 |
| 41.9819 | 60.9180 | 427 | 5.4985 |
| 41.7746 | 61.9180 | 434 | 5.4811 |
| 41.4981 | 62.9180 | 441 | 5.4636 |
| 41.2081 | 63.9180 | 448 | 5.4594 |
| 41.0731 | 64.9180 | 455 | 5.4432 |
| 41.1288 | 65.9180 | 462 | 5.4316 |
| 40.8899 | 66.9180 | 469 | 5.4163 |
| 40.5089 | 67.9180 | 476 | 5.4167 |
| 40.5938 | 68.9180 | 483 | 5.4056 |
| 40.1911 | 69.9180 | 490 | 5.3991 |
| 39.9726 | 70.9180 | 497 | 5.3848 |
| 39.8896 | 71.9180 | 504 | 5.3879 |
| 39.7376 | 72.9180 | 511 | 5.3698 |
| 39.3989 | 73.9180 | 518 | 5.3695 |
| 39.1616 | 74.9180 | 525 | 5.3568 |
| 39.1654 | 75.9180 | 532 | 5.3509 |
| 39.0076 | 76.9180 | 539 | 5.3506 |
| 38.861 | 77.9180 | 546 | 5.3558 |
| 38.5128 | 78.9180 | 553 | 5.3403 |
| 38.4616 | 79.9180 | 560 | 5.3381 |
| 38.415 | 80.9180 | 567 | 5.3444 |
| 38.059 | 81.9180 | 574 | 5.3374 |
| 38.1389 | 82.9180 | 581 | 5.3389 |
| 37.744 | 83.9180 | 588 | 5.3299 |
| 37.8217 | 84.9180 | 595 | 5.3213 |
| 37.398 | 85.9180 | 602 | 5.3205 |
| 37.417 | 86.9180 | 609 | 5.3301 |
| 37.2844 | 87.9180 | 616 | 5.3372 |
| 37.1349 | 88.9180 | 623 | 5.3331 |
| 36.9323 | 89.9180 | 630 | 5.3285 |
| 36.8712 | 90.9180 | 637 | 5.3288 |
| 36.6164 | 91.9180 | 644 | 5.3333 |
| 36.3797 | 92.9180 | 651 | 5.3395 |
| 36.1731 | 93.9180 | 658 | 5.3441 |
| 35.9206 | 94.9180 | 665 | 5.3456 |
| 35.8725 | 95.9180 | 672 | 5.3454 |
| 35.7979 | 96.9180 | 679 | 5.3435 |
| 35.6521 | 97.9180 | 686 | 5.3471 |
| 35.3987 | 98.9180 | 693 | 5.3561 |
| 35.3232 | 99.9180 | 700 | 5.3524 |
| 35.1982 | 100.9180 | 707 | 5.3661 |
| 34.886 | 101.9180 | 714 | 5.3686 |
| 34.7132 | 102.9180 | 721 | 5.3709 |
| 34.6847 | 103.9180 | 728 | 5.3774 |
| 34.5539 | 104.9180 | 735 | 5.3897 |
| 34.4671 | 105.9180 | 742 | 5.3989 |
| 34.2363 | 106.9180 | 749 | 5.3929 |
| 34.0945 | 107.9180 | 756 | 5.3963 |
| 33.8505 | 108.9180 | 763 | 5.4139 |
| 33.7776 | 109.9180 | 770 | 5.4137 |
| 33.7077 | 110.9180 | 777 | 5.4283 |
| 33.5768 | 111.9180 | 784 | 5.4255 |
| 33.4114 | 112.9180 | 791 | 5.4368 |
| 33.124 | 113.9180 | 798 | 5.4533 |
| 33.1255 | 114.9180 | 805 | 5.4452 |
| 32.9746 | 115.9180 | 812 | 5.4670 |
| 32.9757 | 116.9180 | 819 | 5.4674 |
| 32.7149 | 117.9180 | 826 | 5.4849 |
| 32.4399 | 118.9180 | 833 | 5.4895 |
| 32.6289 | 119.9180 | 840 | 5.5009 |
| 32.3678 | 120.9180 | 847 | 5.5007 |
| 32.1054 | 121.9180 | 854 | 5.5052 |
| 31.9792 | 122.9180 | 861 | 5.5272 |
| 32.0312 | 123.9180 | 868 | 5.5281 |
| 31.8027 | 124.9180 | 875 | 5.5397 |
| 31.7089 | 125.9180 | 882 | 5.5513 |
| 31.4487 | 126.9180 | 889 | 5.5479 |
| 31.3213 | 127.9180 | 896 | 5.5571 |
| 31.2986 | 128.9180 | 903 | 5.5726 |
| 31.1625 | 129.9180 | 910 | 5.5723 |
| 31.0116 | 130.9180 | 917 | 5.5939 |
| 30.9386 | 131.9180 | 924 | 5.6084 |
| 30.6873 | 132.9180 | 931 | 5.6066 |
| 30.5603 | 133.9180 | 938 | 5.6187 |
| 30.4922 | 134.9180 | 945 | 5.6356 |
| 30.5098 | 135.9180 | 952 | 5.6411 |
| 30.3877 | 136.9180 | 959 | 5.6489 |
| 30.0047 | 137.9180 | 966 | 5.6620 |
| 29.982 | 138.9180 | 973 | 5.6814 |
| 29.666 | 139.9180 | 980 | 5.6748 |
| 29.7369 | 140.9180 | 987 | 5.6936 |
| 29.5357 | 141.9180 | 994 | 5.7011 |
| 29.4863 | 142.9180 | 1001 | 5.7023 |
| 29.1884 | 143.9180 | 1008 | 5.7173 |
| 29.2733 | 144.9180 | 1015 | 5.7391 |
| 29.1444 | 145.9180 | 1022 | 5.7430 |
| 28.9668 | 146.9180 | 1029 | 5.7628 |
| 28.9572 | 147.9180 | 1036 | 5.7740 |
| 28.6542 | 148.9180 | 1043 | 5.7703 |
| 28.6571 | 149.9180 | 1050 | 5.7768 |
| 28.4431 | 150.9180 | 1057 | 5.7939 |
| 28.3375 | 151.9180 | 1064 | 5.8060 |
| 28.2275 | 152.9180 | 1071 | 5.8154 |
| 28.2147 | 153.9180 | 1078 | 5.8239 |
| 28.0644 | 154.9180 | 1085 | 5.8309 |
| 27.9749 | 155.9180 | 1092 | 5.8453 |
| 27.8662 | 156.9180 | 1099 | 5.8514 |
| 27.6157 | 157.9180 | 1106 | 5.8644 |
| 27.3961 | 158.9180 | 1113 | 5.8822 |
| 27.46 | 159.9180 | 1120 | 5.8790 |
| 27.5323 | 160.9180 | 1127 | 5.8899 |
| 27.1845 | 161.9180 | 1134 | 5.9004 |
| 27.1134 | 162.9180 | 1141 | 5.9200 |
| 27.0488 | 163.9180 | 1148 | 5.9319 |
| 26.798 | 164.9180 | 1155 | 5.9282 |
| 26.7074 | 165.9180 | 1162 | 5.9415 |
| 26.7968 | 166.9180 | 1169 | 5.9535 |
| 26.5976 | 167.9180 | 1176 | 5.9539 |
| 26.6141 | 168.9180 | 1183 | 5.9724 |
| 26.4868 | 169.9180 | 1190 | 5.9768 |
| 26.0997 | 170.9180 | 1197 | 5.9941 |
| 26.276 | 171.9180 | 1204 | 6.0098 |
| 26.1329 | 172.9180 | 1211 | 6.0084 |
| 25.9698 | 173.9180 | 1218 | 6.0104 |
| 25.7919 | 174.9180 | 1225 | 6.0339 |
| 25.7292 | 175.9180 | 1232 | 6.0430 |
| 25.5487 | 176.9180 | 1239 | 6.0521 |
| 25.6807 | 177.9180 | 1246 | 6.0665 |
| 25.5744 | 178.9180 | 1253 | 6.0639 |
| 25.4511 | 179.9180 | 1260 | 6.0746 |
| 25.1839 | 180.9180 | 1267 | 6.0985 |
| 25.102 | 181.9180 | 1274 | 6.0936 |
| 25.2993 | 182.9180 | 1281 | 6.1054 |
| 25.0789 | 183.9180 | 1288 | 6.1141 |
| 24.9031 | 184.9180 | 1295 | 6.1289 |
| 24.9472 | 185.9180 | 1302 | 6.1326 |
| 24.7081 | 186.9180 | 1309 | 6.1548 |
| 24.5715 | 187.9180 | 1316 | 6.1579 |
| 24.5298 | 188.9180 | 1323 | 6.1540 |
| 24.4873 | 189.9180 | 1330 | 6.1685 |
| 24.2975 | 190.9180 | 1337 | 6.1855 |
| 24.2701 | 191.9180 | 1344 | 6.1986 |
| 24.2495 | 192.9180 | 1351 | 6.2024 |
| 24.1608 | 193.9180 | 1358 | 6.2128 |
| 23.9288 | 194.9180 | 1365 | 6.2151 |
| 23.9611 | 195.9180 | 1372 | 6.2296 |
| 23.8268 | 196.9180 | 1379 | 6.2358 |
| 23.6677 | 197.9180 | 1386 | 6.2405 |
| 23.7449 | 198.9180 | 1393 | 6.2461 |
| 23.4324 | 199.9180 | 1400 | 6.2662 |
| 23.4854 | 200.9180 | 1407 | 6.2655 |
| 23.3554 | 201.9180 | 1414 | 6.2769 |
| 23.167 | 202.9180 | 1421 | 6.2847 |
| 23.2855 | 203.9180 | 1428 | 6.2861 |
| 23.1166 | 204.9180 | 1435 | 6.3017 |
| 23.0398 | 205.9180 | 1442 | 6.3119 |
| 23.0255 | 206.9180 | 1449 | 6.3172 |
| 22.999 | 207.9180 | 1456 | 6.3255 |
| 22.7308 | 208.9180 | 1463 | 6.3393 |
| 22.7178 | 209.9180 | 1470 | 6.3417 |
| 22.6128 | 210.9180 | 1477 | 6.3461 |
| 22.5973 | 211.9180 | 1484 | 6.3585 |
| 22.6145 | 212.9180 | 1491 | 6.3666 |
| 22.4369 | 213.9180 | 1498 | 6.3749 |
| 22.3656 | 214.9180 | 1505 | 6.3802 |
| 22.2833 | 215.9180 | 1512 | 6.3983 |
| 22.1951 | 216.9180 | 1519 | 6.3926 |
| 22.1625 | 217.9180 | 1526 | 6.4041 |
| 21.998 | 218.9180 | 1533 | 6.4135 |
| 21.991 | 219.9180 | 1540 | 6.4330 |
| 21.9023 | 220.9180 | 1547 | 6.4238 |
| 21.9138 | 221.9180 | 1554 | 6.4345 |
| 21.9563 | 222.9180 | 1561 | 6.4423 |
| 21.7125 | 223.9180 | 1568 | 6.4432 |
| 21.6526 | 224.9180 | 1575 | 6.4544 |
| 21.574 | 225.9180 | 1582 | 6.4674 |
| 21.5197 | 226.9180 | 1589 | 6.4717 |
| 21.475 | 227.9180 | 1596 | 6.4811 |
| 21.3944 | 228.9180 | 1603 | 6.4886 |
| 21.4001 | 229.9180 | 1610 | 6.4940 |
| 21.2599 | 230.9180 | 1617 | 6.5045 |
| 21.3074 | 231.9180 | 1624 | 6.5078 |
| 21.0136 | 232.9180 | 1631 | 6.5144 |
| 21.1079 | 233.9180 | 1638 | 6.5142 |
| 21.0878 | 234.9180 | 1645 | 6.5233 |
| 20.9775 | 235.9180 | 1652 | 6.5266 |
| 20.9687 | 236.9180 | 1659 | 6.5404 |
| 20.7984 | 237.9180 | 1666 | 6.5467 |
| 20.7934 | 238.9180 | 1673 | 6.5521 |
| 20.7419 | 239.9180 | 1680 | 6.5511 |
| 20.5449 | 240.9180 | 1687 | 6.5641 |
| 20.6149 | 241.9180 | 1694 | 6.5764 |
| 20.6499 | 242.9180 | 1701 | 6.5704 |
| 20.5261 | 243.9180 | 1708 | 6.5780 |
| 20.4831 | 244.9180 | 1715 | 6.5889 |
| 20.4239 | 245.9180 | 1722 | 6.5939 |
| 20.2128 | 246.9180 | 1729 | 6.6054 |
| 20.1934 | 247.9180 | 1736 | 6.6072 |
| 20.1968 | 248.9180 | 1743 | 6.6114 |
| 20.1866 | 249.9180 | 1750 | 6.6134 |
| 20.104 | 250.9180 | 1757 | 6.6175 |
| 20.0609 | 251.9180 | 1764 | 6.6316 |
| 20.0985 | 252.9180 | 1771 | 6.6390 |
| 19.9381 | 253.9180 | 1778 | 6.6366 |
| 19.9409 | 254.9180 | 1785 | 6.6414 |
| 19.8636 | 255.9180 | 1792 | 6.6460 |
| 19.8073 | 256.9180 | 1799 | 6.6524 |
| 19.8491 | 257.9180 | 1806 | 6.6585 |
| 19.7852 | 258.9180 | 1813 | 6.6658 |
| 19.6229 | 259.9180 | 1820 | 6.6708 |
| 19.5722 | 260.9180 | 1827 | 6.6739 |
| 19.5835 | 261.9180 | 1834 | 6.6854 |
| 19.5987 | 262.9180 | 1841 | 6.6936 |
| 19.4856 | 263.9180 | 1848 | 6.6930 |
| 19.5904 | 264.9180 | 1855 | 6.6983 |
| 19.3708 | 265.9180 | 1862 | 6.7038 |
| 19.3553 | 266.9180 | 1869 | 6.7077 |
| 19.3373 | 267.9180 | 1876 | 6.7083 |
| 19.2019 | 268.9180 | 1883 | 6.7167 |
| 19.1206 | 269.9180 | 1890 | 6.7259 |
| 19.1018 | 270.9180 | 1897 | 6.7236 |
| 19.2208 | 271.9180 | 1904 | 6.7353 |
| 19.0552 | 272.9180 | 1911 | 6.7368 |
| 19.0681 | 273.9180 | 1918 | 6.7394 |
| 19.0372 | 274.9180 | 1925 | 6.7420 |
| 19.0147 | 275.9180 | 1932 | 6.7465 |
| 18.9359 | 276.9180 | 1939 | 6.7533 |
| 18.9365 | 277.9180 | 1946 | 6.7533 |
| 18.8647 | 278.9180 | 1953 | 6.7576 |
| 18.7693 | 279.9180 | 1960 | 6.7628 |
| 18.7637 | 280.9180 | 1967 | 6.7683 |
| 18.8001 | 281.9180 | 1974 | 6.7683 |
| 18.6263 | 282.9180 | 1981 | 6.7707 |
| 18.6731 | 283.9180 | 1988 | 6.7820 |
| 18.6376 | 284.9180 | 1995 | 6.7786 |
| 18.6834 | 285.9180 | 2002 | 6.7890 |
| 18.5305 | 286.9180 | 2009 | 6.7860 |
| 18.5434 | 287.9180 | 2016 | 6.7951 |
| 18.4738 | 288.9180 | 2023 | 6.7954 |
| 18.5392 | 289.9180 | 2030 | 6.8031 |
| 18.4525 | 290.9180 | 2037 | 6.8034 |
| 18.3516 | 291.9180 | 2044 | 6.8089 |
| 18.4084 | 292.9180 | 2051 | 6.8107 |
| 18.3341 | 293.9180 | 2058 | 6.8176 |
| 18.2295 | 294.9180 | 2065 | 6.8240 |
| 18.2289 | 295.9180 | 2072 | 6.8254 |
| 18.2957 | 296.9180 | 2079 | 6.8273 |
| 18.1978 | 297.9180 | 2086 | 6.8269 |
| 18.1374 | 298.9180 | 2093 | 6.8368 |
| 18.1589 | 299.9180 | 2100 | 6.8345 |
| 18.0843 | 300.9180 | 2107 | 6.8365 |
| 18.0587 | 301.9180 | 2114 | 6.8508 |
| 17.9929 | 302.9180 | 2121 | 6.8402 |
| 17.9596 | 303.9180 | 2128 | 6.8487 |
| 17.9953 | 304.9180 | 2135 | 6.8466 |
| 17.969 | 305.9180 | 2142 | 6.8532 |
| 18.0339 | 306.9180 | 2149 | 6.8526 |
| 17.8757 | 307.9180 | 2156 | 6.8600 |
| 17.8847 | 308.9180 | 2163 | 6.8596 |
| 17.8781 | 309.9180 | 2170 | 6.8660 |
| 17.8195 | 310.9180 | 2177 | 6.8693 |
| 17.8741 | 311.9180 | 2184 | 6.8666 |
| 17.7714 | 312.9180 | 2191 | 6.8732 |
| 17.7876 | 313.9180 | 2198 | 6.8768 |
| 17.7111 | 314.9180 | 2205 | 6.8820 |
| 17.7864 | 315.9180 | 2212 | 6.8847 |
| 17.7075 | 316.9180 | 2219 | 6.8839 |
| 17.5483 | 317.9180 | 2226 | 6.8867 |
| 17.7455 | 318.9180 | 2233 | 6.8962 |
| 17.598 | 319.9180 | 2240 | 6.8898 |
| 17.6425 | 320.9180 | 2247 | 6.8977 |
| 17.6195 | 321.9180 | 2254 | 6.8918 |
| 17.5003 | 322.9180 | 2261 | 6.8971 |
| 17.5788 | 323.9180 | 2268 | 6.9069 |
| 17.5225 | 324.9180 | 2275 | 6.9025 |
| 17.5252 | 325.9180 | 2282 | 6.9068 |
| 17.5761 | 326.9180 | 2289 | 6.9104 |
| 17.4598 | 327.9180 | 2296 | 6.9088 |
| 17.3877 | 328.9180 | 2303 | 6.9135 |
| 17.3781 | 329.9180 | 2310 | 6.9171 |
| 17.4783 | 330.9180 | 2317 | 6.9221 |
| 17.295 | 331.9180 | 2324 | 6.9189 |
| 17.3924 | 332.9180 | 2331 | 6.9210 |
| 17.2561 | 333.9180 | 2338 | 6.9229 |
| 17.3171 | 334.9180 | 2345 | 6.9279 |
| 17.3314 | 335.9180 | 2352 | 6.9260 |
| 17.345 | 336.9180 | 2359 | 6.9280 |
| 17.2402 | 337.9180 | 2366 | 6.9335 |
| 17.2594 | 338.9180 | 2373 | 6.9359 |
| 17.1937 | 339.9180 | 2380 | 6.9325 |
| 17.1731 | 340.9180 | 2387 | 6.9320 |
| 17.2473 | 341.9180 | 2394 | 6.9390 |
| 17.1868 | 342.9180 | 2401 | 6.9378 |
| 17.1588 | 343.9180 | 2408 | 6.9383 |
| 17.1417 | 344.9180 | 2415 | 6.9439 |
| 17.0871 | 345.9180 | 2422 | 6.9438 |
| 17.104 | 346.9180 | 2429 | 6.9450 |
| 17.1095 | 347.9180 | 2436 | 6.9461 |
| 17.1458 | 348.9180 | 2443 | 6.9487 |
| 17.0723 | 349.9180 | 2450 | 6.9488 |
| 17.1555 | 350.9180 | 2457 | 6.9481 |
| 17.107 | 351.9180 | 2464 | 6.9551 |
| 17.0555 | 352.9180 | 2471 | 6.9532 |
| 17.057 | 353.9180 | 2478 | 6.9550 |
| 17.0571 | 354.9180 | 2485 | 6.9561 |
| 17.0464 | 355.9180 | 2492 | 6.9564 |
| 16.9419 | 356.9180 | 2499 | 6.9552 |
| 16.9971 | 357.9180 | 2506 | 6.9591 |
| 17.0158 | 358.9180 | 2513 | 6.9612 |
| 16.9852 | 359.9180 | 2520 | 6.9609 |
| 16.9336 | 360.9180 | 2527 | 6.9651 |
| 16.9507 | 361.9180 | 2534 | 6.9685 |
| 16.9286 | 362.9180 | 2541 | 6.9668 |
| 16.8417 | 363.9180 | 2548 | 6.9698 |
| 16.9085 | 364.9180 | 2555 | 6.9729 |
| 16.9229 | 365.9180 | 2562 | 6.9705 |
| 16.893 | 366.9180 | 2569 | 6.9724 |
| 16.8789 | 367.9180 | 2576 | 6.9681 |
| 16.8963 | 368.9180 | 2583 | 6.9730 |
| 16.8282 | 369.9180 | 2590 | 6.9736 |
| 16.8398 | 370.9180 | 2597 | 6.9757 |
| 16.8059 | 371.9180 | 2604 | 6.9758 |
| 16.8391 | 372.9180 | 2611 | 6.9773 |
| 16.9314 | 373.9180 | 2618 | 6.9767 |
| 16.8705 | 374.9180 | 2625 | 6.9770 |
| 16.7638 | 375.9180 | 2632 | 6.9794 |
| 16.8538 | 376.9180 | 2639 | 6.9801 |
| 16.7878 | 377.9180 | 2646 | 6.9798 |
| 16.786 | 378.9180 | 2653 | 6.9828 |
| 16.7546 | 379.9180 | 2660 | 6.9813 |
| 16.8046 | 380.9180 | 2667 | 6.9815 |
| 16.7852 | 381.9180 | 2674 | 6.9852 |
| 16.734 | 382.9180 | 2681 | 6.9834 |
| 16.8187 | 383.9180 | 2688 | 6.9820 |
| 16.7764 | 384.9180 | 2695 | 6.9857 |
| 16.7835 | 385.9180 | 2702 | 6.9861 |
| 16.7463 | 386.9180 | 2709 | 6.9860 |
| 16.6309 | 387.9180 | 2716 | 6.9865 |
| 16.6992 | 388.9180 | 2723 | 6.9881 |
| 16.7021 | 389.9180 | 2730 | 6.9872 |
| 16.7778 | 390.9180 | 2737 | 6.9873 |
| 16.784 | 391.9180 | 2744 | 6.9870 |
| 16.7504 | 392.9180 | 2751 | 6.9877 |
| 16.7041 | 393.9180 | 2758 | 6.9891 |
| 16.7505 | 394.9180 | 2765 | 6.9917 |
| 16.7962 | 395.9180 | 2772 | 6.9908 |
| 16.7077 | 396.9180 | 2779 | 6.9912 |
| 16.7166 | 397.9180 | 2786 | 6.9910 |
| 16.7462 | 398.9180 | 2793 | 6.9917 |
| 16.713 | 399.9180 | 2800 | 6.9915 |
| 16.6515 | 400.9180 | 2807 | 6.9921 |
| 16.7043 | 401.9180 | 2814 | 6.9916 |
| 16.719 | 402.9180 | 2821 | 6.9915 |
| 16.697 | 403.9180 | 2828 | 6.9929 |
| 16.7353 | 404.9180 | 2835 | 6.9926 |
| 16.7601 | 405.9180 | 2842 | 6.9916 |
| 16.6814 | 406.9180 | 2849 | 6.9921 |
| 16.7516 | 407.9180 | 2856 | 6.9929 |
| 16.6698 | 408.9180 | 2863 | 6.9931 |
| 16.6765 | 409.9180 | 2870 | 6.9941 |
| 16.6709 | 410.9180 | 2877 | 6.9936 |
| 16.7178 | 411.9180 | 2884 | 6.9932 |
| 16.6784 | 412.9180 | 2891 | 6.9935 |
| 16.7612 | 413.9180 | 2898 | 6.9933 |
| 16.7469 | 414.9180 | 2905 | 6.9932 |
| 16.6571 | 415.9180 | 2912 | 6.9934 |
| 16.6858 | 416.9180 | 2919 | 6.9936 |
| 16.6591 | 417.9180 | 2926 | 6.9935 |
| 16.7057 | 418.9180 | 2933 | 6.9935 |
| 16.7523 | 419.9180 | 2940 | 6.9936 |
| 16.7288 | 420.9180 | 2947 | 6.9936 |
| 16.6824 | 421.9180 | 2954 | 6.9936 |
| 16.6956 | 422.9180 | 2961 | 6.9937 |
| 16.659 | 423.9180 | 2968 | 6.9937 |
| 16.6825 | 424.9180 | 2975 | 6.9937 |
| 16.6794 | 425.9180 | 2982 | 6.9937 |
| 16.677 | 426.9180 | 2989 | 6.9937 |
| 16.6037 | 427.9180 | 2996 | 6.9937 |
| 16.6751 | 428.5246 | 3000 | 6.9937 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.4.0+cu121
- Datasets 3.4.0
- Tokenizers 0.21.0
|
thiomajid/codebert-java-inconsistency | thiomajid | 2025-04-22T23:54:17Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/codebert-base",
"base_model:finetune:microsoft/codebert-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T23:48:38Z | ---
library_name: transformers
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: codebert-java-inconsistency
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. -->
# codebert-java-inconsistency
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3543
- Accuracy: 0.9167
- F1: 0.9183
- Precision: 0.9235
- Recall: 0.9167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.4625 | 3.1290 | 50 | 0.8954 | 0.7531 | 0.7554 | 0.7765 | 0.7531 |
| 0.5834 | 6.2581 | 100 | 0.5559 | 0.8189 | 0.8241 | 0.8483 | 0.8189 |
| 0.2858 | 9.3871 | 150 | 0.4046 | 0.8930 | 0.8945 | 0.8995 | 0.8930 |
| 0.1624 | 12.5161 | 200 | 0.4461 | 0.8642 | 0.8661 | 0.8750 | 0.8642 |
| 0.1084 | 15.6452 | 250 | 0.4012 | 0.9012 | 0.9038 | 0.9123 | 0.9012 |
| 0.074 | 18.7742 | 300 | 0.4689 | 0.8765 | 0.8817 | 0.8972 | 0.8765 |
| 0.0574 | 21.9032 | 350 | 0.4885 | 0.8807 | 0.8845 | 0.8970 | 0.8807 |
| 0.0452 | 25.0 | 400 | 0.4900 | 0.8848 | 0.8888 | 0.9011 | 0.8848 |
| 0.0396 | 28.1290 | 450 | 0.4896 | 0.8765 | 0.8805 | 0.8934 | 0.8765 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Shaelois/MeetingScript | Shaelois | 2025-04-22T23:49:23Z | 13 | 1 | transformers | [
"transformers",
"safetensors",
"bigbird_pegasus",
"text2text-generation",
"summarization",
"en",
"dataset:huuuyeah/meetingbank",
"base_model:google/bigbird-pegasus-large-bigpatent",
"base_model:finetune:google/bigbird-pegasus-large-bigpatent",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| summarization | 2025-04-20T01:04:25Z | ---
license: apache-2.0
datasets:
- huuuyeah/meetingbank
language:
- en
metrics:
- rouge
base_model:
- google/bigbird-pegasus-large-bigpatent
pipeline_tag: summarization
library_name: transformers
---
# MeetingScript
> A BigBird‐Pegasus model fine‑tuned for meeting transcription summarization on the MeetingBank dataset.
📦 **Model Files**
- **Weights & config**: `pytorch_model.bin`, `config.json`
- **Tokenizer**: `tokenizer.json`, `tokenizer_config.json`, `merges.txt`, `special_tokens_map.json`
- **Generation defaults**: `generation_config.json`
🔗 **Hub:** https://github.com/kevin0437/Meeting_scripts
---
## Model Description
**MeetingScript** is a sequence‑to‑sequence model based on
[google/bigbird-pegasus-large-bigpatent](https://huggingface.co/google/bigbird-pegasus-large-bigpatent)
and fine‑tuned on the [MeetingBank](https://huggingface.co/datasets/huuuyeah/meetingbank) corpus of meeting transcripts paired with human‐written summaries.
It is designed to take long meeting transcripts (up to 4096 tokens) and produce concise, coherent summaries.
---
## Evaluation Results
Evaluated on the held‑out test split of MeetingBank (≈ 600 transcripts), using beam search (4 beams, max_length=600):
| Metric | F1 Score (%) |
|-------------|-------------:|
| **ROUGE‑1** | 51.5556 |
| **ROUGE‑2** | 38.5378 |
| **ROUGE‑L** | 48.0786 |
| **ROUGE‑Lsum** | 48.0142 |
---
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
# 1) Load from the Hub
tokenizer = AutoTokenizer.from_pretrained("Shaelois/MeetingScript")
model = AutoModelForSeq2SeqLM.from_pretrained("Shaelois/MeetingScript")
# 2) Summarize a long transcript
transcript = """
Alice: Good morning everyone, let’s get started…
Bob: I updated the design mockups…
… (thousands of words) …
"""
inputs = tokenizer(
transcript,
max_length=4096,
truncation=True,
return_tensors="pt"
).to("cuda")
summary_ids = model.generate(
**inputs,
num_beams=4,
max_length=150,
early_stopping=True
)
summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print("📝 Summary:", summary)
```
---
## Training Data
Dataset: MeetingBank
Splits: Train (5000+), Validation (600+), Test (600+)
Preprocessing: Sliding‑window chunking for sequences > 4096 tokens |
Kariuki20/Eslam | Kariuki20 | 2025-04-22T23:47:44Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-22T23:47:44Z | ---
license: apache-2.0
---
|
NaomiH/model | NaomiH | 2025-04-22T23:45:48Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T23:41:17Z | ---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** NaomiH
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Hartunka/distilbert_rand_20_v2_stsb | Hartunka | 2025-04-22T23:39:37Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/distilbert_rand_20_v2",
"base_model:finetune:Hartunka/distilbert_rand_20_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T23:38:05Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: distilbert_rand_20_v2_stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE STSB
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.29365361139119855
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_rand_20_v2_stsb
This model is a fine-tuned version of [Hartunka/distilbert_rand_20_v2](https://huggingface.co/Hartunka/distilbert_rand_20_v2) on the GLUE STSB dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3318
- Pearson: 0.3011
- Spearmanr: 0.2937
- Combined Score: 0.2974
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:|
| 2.8409 | 1.0 | 23 | 2.6483 | 0.1073 | 0.0837 | 0.0955 |
| 1.937 | 2.0 | 46 | 2.4296 | 0.1982 | 0.1718 | 0.1850 |
| 1.7127 | 3.0 | 69 | 2.4168 | 0.2334 | 0.2205 | 0.2270 |
| 1.3482 | 4.0 | 92 | 2.3318 | 0.3011 | 0.2937 | 0.2974 |
| 0.9691 | 5.0 | 115 | 2.5006 | 0.3014 | 0.2903 | 0.2959 |
| 0.7285 | 6.0 | 138 | 2.4679 | 0.3349 | 0.3254 | 0.3302 |
| 0.572 | 7.0 | 161 | 2.5069 | 0.3510 | 0.3474 | 0.3492 |
| 0.4434 | 8.0 | 184 | 2.4404 | 0.3636 | 0.3552 | 0.3594 |
| 0.3722 | 9.0 | 207 | 2.3603 | 0.3501 | 0.3421 | 0.3461 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
SaddamHasanov213/Qwen2.5-3B-instruct-BankBotv3-GGUF | SaddamHasanov213 | 2025-04-22T23:39:19Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-22T23:37:47Z | ---
base_model: unsloth/Qwen2.5-3B-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** SaddamHasanov213
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
edwindn/orpheus-1b-0.1 | edwindn | 2025-04-22T23:38:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:finetune:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T23:36:12Z | ---
library_name: transformers
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
model-index:
- name: orpheus-1b-0.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# orpheus-1b-0.1
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 64
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Hartunka/distilbert_rand_20_v2_sst2 | Hartunka | 2025-04-22T23:37:57Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/distilbert_rand_20_v2",
"base_model:finetune:Hartunka/distilbert_rand_20_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T23:31:24Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_rand_20_v2_sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8027522935779816
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_rand_20_v2_sst2
This model is a fine-tuned version of [Hartunka/distilbert_rand_20_v2](https://huggingface.co/Hartunka/distilbert_rand_20_v2) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4456
- Accuracy: 0.8028
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3907 | 1.0 | 264 | 0.4456 | 0.8028 |
| 0.2199 | 2.0 | 528 | 0.4952 | 0.8245 |
| 0.1616 | 3.0 | 792 | 0.4961 | 0.8188 |
| 0.1207 | 4.0 | 1056 | 0.6331 | 0.8062 |
| 0.0916 | 5.0 | 1320 | 0.6101 | 0.7970 |
| 0.0728 | 6.0 | 1584 | 0.7173 | 0.8005 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
quangdegi002/quang | quangdegi002 | 2025-04-22T23:34:06Z | 0 | 0 | null | [
"license:bsd-2-clause",
"region:us"
]
| null | 2025-04-22T23:34:06Z | ---
license: bsd-2-clause
---
|
SaddamHasanov213/Qwen2.5-3B-instruct-BankBotv3 | SaddamHasanov213 | 2025-04-22T23:32:13Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T23:28:43Z | ---
base_model: unsloth/Qwen2.5-3B-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** SaddamHasanov213
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rahatneuron/llama3.1_8B_hellaswag_norm_8L | rahatneuron | 2025-04-22T23:32:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T23:28:26Z | ---
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
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- 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. -->
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Summary
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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mlfoundations-dev/openthoughts2_100k_32B | mlfoundations-dev | 2025-04-22T23:31:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-32B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T07:25:41Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-32B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: openthoughts2_100k_32B
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. -->
# openthoughts2_100k_32B
This model is a fine-tuned version of [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) on the mlfoundations-dev/openthoughts2_100k 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: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 128
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 1024
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Hartunka/distilbert_rand_20_v2_rte | Hartunka | 2025-04-22T23:31:12Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/distilbert_rand_20_v2",
"base_model:finetune:Hartunka/distilbert_rand_20_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T23:30:27Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_rand_20_v2_rte
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE RTE
type: glue
args: rte
metrics:
- name: Accuracy
type: accuracy
value: 0.5342960288808665
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_rand_20_v2_rte
This model is a fine-tuned version of [Hartunka/distilbert_rand_20_v2](https://huggingface.co/Hartunka/distilbert_rand_20_v2) on the GLUE RTE dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6893
- Accuracy: 0.5343
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6973 | 1.0 | 10 | 0.6893 | 0.5343 |
| 0.6835 | 2.0 | 20 | 0.6986 | 0.5271 |
| 0.6373 | 3.0 | 30 | 0.7705 | 0.5126 |
| 0.5447 | 4.0 | 40 | 0.8775 | 0.4982 |
| 0.4205 | 5.0 | 50 | 1.0983 | 0.4801 |
| 0.2894 | 6.0 | 60 | 1.4305 | 0.5018 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
rahatneuron/llama3.1_8B_hellaswag_norm_7L | rahatneuron | 2025-04-22T23:29:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T23:26:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[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]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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xw17/Phi-3.5-mini-instruct_finetuned_1_optimized1 | xw17 | 2025-04-22T23:28:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"trl",
"sft",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T23:25:25Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- 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. -->
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[More Information Needed]
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[More Information Needed]
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Lekhansh/Qwen2.5-3B-Instruct-Scientific-Text-Cleaner | Lekhansh | 2025-04-22T23:26:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T18:29:40Z | ---
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
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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
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#### Preprocessing [optional]
[More Information Needed]
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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nathanialhunt2000/661b7c50-01c8-43c5-b785-b0ed3229b3f6 | nathanialhunt2000 | 2025-04-22T23:21:40Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-instruct-v0.2",
"base_model:adapter:unsloth/mistral-7b-instruct-v0.2",
"region:us"
]
| null | 2025-04-22T23:20:43Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/mistral-7b-instruct-v0.2
model-index:
- name: nathanialhunt2000/661b7c50-01c8-43c5-b785-b0ed3229b3f6
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. -->
# nathanialhunt2000/661b7c50-01c8-43c5-b785-b0ed3229b3f6
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5989
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1 | Detomo | 2025-04-22T23:19:10Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"onnx",
"safetensors",
"openvino",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:16199",
"loss:CustomBatchAllTripletLoss",
"arxiv:1908.10084",
"arxiv:1703.07737",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-04-22T14:09:11Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:16199
- loss:CustomBatchAllTripletLoss
widget:
- source_sentence: 科目：コンクリート。名称：立上り壁ｺﾝｸﾘｰﾄ。
sentences:
- 科目：ユニット及びその他。名称：棚。
- 科目：ユニット及びその他。名称：事務室ｽﾁｰﾙﾊﾟｰﾃｨｼｮﾝ。
- 科目：ユニット及びその他。名称：F-R#収納棚。
- source_sentence: 科目：タイル。名称：段鼻ﾀｲﾙ。
sentences:
- 科目：タイル。名称：巾木磁器質ﾀｲﾙ。
- 科目：タイル。名称：立上りﾀｲﾙA。
- 科目：タイル。名称：ｱﾌﾟﾛｰﾁﾃﾗｽ立上り天端床ﾀｲﾙA。
- source_sentence: 科目：ユニット及びその他。名称：#階F-WC#他ﾊﾟｳﾀﾞｰｶｳﾝﾀｰ。
sentences:
- 科目：ユニット及びその他。名称：便所ﾌｯｸ（二段）。
- 科目：ユニット及びその他。名称：ﾃﾗｽ床ｳｯﾄﾞﾃﾞｯｷ。
- 科目：ユニット及びその他。名称：ﾌﾗｯﾄﾃﾗｽ床ｳｯﾄﾞﾃﾞｯｷ。
- source_sentence: 科目：ユニット及びその他。名称：階数表示+停止階案内ｻｲﾝ。
sentences:
- 科目：ユニット及びその他。名称：ｴﾚﾍﾞｰﾀｰﾎｰﾙ入口ｻｲﾝ。
- 科目：ユニット及びその他。名称：場外離着陸用ｵｲﾙﾄﾗｯﾌﾟ。
- 科目：ユニット及びその他。名称：器材ｶｳﾝﾀｰ。
- source_sentence: 科目：ユニット及びその他。名称：階段内踊場階数ｻｲﾝ。
sentences:
- 科目：ユニット及びその他。名称：F-T#布団収納棚。
- 科目：ユニット及びその他。名称：#F廊下#飾り棚。
- 科目：ユニット及びその他。名称：F-#階理科室#収納棚。
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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})
)
```
## 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("Detomo/cl-nagoya-sup-simcse-ja-nss-v1_1")
# Run inference
sentences = [
'科目：ユニット及びその他。名称：階段内踊場階数ｻｲﾝ。',
'科目：ユニット及びその他。名称：F-#階理科室#収納棚。',
'科目：ユニット及びその他。名称：F-T#布団収納棚。',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 16,199 training samples
* Columns: <code>sentence</code> and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| type | string | int |
| details | <ul><li>min: 11 tokens</li><li>mean: 18.73 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~0.30%</li><li>1: ~0.30%</li><li>2: ~0.30%</li><li>3: ~0.30%</li><li>4: ~2.40%</li><li>5: ~0.30%</li><li>6: ~0.30%</li><li>7: ~0.30%</li><li>8: ~0.30%</li><li>9: ~0.30%</li><li>10: ~0.30%</li><li>11: ~0.40%</li><li>12: ~0.30%</li><li>13: ~0.30%</li><li>14: ~0.40%</li><li>15: ~0.30%</li><li>16: ~0.30%</li><li>17: ~0.30%</li><li>18: ~0.90%</li><li>19: ~0.30%</li><li>20: ~1.30%</li><li>21: ~0.30%</li><li>22: ~1.10%</li><li>23: ~0.30%</li><li>24: ~0.30%</li><li>25: ~0.30%</li><li>26: ~0.30%</li><li>27: ~0.30%</li><li>28: ~0.30%</li><li>29: ~0.30%</li><li>30: ~0.30%</li><li>31: ~0.30%</li><li>32: ~0.30%</li><li>33: ~0.30%</li><li>34: ~0.30%</li><li>35: ~0.30%</li><li>36: ~0.30%</li><li>37: ~0.30%</li><li>38: ~0.30%</li><li>39: ~0.30%</li><li>40: ~0.40%</li><li>41: ~0.30%</li><li>42: ~0.30%</li><li>43: ~0.30%</li><li>44: ~0.60%</li><li>45: ~0.70%</li><li>46: ~0.30%</li><li>47: ~0.30%</li><li>48: ~0.30%</li><li>49: ~0.30%</li><li>50: ~0.30%</li><li>51: ~0.30%</li><li>52: ~0.30%</li><li>53: ~0.30%</li><li>54: ~0.30%</li><li>55: ~0.30%</li><li>56: ~0.30%</li><li>57: ~0.80%</li><li>58: ~0.30%</li><li>59: ~0.30%</li><li>60: ~0.60%</li><li>61: ~0.30%</li><li>62: ~0.30%</li><li>63: ~0.30%</li><li>64: ~0.50%</li><li>65: ~0.30%</li><li>66: ~0.30%</li><li>67: ~0.30%</li><li>68: ~0.30%</li><li>69: ~0.50%</li><li>70: ~0.60%</li><li>71: ~0.30%</li><li>72: ~0.30%</li><li>73: ~0.30%</li><li>74: ~0.30%</li><li>75: ~0.30%</li><li>76: ~0.30%</li><li>77: ~0.30%</li><li>78: ~0.30%</li><li>79: ~0.30%</li><li>80: ~0.30%</li><li>81: ~0.30%</li><li>82: ~0.30%</li><li>83: ~0.30%</li><li>84: ~0.80%</li><li>85: ~0.60%</li><li>86: ~0.50%</li><li>87: ~0.30%</li><li>88: ~0.30%</li><li>89: ~16.30%</li><li>90: ~0.30%</li><li>91: ~0.30%</li><li>92: ~0.30%</li><li>93: ~0.30%</li><li>94: ~0.30%</li><li>95: ~0.30%</li><li>96: ~0.30%</li><li>97: ~0.30%</li><li>98: ~0.50%</li><li>99: ~0.30%</li><li>100: ~0.30%</li><li>101: ~0.30%</li><li>102: ~0.30%</li><li>103: ~0.30%</li><li>104: ~0.30%</li><li>105: ~0.30%</li><li>106: ~0.30%</li><li>107: ~0.70%</li><li>108: ~0.30%</li><li>109: ~3.20%</li><li>110: ~0.30%</li><li>111: ~0.40%</li><li>112: ~2.30%</li><li>113: ~0.30%</li><li>114: ~0.30%</li><li>115: ~0.50%</li><li>116: ~0.50%</li><li>117: ~0.50%</li><li>118: ~0.40%</li><li>119: ~0.30%</li><li>120: ~0.30%</li><li>121: ~0.30%</li><li>122: ~0.80%</li><li>123: ~0.30%</li><li>124: ~0.30%</li><li>125: ~0.30%</li><li>126: ~0.30%</li><li>127: ~0.30%</li><li>128: ~0.30%</li><li>129: ~0.30%</li><li>130: ~0.30%</li><li>131: ~0.50%</li><li>132: ~0.30%</li><li>133: ~0.40%</li><li>134: ~0.30%</li><li>135: ~0.30%</li><li>136: ~0.30%</li><li>137: ~0.30%</li><li>138: ~0.30%</li><li>139: ~0.30%</li><li>140: ~0.30%</li><li>141: ~0.30%</li><li>142: ~0.30%</li><li>143: ~0.30%</li><li>144: ~0.40%</li><li>145: ~0.30%</li><li>146: ~0.30%</li><li>147: ~0.30%</li><li>148: ~0.30%</li><li>149: ~0.30%</li><li>150: ~0.30%</li><li>151: ~0.70%</li><li>152: ~0.30%</li><li>153: ~0.30%</li><li>154: ~0.30%</li><li>155: ~1.30%</li><li>156: ~0.30%</li><li>157: ~0.40%</li><li>158: ~0.30%</li><li>159: ~0.30%</li><li>160: ~0.30%</li><li>161: ~1.50%</li><li>162: ~0.30%</li><li>163: ~0.30%</li><li>164: ~0.30%</li><li>165: ~0.30%</li><li>166: ~0.30%</li><li>167: ~0.30%</li><li>168: ~0.30%</li><li>169: ~1.50%</li><li>170: ~0.30%</li><li>171: ~0.30%</li><li>172: ~7.20%</li><li>173: ~0.30%</li><li>174: ~1.00%</li><li>175: ~0.30%</li><li>176: ~0.30%</li><li>177: ~0.30%</li><li>178: ~1.80%</li><li>179: ~0.30%</li><li>180: ~0.50%</li><li>181: ~0.70%</li><li>182: ~0.30%</li><li>183: ~0.30%</li></ul> |
* Samples:
| sentence | label |
|:-----------------------------------------|:---------------|
| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
| <code>科目：コンクリート。名称：免震基礎天端ｸﾞﾗｳﾄ注入。</code> | <code>0</code> |
* Loss: <code>sentence_transformer_lib.custom_batch_all_trip_loss.CustomBatchAllTripletLoss</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `learning_rate`: 1e-05
- `weight_decay`: 0.01
- `num_train_epochs`: 250
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: group_by_label
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `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`: 1e-05
- `weight_decay`: 0.01
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 250
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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`: 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`: group_by_label
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:-------:|:----:|:-------------:|
| 4.125 | 100 | 0.0682 |
| 8.25 | 200 | 0.0745 |
| 12.375 | 300 | 0.0764 |
| 16.5 | 400 | 0.0778 |
| 20.625 | 500 | 0.077 |
| 24.75 | 600 | 0.0767 |
| 29.125 | 700 | 0.0738 |
| 33.25 | 800 | 0.0701 |
| 37.375 | 900 | 0.0677 |
| 41.5 | 1000 | 0.0689 |
| 45.625 | 1100 | 0.0661 |
| 49.75 | 1200 | 0.0677 |
| 54.125 | 1300 | 0.0627 |
| 58.25 | 1400 | 0.0629 |
| 62.375 | 1500 | 0.0625 |
| 66.5 | 1600 | 0.0655 |
| 70.625 | 1700 | 0.0645 |
| 74.75 | 1800 | 0.0595 |
| 79.125 | 1900 | 0.0608 |
| 83.25 | 2000 | 0.0614 |
| 87.375 | 2100 | 0.0567 |
| 91.5 | 2200 | 0.0612 |
| 95.625 | 2300 | 0.0599 |
| 99.75 | 2400 | 0.059 |
| 104.125 | 2500 | 0.0547 |
| 108.25 | 2600 | 0.0571 |
| 112.375 | 2700 | 0.0543 |
| 116.5 | 2800 | 0.0574 |
| 120.625 | 2900 | 0.0561 |
| 124.75 | 3000 | 0.0534 |
| 129.125 | 3100 | 0.0554 |
| 133.25 | 3200 | 0.0507 |
| 137.375 | 3300 | 0.0533 |
| 141.5 | 3400 | 0.05 |
| 145.625 | 3500 | 0.0569 |
| 149.75 | 3600 | 0.0551 |
| 154.125 | 3700 | 0.0558 |
| 158.25 | 3800 | 0.0539 |
| 162.375 | 3900 | 0.0498 |
| 166.5 | 4000 | 0.0512 |
| 170.625 | 4100 | 0.0481 |
| 174.75 | 4200 | 0.0492 |
| 179.125 | 4300 | 0.0513 |
| 183.25 | 4400 | 0.0474 |
| 187.375 | 4500 | 0.0491 |
| 191.5 | 4600 | 0.0513 |
| 195.625 | 4700 | 0.0453 |
| 199.75 | 4800 | 0.0453 |
| 204.125 | 4900 | 0.0489 |
| 208.25 | 5000 | 0.0481 |
| 212.375 | 5100 | 0.0498 |
| 216.5 | 5200 | 0.044 |
| 220.625 | 5300 | 0.0486 |
| 224.75 | 5400 | 0.0399 |
| 229.125 | 5500 | 0.0384 |
| 233.25 | 5600 | 0.0428 |
| 237.375 | 5700 | 0.0447 |
| 241.5 | 5800 | 0.0479 |
| 245.625 | 5900 | 0.0434 |
| 249.75 | 6000 | 0.0442 |
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- 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",
}
```
#### CustomBatchAllTripletLoss
```bibtex
@misc{hermans2017defense,
title={In Defense of the Triplet Loss for Person Re-Identification},
author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
year={2017},
eprint={1703.07737},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
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## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
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peekayitachi/roberta-political-bias | peekayitachi | 2025-04-22T23:15:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"dataset:pranjali97/Bias-detection-combined",
"dataset:valurank/PoliticalBias_AllSides_Txt",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T22:41:08Z | ---
library_name: transformers
license: mit
datasets:
- pranjali97/Bias-detection-combined
- valurank/PoliticalBias_AllSides_Txt
metrics:
- accuracy
- f1
---
# Model Card for Model ID
---
library_name: transformers
license: mit
tags:
- roberta
- text-classification
- political-bias
- transformers
- nlp
- fine-tuned
datasets:
- pranjali97/Bias-detection-combined
- peekayitachi/allsides
- custom-political-bias-data
---
# 🧠 RoBERTa Political Bias Classifier
This is a fine-tuned [RoBERTa](https://huggingface.co/roberta-base) model for **political bias detection** in text. It classifies a sentence or article snippet into one of the following three categories:
- 🔴 **Right**
- 🟡 **Center**
- 🔵 **Left**
Trained on a combination of public and custom-labeled datasets, the model is capable of classifying political leaning in Indian and general English news/opinion text.
---
## 📥 Example Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained("peekayitachi/roberta-political-bias")
tokenizer = AutoTokenizer.from_pretrained("peekayitachi/roberta-political-bias")
text = "Our nation's sovereignty must be protected, and we should prioritize national interests."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
logits = model(**inputs).logits
predicted = torch.argmax(logits, dim=1).item()
label_map = {0: "Left", 1: "Center", 2: "Right"}
print("Predicted Bias:", label_map[predicted])
## Model Details
### Model Description
Base model: roberta-base
Architecture: Transformer encoder with classification head
Fine-tuned on: Multi-source labeled data (~38k samples)
Languages: English (Indian and global political context)
License: MIT
Author: peekayitachi (Pranav)
## 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 model reflects the labeling choices and distribution of the training data. It may:
Overfit to news-style text and miss subtle bias in blogs/social media
Be less accurate on texts that are neutral in tone or multi-opinionated
Reflect U.S./Indian-centric definitions of political categories
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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### Compute Infrastructure
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|
mixxxeee/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_humming_porcupine | mixxxeee | 2025-04-22T23:14:46Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am aquatic humming porcupine",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-21T19:07:02Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_humming_porcupine
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am aquatic humming porcupine
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_humming_porcupine
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="mixxxeee/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_humming_porcupine", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
cindyyyy/tuning | cindyyyy | 2025-04-22T23:07:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T11:19:01Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: tuning
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. -->
# tuning
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3260
- Accuracy: 0.9199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3735 | 1.0 | 1250 | 0.2887 | 0.9132 |
| 0.2082 | 2.0 | 2500 | 0.3260 | 0.9199 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
dgambettaphd/M_llm3_gen5_run0_W_doc1000_synt64_tot128_SYNLAST | dgambettaphd | 2025-04-22T23:07:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T23:06:53Z | ---
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]
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- **Shared by [optional]:** [More Information Needed]
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- **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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
HanningZhang/Qwen2.5-Math-7B-raft-plusplus_em-sample1n8-sample8-filter1.0-insufficient0.0-a0.001-b2.0-iter12 | HanningZhang | 2025-04-22T23:02:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T22:59:29Z | ---
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] |
cnaidu402/roberta-large-peft-lora | cnaidu402 | 2025-04-22T22:58:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T01:39:16Z | ---
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]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
objfilm659/obaida | objfilm659 | 2025-04-22T22:55:50Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-04-22T22:55:50Z | ---
license: apache-2.0
---
|
mlx-community/GLM-4-32B-0414-8bit | mlx-community | 2025-04-22T22:52:46Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"glm4",
"text-generation",
"conversational",
"zh",
"en",
"base_model:THUDM/GLM-4-32B-0414",
"base_model:quantized:THUDM/GLM-4-32B-0414",
"license:mit",
"8-bit",
"region:us"
]
| text-generation | 2025-04-22T21:44:51Z | ---
license: mit
language:
- zh
- en
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
base_model: THUDM/GLM-4-32B-0414
---
# mlx-community/GLM-4-32B-0414-8bit
This model [mlx-community/GLM-4-32B-0414-8bit](https://huggingface.co/mlx-community/GLM-4-32B-0414-8bit) was
converted to MLX format from [THUDM/GLM-4-32B-0414](https://huggingface.co/THUDM/GLM-4-32B-0414)
using mlx-lm version **0.23.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/GLM-4-32B-0414-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
HanningZhang/Qwen2.5-Math-7B-raft-plusplus_em-sample1n8-sample8-filter1.0-insufficient0.0-a0.001-b2.0-iter11 | HanningZhang | 2025-04-22T22:49:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T22:46:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### 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] |
Hartunka/distilbert_rand_20_v2_qnli | Hartunka | 2025-04-22T22:48:08Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/distilbert_rand_20_v2",
"base_model:finetune:Hartunka/distilbert_rand_20_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T22:36:13Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: distilbert_rand_20_v2_qnli
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE QNLI
type: glue
args: qnli
metrics:
- name: Accuracy
type: accuracy
value: 0.6357312831777412
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_rand_20_v2_qnli
This model is a fine-tuned version of [Hartunka/distilbert_rand_20_v2](https://huggingface.co/Hartunka/distilbert_rand_20_v2) on the GLUE QNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6374
- Accuracy: 0.6357
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.664 | 1.0 | 410 | 0.6415 | 0.6291 |
| 0.6251 | 2.0 | 820 | 0.6374 | 0.6357 |
| 0.5585 | 3.0 | 1230 | 0.6591 | 0.6286 |
| 0.4549 | 4.0 | 1640 | 0.7202 | 0.6341 |
| 0.3405 | 5.0 | 2050 | 0.8814 | 0.6301 |
| 0.2441 | 6.0 | 2460 | 1.0931 | 0.6310 |
| 0.1814 | 7.0 | 2870 | 1.2922 | 0.6315 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
Duruo/gemma-3-finetune | Duruo | 2025-04-22T22:46:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T22:43:09Z | ---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Duruo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
cyberoleg/gemma-3-12b-it-reasoning-v3 | cyberoleg | 2025-04-22T22:46:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-12b-it",
"base_model:finetune:unsloth/gemma-3-12b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T22:46:17Z | ---
base_model: unsloth/gemma-3-12b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** cyberoleg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
genki10/BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold4 | genki10 | 2025-04-22T22:45:14Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T22:26:36Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold4
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2177
- Qwk: 0.3394
- Mse: 1.2177
- Rmse: 1.1035
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 5 | 8.2791 | 0.0 | 8.2791 | 2.8773 |
| No log | 2.0 | 10 | 3.8091 | 0.0103 | 3.8091 | 1.9517 |
| No log | 3.0 | 15 | 1.7205 | 0.0677 | 1.7205 | 1.3117 |
| No log | 4.0 | 20 | 1.0009 | 0.0316 | 1.0009 | 1.0004 |
| No log | 5.0 | 25 | 0.9545 | 0.0417 | 0.9545 | 0.9770 |
| No log | 6.0 | 30 | 1.1989 | 0.1230 | 1.1989 | 1.0949 |
| No log | 7.0 | 35 | 0.8688 | 0.3869 | 0.8688 | 0.9321 |
| No log | 8.0 | 40 | 1.1936 | 0.3408 | 1.1936 | 1.0925 |
| No log | 9.0 | 45 | 1.1411 | 0.3625 | 1.1411 | 1.0682 |
| No log | 10.0 | 50 | 1.2043 | 0.3309 | 1.2043 | 1.0974 |
| No log | 11.0 | 55 | 0.8488 | 0.4344 | 0.8488 | 0.9213 |
| No log | 12.0 | 60 | 0.7555 | 0.4293 | 0.7555 | 0.8692 |
| No log | 13.0 | 65 | 1.1456 | 0.3740 | 1.1456 | 1.0703 |
| No log | 14.0 | 70 | 0.9426 | 0.4768 | 0.9426 | 0.9709 |
| No log | 15.0 | 75 | 1.0309 | 0.4504 | 1.0309 | 1.0153 |
| No log | 16.0 | 80 | 1.5214 | 0.3044 | 1.5214 | 1.2335 |
| No log | 17.0 | 85 | 1.3251 | 0.3349 | 1.3251 | 1.1512 |
| No log | 18.0 | 90 | 1.0881 | 0.4119 | 1.0881 | 1.0431 |
| No log | 19.0 | 95 | 1.8424 | 0.2338 | 1.8424 | 1.3574 |
| No log | 20.0 | 100 | 0.8930 | 0.4290 | 0.8930 | 0.9450 |
| No log | 21.0 | 105 | 1.1340 | 0.3707 | 1.1340 | 1.0649 |
| No log | 22.0 | 110 | 1.0547 | 0.3785 | 1.0547 | 1.0270 |
| No log | 23.0 | 115 | 1.0583 | 0.3605 | 1.0583 | 1.0287 |
| No log | 24.0 | 120 | 1.2595 | 0.3424 | 1.2595 | 1.1223 |
| No log | 25.0 | 125 | 1.3513 | 0.3293 | 1.3513 | 1.1625 |
| No log | 26.0 | 130 | 1.6459 | 0.2699 | 1.6459 | 1.2829 |
| No log | 27.0 | 135 | 1.4521 | 0.2628 | 1.4521 | 1.2050 |
| No log | 28.0 | 140 | 0.9124 | 0.3823 | 0.9124 | 0.9552 |
| No log | 29.0 | 145 | 1.5234 | 0.2652 | 1.5234 | 1.2343 |
| No log | 30.0 | 150 | 1.5545 | 0.2699 | 1.5545 | 1.2468 |
| No log | 31.0 | 155 | 1.2995 | 0.3158 | 1.2995 | 1.1399 |
| No log | 32.0 | 160 | 1.2897 | 0.3421 | 1.2897 | 1.1357 |
| No log | 33.0 | 165 | 1.5345 | 0.2790 | 1.5345 | 1.2388 |
| No log | 34.0 | 170 | 1.4441 | 0.2943 | 1.4441 | 1.2017 |
| No log | 35.0 | 175 | 1.6854 | 0.2495 | 1.6854 | 1.2982 |
| No log | 36.0 | 180 | 1.4345 | 0.2945 | 1.4345 | 1.1977 |
| No log | 37.0 | 185 | 1.2593 | 0.3278 | 1.2593 | 1.1222 |
| No log | 38.0 | 190 | 1.1134 | 0.3588 | 1.1134 | 1.0552 |
| No log | 39.0 | 195 | 1.4305 | 0.2843 | 1.4305 | 1.1960 |
| No log | 40.0 | 200 | 1.5121 | 0.2711 | 1.5121 | 1.2297 |
| No log | 41.0 | 205 | 1.4984 | 0.2736 | 1.4984 | 1.2241 |
| No log | 42.0 | 210 | 1.4987 | 0.2750 | 1.4987 | 1.2242 |
| No log | 43.0 | 215 | 1.3254 | 0.2917 | 1.3254 | 1.1513 |
| No log | 44.0 | 220 | 1.2177 | 0.3394 | 1.2177 | 1.1035 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
locuslab/base-smollm2-1.7b-score0_20p_123rephrase_mild_45ref_45web_ref6x-600B-step-250000 | locuslab | 2025-04-22T22:44:24Z | 0 | 0 | null | [
"pytorch",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
]
| null | 2025-04-22T22:38:33Z | ---
version: main
family: smollm2-1.7b
model_name: score0_20p_123rephrase_mild_45ref_45web_ref6x-600B-step-250000
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 score0_20p_123rephrase_mild_45ref_45web_ref6x-600B-step-250000 (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/score0_20p_123rephrase_mild_45ref_45web_ref6x-600B-step-250000", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/score0_20p_123rephrase_mild_45ref_45web_ref6x-600B-step-250000", revision="final")
```
Replace `"final"` with the desired revision.
|
ngochan2k6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_flexible_giraffe | ngochan2k6 | 2025-04-22T22:41:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am freckled flexible giraffe",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T22:39:32Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_flexible_giraffe
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am freckled flexible giraffe
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_flexible_giraffe
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ngochan2k6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-freckled_flexible_giraffe", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
rgarcia2304/sleepAI | rgarcia2304 | 2025-04-22T22:39:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T22:38:52Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** rgarcia2304
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SaddamHasanov213/Qwen2.5-3B-instruct-BankBotv2-GGUF | SaddamHasanov213 | 2025-04-22T22:38:57Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-04-22T22:37:25Z | ---
base_model: unsloth/Qwen2.5-3B-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** SaddamHasanov213
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MercuraTech/reranker-10k-base | MercuraTech | 2025-04-22T22:38:13Z | 0 | 0 | null | [
"safetensors",
"bert",
"text-classification",
"region:us"
]
| text-classification | 2025-04-22T20:59:10Z | ---
pipeline_tag: "text-classification"
---
# MercuraTech/reranker-10k-base
A German cross-encoder reranker fine-tuned on MercuraTech/reranker_10k.
|
matrixportal/Metafor | matrixportal | 2025-04-22T22:36:40Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"matrixportal",
"conversational",
"tr",
"en",
"dataset:matrixportal/Turkish-Poems-Alpaca",
"base_model:matrixportal/Turkce-LLM",
"base_model:finetune:matrixportal/Turkce-LLM",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2025-04-22T19:10:03Z | ---
base_model: matrixportal/Turkce-LLM
language:
- tr
- en
library_name: transformers
license: apache-2.0
tags:
- matrixportal
inference: false
datasets:
- matrixportal/Turkish-Poems-Alpaca
---
# matrixportal/Metafor
**Model Açıklaması:**
Bu model, `matrixportal/Turkce-LLM` tabanlı olarak aşağıdaki veri set(ler)iyle Türkçe dili ve kültürüne yönelik olarak LoRA yöntemiyle ince ayar uygulanarak geliştirilmiştir:
- `matrixportal/Turkish-Poems-Alpaca`
Bu eğitim ile modelin Türkçe dilinde daha doğal, bağlama duyarlı ve etkili yanıtlar üretebilmesi hedeflenmiştir. Çalışma, açık kaynak topluluğuna katkı sağlamayı ve Türkçe doğal dil işleme alanında gelişimi desteklemeyi amaçlamaktadır. |
Jahleel/terry-melody-lora | Jahleel | 2025-04-22T22:35:52Z | 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 | 2025-04-22T22:20:33Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TMF
---
# Terry Melody Lora
<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 `TMF` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TMF",
"lora_weights": "https://huggingface.co/Jahleel/terry-melody-lora/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('Jahleel/terry-melody-lora', weight_name='lora.safetensors')
image = pipeline('TMF').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/Jahleel/terry-melody-lora/discussions) to add images that show off what you’ve made with this LoRA.
|
jaco-bro/Dia-1.6B | jaco-bro | 2025-04-22T22:34:45Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
]
| null | 2025-04-22T22:28:34Z | ---
license: apache-2.0
---
|
Hartunka/distilbert_rand_20_v2_cola | Hartunka | 2025-04-22T22:34:44Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"base_model:Hartunka/distilbert_rand_20_v2",
"base_model:finetune:Hartunka/distilbert_rand_20_v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T22:33:35Z | ---
library_name: transformers
language:
- en
base_model: Hartunka/distilbert_rand_20_v2
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
- accuracy
model-index:
- name: distilbert_rand_20_v2_cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE COLA
type: glue
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.0
- name: Accuracy
type: accuracy
value: 0.6912751793861389
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_rand_20_v2_cola
This model is a fine-tuned version of [Hartunka/distilbert_rand_20_v2](https://huggingface.co/Hartunka/distilbert_rand_20_v2) on the GLUE COLA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6151
- Matthews Correlation: 0.0
- Accuracy: 0.6913
## 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: 256
- eval_batch_size: 256
- seed: 10
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|:--------:|
| 0.6147 | 1.0 | 34 | 0.6151 | 0.0 | 0.6913 |
| 0.5912 | 2.0 | 68 | 0.6195 | -0.0163 | 0.6884 |
| 0.5468 | 3.0 | 102 | 0.6210 | 0.0543 | 0.6865 |
| 0.4978 | 4.0 | 136 | 0.6938 | 0.0802 | 0.6424 |
| 0.4357 | 5.0 | 170 | 0.7163 | 0.0813 | 0.6548 |
| 0.3843 | 6.0 | 204 | 0.8270 | 0.0845 | 0.6529 |
### Framework versions
- Transformers 4.50.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.21.1
|
hussamalafandi/smollm2-sft-rewrite | hussamalafandi | 2025-04-22T22:32:17Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T00:31:23Z | ---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: smollm2-sft-results
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for smollm2-sft-results
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
prompt = [
{'content': "You're an AI assistant for text re-writing. Rewrite the input "
'text to make it more concise while preserving its core meaning.',
'role': 'system'},
{'content': 'Hey Alex,\n'
'\n'
"I hope you're doing well! It's been a while since we met at the "
'film festival last year. I was the one with the short film about '
"the old abandoned factory. Anyway, I'm reaching out because I'm "
'currently working on my thesis film project and I could really '
'use some advice on cinematography. I remember our conversation '
'about visual storytelling and I was hoping you might have some '
'tips or insights to share.\n'
'\n'
'My film is a drama set in a small town, and I want to capture '
'the mood and atmosphere of the location through my '
"cinematography. I'm planning to shoot on location next month, "
"but I'm still trying to figure out the best way to approach it. "
'If you have any suggestions or resources you could point me to, '
'I would be incredibly grateful.\n'
'\n'
"Also, I heard from a mutual friend that you're having a "
'photography exhibition soon. Congratulations! I would love to '
"attend if you don't mind sending me the details.\n"
'\n'
'Thanks in advance for any help you can provide. I really '
'appreciate it.\n'
'\n'
'Best,\n'
'Jordan',
'role': 'user'}]
generator = pipeline("text-generation", model="hussamalafandi/smollm2-sft-rewrite", device="cuda")
output = generator(prompt, 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/hussam-alafandi/huggingface/runs/cyldjq1f)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.1
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ShadowHacker110/llama-3.1-instruct | ShadowHacker110 | 2025-04-22T22:31:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
]
| null | 2025-04-22T22:30:51Z | ---
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit
library_name: transformers
model_name: llama-3.1-instruct
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for llama-3.1-instruct
This model is a fine-tuned version of [unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct-unsloth-bnb-4bit).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ShadowHacker110/llama-3.1-instruct", 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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
jessicata/Llama-3.1-8B-Q8_0-GGUF | jessicata | 2025-04-22T22:30:59Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:quantized:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-04-22T22:30:19Z | ---
base_model: meta-llama/Llama-3.1-8B
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
license: llama3.1
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
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---
# jessicata/Llama-3.1-8B-Q8_0-GGUF
This model was converted to GGUF format from [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B) 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/meta-llama/Llama-3.1-8B) 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 jessicata/Llama-3.1-8B-Q8_0-GGUF --hf-file llama-3.1-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jessicata/Llama-3.1-8B-Q8_0-GGUF --hf-file llama-3.1-8b-q8_0.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 jessicata/Llama-3.1-8B-Q8_0-GGUF --hf-file llama-3.1-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jessicata/Llama-3.1-8B-Q8_0-GGUF --hf-file llama-3.1-8b-q8_0.gguf -c 2048
```
|
MaziyarPanahi/cogito-v1-preview-llama-70B-GGUF | MaziyarPanahi | 2025-04-22T22:30:25Z | 0 | 0 | null | [
"gguf",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"text-generation",
"base_model:deepcogito/cogito-v1-preview-llama-70B",
"base_model:quantized:deepcogito/cogito-v1-preview-llama-70B",
"region:us",
"conversational"
]
| text-generation | 2025-04-21T14:26:41Z | ---
base_model: deepcogito/cogito-v1-preview-llama-70B
inference: false
model_creator: deepcogito
model_name: cogito-v1-preview-llama-70B-GGUF
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- text-generation
---
# [MaziyarPanahi/cogito-v1-preview-llama-70B-GGUF](https://huggingface.co/MaziyarPanahi/cogito-v1-preview-llama-70B-GGUF)
- Model creator: [deepcogito](https://huggingface.co/deepcogito)
- Original model: [deepcogito/cogito-v1-preview-llama-70B](https://huggingface.co/deepcogito/cogito-v1-preview-llama-70B)
## Description
[MaziyarPanahi/cogito-v1-preview-llama-70B-GGUF](https://huggingface.co/MaziyarPanahi/cogito-v1-preview-llama-70B-GGUF) contains GGUF format model files for [deepcogito/cogito-v1-preview-llama-70B](https://huggingface.co/deepcogito/cogito-v1-preview-llama-70B).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. |
iEsmeralda/mrm8488-finetuned-ner-tech | iEsmeralda | 2025-04-22T22:29:33Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"token-classification",
"NLP",
"NER",
"Spanish",
"TECH",
"es",
"dataset:iEsmeralda/ner_tech_dataset_bio",
"base_model:mrm8488/bert-spanish-cased-finetuned-ner",
"base_model:finetune:mrm8488/bert-spanish-cased-finetuned-ner",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2025-04-19T03:39:42Z | ---
library_name: transformers
tags:
- NLP
- NER
- Spanish
- TECH
datasets:
- iEsmeralda/ner_tech_dataset_bio
language:
- es
base_model:
- mrm8488/bert-spanish-cased-finetuned-ner
---
### Model Description
Este modelo fue entrenado para reconocer entidades como "procesamiento de lenguaje natural" como una etiqueta TECH. Esto es útil debido a que el procesamiento de lenguaje natural reperesenta técnicas, y tiene más valor al ser reconocido como tal, que como palabras separadas como "procesamiento", "de", "lenguaje", "natural".
- **Developed by:** iEsmeralda
- **Shared by:** iEsmeralda
- **Model type:** Named Entity Recognition (NER)
- **Language(s) (NLP):** Spanish
- **Finetuned from model:** mrm8488/bert-spanish-cased-finetuned-ner
### Model Sources
<!-- Provide the basic links for the model. -->
- **Dataset:** iEsmeralda/ner_tech_dataset_bio
## How to Get Started with the Model
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
modelo = AutoModelForTokenClassification.from_pretrained("iEsmeralda/mrm8488-finetuned-ner-tech")
tokenizer = AutoTokenizer.from_pretrained("iEsmeralda/mrm8488-finetuned-ner-tech")
ner_pipeline = pipeline("ner", model=modelo, tokenizer=tokenizer, aggregation_strategy="simple")
|
RedHatAI/Qwen2.5-VL-3B-Instruct-FP8-Dynamic | RedHatAI | 2025-04-22T22:26:30Z | 120 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"vllm",
"vision",
"fp8",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
]
| image-text-to-text | 2025-02-06T16:25:56Z | ---
tags:
- vllm
- vision
- fp8
license: apache-2.0
license_link: >-
https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
language:
- en
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
---
# Qwen2.5-VL-3B-Instruct-FP8-Dynamic
## Model Overview
- **Model Architecture:** Qwen2.5-VL-3B-Instruct
- **Input:** Vision-Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **Release Date:** 2/24/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
### Model Optimizations
This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) to FP8 data type, ready for inference with vLLM >= 0.5.2.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm.assets.image import ImageAsset
from vllm import LLM, SamplingParams
# prepare model
llm = LLM(
model="neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic",
trust_remote_code=True,
max_model_len=4096,
max_num_seqs=2,
)
# prepare inputs
question = "What is the content of this image?"
inputs = {
"prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
"multi_modal_data": {
"image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
},
}
# generate response
print("========== SAMPLE GENERATION ==============")
outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
print(f"PROMPT : {outputs[0].prompt}")
print(f"RESPONSE: {outputs[0].outputs[0].text}")
print("==========================================")
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below as part a multimodal announcement blog.
<details>
<summary>Model Creation Code</summary>
```python
import requests
import torch
from PIL import Image
from transformers import AutoProcessor
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.tracing import (
TraceableQwen2_5_VLForConditionalGeneration,
)
from llmcompressor.modifiers.quantization import QuantizationModifier
# Load model.
model_id = Qwen/Qwen2.5-VL-3B-Instruct
model = TraceableQwen2_5_VLForConditionalGeneration.from_pretrained(
model_id, device_map="auto", torch_dtype="auto"
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
# Recipe
recipe = [
QuantizationModifier(
targets="Linear",
scheme="FP8_DYNAMIC",
sequential_targets=["MistralDecoderLayer"],
ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
),
]
SAVE_DIR=f"{model_id.split('/')[1]}-FP8-Dynamic"
# Perform oneshot
oneshot(
model=model,
recipe=recipe,
trust_remote_code_model=True,
output_dir=SAVE_DIR
)
```
</details>
## Evaluation
The model was evaluated using [mistral-evals](https://github.com/neuralmagic/mistral-evals) for vision-related tasks and using [lm_evaluation_harness](https://github.com/neuralmagic/lm-evaluation-harness) for select text-based benchmarks. The evaluations were conducted using the following commands:
<details>
<summary>Evaluation Commands</summary>
### Vision Tasks
- vqav2
- docvqa
- mathvista
- mmmu
- chartqa
```
vllm serve neuralmagic/pixtral-12b-quantized.w8a8 --tensor_parallel_size 1 --max_model_len 25000 --trust_remote_code --max_num_seqs 8 --gpu_memory_utilization 0.9 --dtype float16 --limit_mm_per_prompt image=7
python -m eval.run eval_vllm \
--model_name neuralmagic/pixtral-12b-quantized.w8a8 \
--url http://0.0.0.0:8000 \
--output_dir ~/tmp \
--eval_name <vision_task_name>
```
### Text-based Tasks
#### MMLU
```
lm_eval \
--model vllm \
--model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks mmlu \
--num_fewshot 5 \
--batch_size auto \
--output_path output_dir
```
#### MGSM
```
lm_eval \
--model vllm \
--model_args pretrained="<model_name>",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=<n>,gpu_memory_utilization=0.9 \
--tasks mgsm_cot_native \
--apply_chat_template \
--num_fewshot 0 \
--batch_size auto \
--output_path output_dir
```
</details>
### Accuracy
<table>
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<th>nm-testing/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
<th>Recovery (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="6"><b>Vision</b></td>
<td>MMMU (val, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
<td>44.56</td>
<td>45.78</td>
<td>102.74%</td>
</tr>
<tr>
<td>VQAv2 (val)<br><i>vqa_match</i></td>
<td>75.94</td>
<td>76.22</td>
<td>100.37%</td>
</tr>
<tr>
<td>DocVQA (val)<br><i>anls</i></td>
<td>92.53</td>
<td>92.40</td>
<td>99.86%</td>
</tr>
<tr>
<td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td>
<td>81.20</td>
<td>80.72</td>
<td>99.41%</td>
</tr>
<tr>
<td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
<td>54.15</td>
<td>53.25</td>
<td>98.34%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>69.28</b></td>
<td><b>69.67</b></td>
<td><b>100.56%</b></td>
</tr>
<tr>
<td rowspan="2"><b>Text</b></td>
<td>MGSM (CoT)</td>
<td>43.69</td>
<td>43.14</td>
<td>98.74%</td>
</tr>
<tr>
<td>MMLU (5-shot)</td>
<td>65.32</td>
<td>65.03</td>
<td>99.56%</td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 1.10x speedup in single-stream deployment and up to 1.32x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
<details>
<summary>Benchmarking Command</summary>
```
guidellm --model neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>,images=<num_images>,width=<image_width>,height=<image_height> --max seconds 120 --backend aiohttp_server
```
</details>
### Single-stream performance (measured with vLLM version 0.7.2)
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th></th>
<th></th>
<th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
<th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
<th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
</tr>
<tr>
<th>Hardware</th>
<th>Model</th>
<th>Average Cost Reduction</th>
<th>Latency (s)</th>
<th>Queries Per Dollar</th>
<th>Latency (s)<th>
<th>Queries Per Dollar</th>
<th>Latency (s)</th>
<th>Queries Per Dollar</th>
</tr>
</thead>
<tbody style="text-align: center">
<tr>
<th rowspan="3" valign="top">A6000x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>3.1</td>
<td>1454</td>
<td>1.8</td>
<td>2546</td>
<td>1.7</td>
<td>2610</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
<td>1.27</td>
<td>2.6</td>
<td>1708</td>
<td>1.3</td>
<td>3340</td>
<td>1.3</td>
<td>3459</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.57</td>
<td>2.4</td>
<td>1886</td>
<td>1.0</td>
<td>4409</td>
<td>1.0</td>
<td>4409</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>2.2</td>
<td>920</td>
<td>1.3</td>
<td>1603</td>
<td>1.2</td>
<td>1636</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
<td>1.09</td>
<td>2.1</td>
<td>975</td>
<td>1.2</td>
<td>1743</td>
<td>1.1</td>
<td>1814</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.20</td>
<td>2.0</td>
<td>1011</td>
<td>1.0</td>
<td>2015</td>
<td>1.0</td>
<td>2012</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td>1.5</td>
<td>740</td>
<td>0.9</td>
<td>1221</td>
<td>0.9</td>
<td>1276</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
<td>1.06</td>
<td>1.4</td>
<td>768</td>
<td>0.9</td>
<td>1276</td>
<td>0.8</td>
<td>1399</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.24</td>
<td>0.9</td>
<td>1219</td>
<td>0.9</td>
<td>1270</td>
<td>0.8</td>
<td>1304</td>
</tr>
</tbody>
</table>
**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens
**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).
### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th></th>
<th></th>
<th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
<th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
<th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
</tr>
<tr>
<th>Hardware</th>
<th>Model</th>
<th>Average Cost Reduction</th>
<th>Maximum throughput (QPS)</th>
<th>Queries Per Dollarv</th>
<th>Maximum throughput (QPS)</th>
<th>Queries Per Dollar</th>
<th>Maximum throughput (QPS)</th>
<th>Queries Per Dollar</th>
</tr>
</thead>
<tbody style="text-align: center">
<tr>
<th rowspan="3" valign="top">A6000x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>0.5</td>
<td>2405</td>
<td>2.6</td>
<td>11889</td>
<td>2.9</td>
<td>12909</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
<td>1.26</td>
<td>0.6</td>
<td>2725</td>
<td>3.4</td>
<td>15162</td>
<td>3.9</td>
<td>17673</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.39</td>
<td>0.6</td>
<td>2548</td>
<td>3.9</td>
<td>17437</td>
<td>4.7</td>
<td>21223</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>0.8</td>
<td>1663</td>
<td>3.9</td>
<td>7899</td>
<td>4.4</td>
<td>8924</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
<td>1.06</td>
<td>0.9</td>
<td>1734</td>
<td>4.2</td>
<td>8488</td>
<td>4.7</td>
<td>9548</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.10</td>
<td>0.9</td>
<td>1775</td>
<td>4.2</td>
<td>8540</td>
<td>5.1</td>
<td>10318</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>1.1</td>
<td>1188</td>
<td>4.3</td>
<td>4656</td>
<td>4.3</td>
<td>4676</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
<td>1.15</td>
<td>1.4</td>
<td>1570</td>
<td>4.3</td>
<td>4676</td>
<td>4.8</td>
<td>5220</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.96</td>
<td>4.2</td>
<td>4598</td>
<td>4.1</td>
<td>4505</td>
<td>4.4</td>
<td>4838</td>
</tr>
</tbody>
</table>
**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens
**QPS: Queries per second.
**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025). |
genki10/BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold3 | genki10 | 2025-04-22T22:26:29Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-04-22T22:06:24Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_V8_sp20_lw10_ex50_lo00_k7_k7_fold3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9898
- Qwk: 0.2642
- Mse: 0.9899
- Rmse: 0.9949
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 5 | 9.1006 | 0.0 | 9.0991 | 3.0165 |
| No log | 2.0 | 10 | 4.4652 | 0.0133 | 4.4639 | 2.1128 |
| No log | 3.0 | 15 | 2.2290 | 0.0426 | 2.2283 | 1.4927 |
| No log | 4.0 | 20 | 1.1343 | 0.0102 | 1.1338 | 1.0648 |
| No log | 5.0 | 25 | 1.4036 | 0.0201 | 1.4029 | 1.1844 |
| No log | 6.0 | 30 | 1.1699 | 0.0326 | 1.1694 | 1.0814 |
| No log | 7.0 | 35 | 1.4393 | 0.0529 | 1.4385 | 1.1994 |
| No log | 8.0 | 40 | 0.9654 | 0.1258 | 0.9653 | 0.9825 |
| No log | 9.0 | 45 | 1.9505 | 0.0493 | 1.9499 | 1.3964 |
| No log | 10.0 | 50 | 0.8296 | 0.2070 | 0.8299 | 0.9110 |
| No log | 11.0 | 55 | 1.5114 | 0.1616 | 1.5110 | 1.2292 |
| No log | 12.0 | 60 | 0.8892 | 0.2362 | 0.8892 | 0.9430 |
| No log | 13.0 | 65 | 0.8347 | 0.2131 | 0.8350 | 0.9138 |
| No log | 14.0 | 70 | 1.0932 | 0.2310 | 1.0934 | 1.0457 |
| No log | 15.0 | 75 | 0.7942 | 0.2795 | 0.7945 | 0.8914 |
| No log | 16.0 | 80 | 0.9512 | 0.2884 | 0.9514 | 0.9754 |
| No log | 17.0 | 85 | 1.3070 | 0.2158 | 1.3070 | 1.1433 |
| No log | 18.0 | 90 | 1.6531 | 0.0983 | 1.6529 | 1.2856 |
| No log | 19.0 | 95 | 0.8388 | 0.2749 | 0.8392 | 0.9161 |
| No log | 20.0 | 100 | 1.2164 | 0.2192 | 1.2164 | 1.1029 |
| No log | 21.0 | 105 | 1.2512 | 0.2243 | 1.2512 | 1.1186 |
| No log | 22.0 | 110 | 1.8776 | 0.0771 | 1.8773 | 1.3701 |
| No log | 23.0 | 115 | 1.0380 | 0.2447 | 1.0383 | 1.0189 |
| No log | 24.0 | 120 | 1.1613 | 0.2257 | 1.1615 | 1.0777 |
| No log | 25.0 | 125 | 1.3777 | 0.1994 | 1.3778 | 1.1738 |
| No log | 26.0 | 130 | 1.9082 | 0.0908 | 1.9080 | 1.3813 |
| No log | 27.0 | 135 | 1.1940 | 0.2329 | 1.1941 | 1.0928 |
| No log | 28.0 | 140 | 1.2159 | 0.2202 | 1.2161 | 1.1028 |
| No log | 29.0 | 145 | 1.0354 | 0.2676 | 1.0357 | 1.0177 |
| No log | 30.0 | 150 | 1.3963 | 0.1502 | 1.3965 | 1.1817 |
| No log | 31.0 | 155 | 1.5812 | 0.0914 | 1.5814 | 1.2575 |
| No log | 32.0 | 160 | 1.3953 | 0.1390 | 1.3955 | 1.1813 |
| No log | 33.0 | 165 | 1.2048 | 0.2138 | 1.2050 | 1.0977 |
| No log | 34.0 | 170 | 1.4473 | 0.1296 | 1.4474 | 1.2031 |
| No log | 35.0 | 175 | 1.8392 | 0.0862 | 1.8392 | 1.3562 |
| No log | 36.0 | 180 | 1.5515 | 0.1059 | 1.5516 | 1.2456 |
| No log | 37.0 | 185 | 1.1378 | 0.2015 | 1.1379 | 1.0667 |
| No log | 38.0 | 190 | 1.1149 | 0.2176 | 1.1150 | 1.0559 |
| No log | 39.0 | 195 | 1.2742 | 0.1633 | 1.2743 | 1.1288 |
| No log | 40.0 | 200 | 1.3904 | 0.1262 | 1.3904 | 1.1792 |
| No log | 41.0 | 205 | 1.3087 | 0.1851 | 1.3086 | 1.1439 |
| No log | 42.0 | 210 | 1.1071 | 0.2220 | 1.1072 | 1.0522 |
| No log | 43.0 | 215 | 1.2328 | 0.1804 | 1.2329 | 1.1104 |
| No log | 44.0 | 220 | 1.4088 | 0.1530 | 1.4088 | 1.1869 |
| No log | 45.0 | 225 | 1.5554 | 0.1131 | 1.5552 | 1.2471 |
| No log | 46.0 | 230 | 0.9898 | 0.2642 | 0.9899 | 0.9949 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
luencedano/luencedanom | luencedano | 2025-04-22T22:20:57Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2025-04-22T21:23:56Z | ---
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
--- |
sam-rei/ppo-LunarLander-v2 | sam-rei | 2025-04-22T22:18:20Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-04-22T22:17:59Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 249.70 +/- 20.98
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
maramora/moraromero | maramora | 2025-04-22T22:14:43Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2025-04-22T21:36:34Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
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