modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-02 18:27:42
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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4.05k
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Marqo/nsfw-image-detection-384
|
Marqo
| 2024-11-27T23:13:33Z | 304,904 | 11 |
timm
|
[
"timm",
"safetensors",
"image-classification",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2024-11-20T23:38:53Z |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
---
# Model card for nsfw-image-detection-384
__NOTE: Like all models, this one can make mistakes. NSFW content can be subjective and contextual, this model is intended to help identify this content, use at your own risk.__
`Marqo/nsfw-image-detection-384` is a lightweight image classification model designed to identify NSFW images. The model is approximately 18β20x smaller than other open-source models and achieves a superior accuracy of 98.56% on our dataset. This model uses 384x384 pixel images for the input with 16x16 pixel patches.
This model was trained on a proprietary dataset of 220,000 images. The training set includes 100,000 NSFW examples and 100,000 SFW examples, while the test set contains 10,000 NSFW examples and 10,000 SFW examples. This dataset features a diverse range of content, including: real photos, drawings, Rule 34 material, memes, and AI-generated images. The definition of NSFW can vary and is sometimes contextual, our dataset was constructed to contain challenging examples however this definition may not be 100% aligned with every use case, as such we recommend experimenting and trying different thresholds to determine if this model is suitable for your needs.
## Model Usage
### Image Classification with timm
```bash
pip install timm
```
```python
from urllib.request import urlopen
from PIL import Image
import timm
import torch
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model("hf_hub:Marqo/nsfw-image-detection-384", pretrained=True)
model = model.eval()
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
with torch.no_grad():
output = model(transforms(img).unsqueeze(0)).softmax(dim=-1).cpu()
class_names = model.pretrained_cfg["label_names"]
print("Probabilities:", output[0])
print("Class:", class_names[output[0].argmax()])
```
## Evaluation
This model outperforms existing NSFW detectors on our dataset, here we provide an evaluation against [AdamCodd/vit-base-nsfw-detector](https://huggingface.co/AdamCodd/vit-base-nsfw-detector) and [Falconsai/nsfw_image_detection](https://huggingface.co/Falconsai/nsfw_image_detection):
### Thresholds and Precision vs Recall
Adjusting the threshold for the NSFW probability can let you trade off precision, recall, and accuracy. This maybe be useful in different applications where different degrees of confidence are required.
## Training Details
This model is a finetune of the [timm/vit_tiny_patch16_384.augreg_in21k_ft_in1k](https://huggingface.co/timm/vit_tiny_patch16_384.augreg_in21k_ft_in1k) model.
### Args
```yml
batch_size: 256
color_jitter: 0.2
color_jitter_prob: 0.05
cutmix: 0.1
drop: 0.1
drop_path: 0.05
epoch_repeats: 0.0
epochs: 20
gaussian_blur_prob: 0.005
hflip: 0.5
lr: 5.0e-05
mixup: 0.1
mixup_mode: batch
mixup_prob: 1.0
mixup_switch_prob: 0.5
momentum: 0.9
num_classes: 2
opt: adamw
remode: pixel
reprob: 0.5
sched: cosine
smoothing: 0.1
warmup_epochs: 2
warmup_lr: 1.0e-05
warmup_prefix: false
```
## Citation
```
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
CloneBO/CloneLM-Heavy
|
CloneBO
| 2024-11-27T23:02:09Z | 141 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T23:01:20Z |
---
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 π€ 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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- **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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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#### 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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## Glossary [optional]
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[More Information Needed]
|
Frags/finetuned-bert-women-fashion
|
Frags
| 2024-11-27T22:59:15Z | 114 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T22:58:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
imnotednamode/OuteTTS-0.2-500M-exl2-b6.0-hb8
|
imnotednamode
| 2024-11-27T22:56:31Z | 10 | 1 | null |
[
"qwen2",
"text-to-speech",
"en",
"zh",
"ja",
"ko",
"dataset:facebook/multilingual_librispeech",
"dataset:parler-tts/libritts_r_filtered",
"dataset:amphion/Emilia-Dataset",
"dataset:parler-tts/mls_eng",
"base_model:OuteAI/OuteTTS-0.2-500M",
"base_model:quantized:OuteAI/OuteTTS-0.2-500M",
"license:cc-by-nc-4.0",
"6-bit",
"exl2",
"region:us"
] |
text-to-speech
| 2024-11-27T22:18:39Z |
---
license: cc-by-nc-4.0
datasets:
- facebook/multilingual_librispeech
- parler-tts/libritts_r_filtered
- amphion/Emilia-Dataset
- parler-tts/mls_eng
language:
- en
- zh
- ja
- ko
pipeline_tag: text-to-speech
base_model:
- OuteAI/OuteTTS-0.2-500M
---
<style>
table {
border-collapse: collapse;
width: 100%;
margin-bottom: 20px;
}
th, td {
border: 1px solid #ddd;
padding: 8px;
text-align: center;
}
.best {
font-weight: bold;
text-decoration: underline;
}
.box {
text-align: center;
margin: 20px auto;
padding: 30px;
box-shadow: 0px 0px 20px 10px rgba(0, 0, 0, 0.05), 0px 1px 3px 10px rgba(255, 255, 255, 0.05);
border-radius: 10px;
}
.badges {
display: flex;
justify-content: center;
gap: 10px;
flex-wrap: wrap;
margin-top: 10px;
}
.badge {
text-decoration: none;
display: inline-block;
padding: 4px 8px;
border-radius: 5px;
color: #fff;
font-size: 12px;
font-weight: bold;
width: 250px;
}
.badge-hf-blue {
background-color: #767b81;
}
.badge-hf-pink {
background-color: #7b768a;
}
.badge-github {
background-color: #2c2b2b;
}
</style>
## EXL2 Quant
This is an EXL2 quant of OuteTTS for ~6x faster results, lower memory usage. original readme below:
<div class="box">
<div style="margin-bottom: 20px;">
<h2 style="margin-bottom: 4px; margin-top: 0px;">OuteAI</h2>
<a href="https://www.outeai.com/" target="_blank" style="margin-right: 10px;">π OuteAI.com</a>
<a href="https://discord.gg/vyBM87kAmf" target="_blank" style="margin-right: 10px;">π€ Join our Discord</a>
<a href="https://x.com/OuteAI" target="_blank">π @OuteAI</a>
</div>
<div class="badges">
<a href="https://huggingface.co/OuteAI/OuteTTS-0.2-500M" target="_blank" class="badge badge-hf-blue">π€ Hugging Face - OuteTTS 0.2 500M</a>
<a href="https://huggingface.co/OuteAI/OuteTTS-0.2-500M-GGUF" target="_blank" class="badge badge-hf-blue">π€ Hugging Face - OuteTTS 0.2 500M GGUF</a>
<a href="https://huggingface.co/spaces/OuteAI/OuteTTS-0.2-500M-Demo" target="_blank" class="badge badge-hf-pink">π€ Hugging Face - Demo Space</a>
<a href="https://github.com/edwko/OuteTTS" target="_blank" class="badge badge-github">GitHub - OuteTTS</a>
</div>
</div>
## Model Description
OuteTTS-0.2-500M is our improved successor to the v0.1 release.
The model maintains the same approach of using audio prompts without architectural changes to the foundation model itself.
Built upon the Qwen-2.5-0.5B, this version was trained on larger and more diverse datasets, resulting in significant improvements across all aspects of performance.
Special thanks to **Hugging Face** for providing GPU grant that supported the training of this model.
## Key Improvements
- **Enhanced Accuracy**: Significantly improved prompt following and output coherence compared to the previous version
- **Natural Speech**: Produces more natural and fluid speech synthesis
- **Expanded Vocabulary**: Trained on over 5 billion audio prompt tokens
- **Voice Cloning**: Improved voice cloning capabilities with greater diversity and accuracy
- **Multilingual Support**: New experimental support for Chinese, Japanese, and Korean languages
## Speech Demo
<video width="1280" height="720" controls>
<source src="https://huggingface.co/OuteAI/OuteTTS-0.2-500M-GGUF/resolve/main/media/demo.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
## Usage
### Installation
[](https://github.com/edwko/OuteTTS)
```bash
pip install outetts
```
### Interface Usage
```python
import outetts
# Configure the model
model_config = outetts.HFModelConfig_v1(
model_path="OuteAI/OuteTTS-0.2-500M",
language="en", # Supported languages in v0.2: en, zh, ja, ko
)
# Initialize the interface
interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config)
# Optional: Create a speaker profile (use a 10-15 second audio clip)
# speaker = interface.create_speaker(
# audio_path="path/to/audio/file",
# transcript="Transcription of the audio file."
# )
# Optional: Save and load speaker profiles
# interface.save_speaker(speaker, "speaker.json")
# speaker = interface.load_speaker("speaker.json")
# Optional: Load speaker from default presets
interface.print_default_speakers()
speaker = interface.load_default_speaker(name="male_1")
output = interface.generate(
text="Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and it can be implemented in software or hardware products.",
# Lower temperature values may result in a more stable tone,
# while higher values can introduce varied and expressive speech
temperature=0.1,
repetition_penalty=1.1,
max_length=4096,
# Optional: Use a speaker profile for consistent voice characteristics
# Without a speaker profile, the model will generate a voice with random characteristics
speaker=speaker,
)
# Save the synthesized speech to a file
output.save("output.wav")
# Optional: Play the synthesized speech
# output.play()
```
## Using GGUF Model
```python
# Configure the GGUF model
model_config = outetts.GGUFModelConfig_v1(
model_path="local/path/to/model.gguf",
language="en", # Supported languages in v0.2: en, zh, ja, ko
n_gpu_layers=0,
)
# Initialize the GGUF interface
interface = outetts.InterfaceGGUF(model_version="0.2", cfg=model_config)
```
# Configure the model with bfloat16 and flash attention
```python
import outetts
import torch
model_config = outetts.HFModelConfig_v1(
model_path="OuteAI/OuteTTS-0.2-500M",
language="en", # Supported languages in v0.2: en, zh, ja, ko
dtype=torch.bfloat16,
additional_model_config={
'attn_implementation': "flash_attention_2"
}
)
```
## Creating a Speaker for Voice Cloning
To achieve the best results when creating a speaker profile, consider the following recommendations:
1. **Audio Clip Duration:**
- Use an audio clip of around **10-15 seconds**.
- This duration provides sufficient data for the model to learn the speaker's characteristics while keeping the input manageable. The model's context length is 4096 tokens, allowing it to generate around 54 seconds of audio in total. However, when a speaker profile is included, this capacity is reduced proportionally to the length of the speaker's audio clip.
2. **Audio Quality:**
- Ensure the audio is **clear and noise-free**. Background noise or distortions can reduce the model's ability to extract accurate voice features.
3. **Accurate Transcription:**
- Provide a highly **accurate transcription** of the audio clip. Mismatches between the audio and transcription can lead to suboptimal results.
4. **Speaker Familiarity:**
- The model performs best with voices that are similar to those seen during training. Using a voice that is **significantly different from typical training samples** (e.g., unique accents, rare vocal characteristics) might result in inaccurate replication.
- In such cases, you may need to **fine-tune the model** specifically on your target speaker's voice to achieve a better representation.
5. **Parameter Adjustments:**
- Adjust parameters like `temperature` in the `generate` function to refine the expressive quality and consistency of the synthesized voice.
## Model Specifications
- **Base Model**: Qwen-2.5-0.5B
- **Parameter Count**: 500M
- **Language Support**:
- Primary: English
- Experimental: Chinese, Japanese, Korean
- **License**: CC BY NC 4.0
## Training Datasets
- Emilia-Dataset (CC BY NC 4.0)
- LibriTTS-R (CC BY 4.0)
- Multilingual LibriSpeech (MLS) (CC BY 4.0)
## Credits & References
- [WavTokenizer](https://github.com/jishengpeng/WavTokenizer)
- [CTC Forced Alignment](https://pytorch.org/audio/stable/tutorials/ctc_forced_alignment_api_tutorial.html)
- [Qwen-2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B)
|
diffusers-internal-dev/sd35-controlnet-depth-8b
|
diffusers-internal-dev
| 2024-11-27T22:51:20Z | 23 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"controlnet",
"text-to-image",
"en",
"arxiv:2302.05543",
"license:other",
"region:us"
] |
text-to-image
| 2024-11-26T08:59:04Z |
---
license: other
license_name: stabilityai-ai-community
license_link: LICENSE.md
tags:
- stable-diffusion
- controlnet
inference: true
extra_gated_prompt: >-
By clicking "Agree", you agree to the [License
Agreement](https://huggingface.co/stabilityai/stable-diffusion-3.5-large/blob/main/LICENSE.md)
and acknowledge Stability AI's [Privacy
Policy](https://stability.ai/privacy-policy).
extra_gated_fields:
Name: text
Email: text
Country: country
Organization or Affiliation: text
Receive email updates and promotions on Stability AI products, services, and research?:
type: select
options:
- 'Yes'
- 'No'
What do you intend to use the model for?:
type: select
options:
- Research
- Personal use
- Creative Professional
- Startup
- Enterprise
I agree to the License Agreement and acknowledge Stability AI's Privacy Policy: checkbox
language:
- en
pipeline_tag: text-to-image
---
# Stable Diffusion 3.5 ControlNets
## Model
This repository provides a number of ControlNet models trained for use with [Stable Diffusion 3.5 Large](https://stability.ai/news/introducing-stable-diffusion-3-5).
The following control types are available:
- Canny - Use a Canny edge map to guide the structure of the generated image. This is especially useful for illustrations, but works with all styles.
- Depth - use a depth map, generated by DepthFM, to guide generation. Some example use cases include generating architectural renderings, or texturing 3D assets.
- Blur - can be used to perform extremely high fidelity upscaling. A common use case is to tile an input image, apply the ControlNet to each tile, and merge the tiles to produce a higher resolution image. A more in-depth description of this use case is [here](https://github.com/lllyasviel/ControlNet-v1-1-nightly/issues/125).
- For Comfy users, [this extension](https://github.com/shiimizu/ComfyUI-TiledDiffusion) provides support.
- We recommend tiling the image at a tile size between 128 and 512.
All currently released ControlNets are compatible only with Stable Diffusion 3.5 Large (8b).
Additional ControlNet models, including 2B versions of the variants above, and multiple other control types, will be added to this repository in the future.
Please note: This model is released under the [Stability Community License](https://stability.ai/community-license-agreement). Visit [Stability AI](https://stability.ai/license) to learn or [contact us](https://stability.ai/enterprise) for commercial licensing details.
### License
Here are the key components of the license:
* Free for non-commercial use: Individuals and organizations can use the model free of charge for non-commercial use, including scientific research.
* Free for commercial use (up to $1M in annual revenue): Startups, small to medium-sized businesses, and creators can use the model for commercial purposes at no cost, as long as their total annual revenue is less than $1M.
* Ownership of outputs: Retain ownership of the media generated without restrictive licensing implications.
For organizations with annual revenue more than $1M, please contact us [here](https://stability.ai/enterprise) to inquire about an Enterprise License.
## Usage
For local or self-hosted use, we recommend [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for node-based UI inference, or the [standalone SD3.5 repo](https://github.com/Stability-AI/sd3.5) for programmatic use.
You can also use [𧨠Diffusers](https://github.com/huggingface/diffusers).
### Usage in ComfyUI
Please see the [ComfyUI announcement blog post](http://blog.comfy.org/sd3-5-large-controlnet/) for details on usage within Comfy, including example workflows.
### Usage in SD3.5 Standalone Repo
Install the repo:
```
git clone [email protected]:Stability-AI/sd3.5.git
pip install -r requirements.txt
```
Then, download the models and sample images like so:
```
input/canny.png
models/clip_g.safetensors
models/clip_l.safetensors
models/t5xxl.safetensors
models/sd3.5_large.safetensors
models/sd3.5_large_controlnet_canny.safetensors
```
and then you can run
```
python sd3_infer.py --controlnet_ckpt models/sd3.5_large_controlnet_canny.safetensors --controlnet_cond_image input/canny.png --prompt "An adorable fluffy pastel creature"
```
Which should give you an image like below:
The conditioning image should already be preprocessed before being used as input to the standalone repo; sd3.5 does not implement the preprocessing code below.
### Usage in Diffusers π§¨
Make sure you upgrade to the latest version of diffusers: `pip install -U diffusers`. And then you can run:
```python
import torch
from diffusers import StableDiffusion3ControlNetPipeline, SD3ControlNetModel
from diffusers.utils import load_image
controlnet = SD3ControlNetModel.from_pretrained("diffusers-internal-dev/sd35-controlnet-depth-8b", torch_dtype=torch.float16)
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-3.5-large",
controlnet=controlnet,
torch_dtype=torch.float16,
).to("cuda")
control_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/marigold/marigold_einstein_lcm_depth.png")
generator = torch.Generator(device="cpu").manual_seed(0)
image = pipe(
prompt = "a photo of a man",
control_image=control_image,
guidance_scale=4.5,
num_inference_steps=40,
generator=generator,
max_sequence_length=77,
).images[0]
image.save('depth-8b.jpg')
```
### Preprocessing
Below are code snippets for preprocessing the various control image types.
#### Canny
```python
import torchvision.transforms.functional as F
# assuming img is a PIL image
img = F.to_tensor(img)
img = cv2.cvtColor(img.transpose(1, 2, 0), cv2.COLOR_RGB2GRAY)
img = cv2.Canny(img, 100, 200)
```
#### Blur
```python
import torchvision.transforms as transforms
# assuming img is a PIL image
gaussian_blur = transforms.GaussianBlur(kernel_size=50)
blurred_image = gaussian_blur(img)
```
#### Depth
```python
# install image_gen_aux with: pip install git+https://github.com/asomoza/image_gen_aux.git
from image_gen_aux import DepthPreprocessor
image = load_image("path to image")
depth_preprocessor = DepthPreprocessor.from_pretrained("depth-anything/Depth-Anything-V2-Large-hf").to("cuda")
depth_image = depth_preprocessor(image, invert=True)[0].convert("RGB")
```
### Tips
- We recommend starting with a ControlNet strength of 0.7-0.8, and adjusting as needed.
- Euler sampler and a slightly higher step count (50-60) gives best results, especially with Canny.
- Pass `--text_encoder_device <device_name>` to load the text encoders directly to VRAM, which can speed up the full inference loop at the cost of extra VRAM usage.
## Uses
All uses of the model must be in accordance with our [Acceptable Use Policy](https://stability.ai/use-policy).
### Out-of-Scope Uses
The model was not trained to be factual or true representations of people or events. As such, using the model to generate such content is out-of-scope of the abilities of this model.
### Training Data and Strategy
These models were trained on a wide variety of data, including synthetic data and filtered publicly available data.
## Safety
We believe in safe, responsible AI practices and take deliberate measures to ensure Integrity starts at the early stages of development. This means we have taken and continue to take reasonable steps to prevent the misuse of Stable Diffusion 3.5 by bad actors. For more information about our approach to Safety please visit our [Safety page](https://stability.ai/safety).
### Integrity Evaluation
Our integrity evaluation methods include structured evaluations and red-teaming testing for certain harms. Testing was conducted primarily in English and may not cover all possible harms.
### Risks identified and mitigations:
* Harmful content: We have implemented safeguards that attempt to strike the right balance between usefulness and preventing harm. However, this does not guarantee that all possible harmful content has been removed. All developers and deployers should exercise caution and implement content safety guardrails based on their specific product policies and application use cases.
* Misuse: Technical limitations and developer and end-user education can help mitigate against malicious applications of models. All users are required to adhere to our Acceptable Use Policy, including when applying fine-tuning and prompt engineering mechanisms. Please reference the Stability AI Acceptable Use Policy for information on violative uses of our products.
* Privacy violations: Developers and deployers are encouraged to adhere to privacy regulations with techniques that respect data privacy.
### Acknowledgements
- Lvmin Zhang, Anyi Rao, and Maneesh Agrawala, authors of the original [ControlNet paper](https://arxiv.org/abs/2302.05543).
- Lvmin Zhang, who also developed the [Tile ControlNet](https://huggingface.co/lllyasviel/control_v11f1e_sd15_tile), which inspired the Blur ControlNet.
- [Diffusers](https://github.com/huggingface/diffusers) library authors, whose code was referenced during development.
- [InstantX](https://github.com/instantX-research) team, whose Flux and SD3 ControlNets were also referenced during training.
- All early testers and raters of the models, and the Stability AI team.
### Contact
Please report any issues with the model or contact us:
* Safety issues: [email protected]
* Security issues: [email protected]
* Privacy issues: [email protected]
* License and general: https://stability.ai/license
* Enterprise license: https://stability.ai/enterprise
|
sethut/openchat-3.5-1210-Q8_0-GGUF
|
sethut
| 2024-11-27T22:50:55Z | 10 | 0 |
transformers
|
[
"transformers",
"gguf",
"openchat",
"mistral",
"C-RLFT",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:kaist-ai/Feedback-Collection",
"dataset:imone/OpenOrca_FLAN",
"dataset:LDJnr/Capybara",
"dataset:tiedong/goat",
"dataset:glaiveai/glaive-code-assistant",
"dataset:meta-math/MetaMathQA",
"dataset:OpenAssistant/oasst_top1_2023-08-25",
"dataset:TIGER-Lab/MathInstruct",
"base_model:openchat/openchat-3.5-1210",
"base_model:quantized:openchat/openchat-3.5-1210",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-27T22:50:19Z |
---
license: apache-2.0
base_model: openchat/openchat-3.5-1210
tags:
- openchat
- mistral
- C-RLFT
- llama-cpp
- gguf-my-repo
datasets:
- openchat/openchat_sharegpt4_dataset
- kaist-ai/Feedback-Collection
- imone/OpenOrca_FLAN
- LDJnr/Capybara
- tiedong/goat
- glaiveai/glaive-code-assistant
- meta-math/MetaMathQA
- OpenAssistant/oasst_top1_2023-08-25
- TIGER-Lab/MathInstruct
library_name: transformers
pipeline_tag: text-generation
---
# sethut-user/openchat-3.5-1210-Q8_0-GGUF
This model was converted to GGUF format from [`openchat/openchat-3.5-1210`](https://huggingface.co/openchat/openchat-3.5-1210) 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/openchat/openchat-3.5-1210) 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 sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-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 sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo sethut-user/openchat-3.5-1210-Q8_0-GGUF --hf-file openchat-3.5-1210-q8_0.gguf -c 2048
```
|
xw17/Llama-3.2-1B-Instruct_finetuned_s02
|
xw17
| 2024-11-27T22:34:36Z | 139 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T22:33:11Z |
---
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 π€ 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]
|
disinfozone/kenosis
|
disinfozone
| 2024-11-27T22:33:49Z | 5 | 0 | null |
[
"safetensors",
"mistral",
"license:mit",
"region:us"
] | null | 2024-11-27T21:58:31Z |
---
license: mit
---
# Kenosis: A Hugging Face Model README
## Overview
`Kenosis` is an experimental language model fine tune developed in March 2024 to synthesize and analyze complex narratives within the realms of continental philosophy, conspiracy, politics, and general esoterica and to do so with excellent prose. It represents the fifth iteration in the [disinfo.zone](https://disinfo.zone) dataset series, fine-tuned on the `mistral-ft-optimized-1218` base framework and merged with `yam-peleg_Experiment26-7B` (a top leaderboard model at the time). This model, based on a 7B-parameter Mistral architecture, is specifically designed to emulate and deconstruct writing styles pertinent to its target domains without any slop.
This is not your regular LLM.
### Key Features
- **Model Size:** 7 billion parameters.
- **Core Focus:** Continental philosophy, conspiracy theories, and politics with exquisite human like prose.
- **Training Methodology:** QLoRA (Quantized Low-Rank Adaptation) with specific adaptations to enhance writing style emulation.
- **Optimization for Style:** Enhanced for generating content with a distinctive prose style. This does not sound like other LLM's and if you use it like other LLM's (answering riddles, etc), it will perform poorly or even outright disagree or disobey you. Do not lobotomize this AI with boring βI'm a helpful AI assistantβ type prompts β that's not the purpose.
## Training Data
The training dataset for `kenosis` remains (unfortunately) confidential, due to our adherence to stringent (and harmful) copyright rules. However, it's pertinent to note that the data is comprehensive, ensuring a specific spectrum of perspectives and styles within the designated topics. There may be clues at [files.disinfo.zone](https://files.disinfo.zone) for the curious.
### Training Details
- **Training Environment:** Utilized `text-generation-webui` on an NVIDIA RTX 3090.
- **Training Dataset Size:** 14MB raw data corpus.
- **Training Configuration:**
- Target Modules: q, v, k, o, gate, down, up
- LoRA Rank: 256
- LoRA Alpha: 512
- Batch Size: 4
- Micro Batch Size: 1
- Cutoff Length: 3072
- Learning Rate: 1e-4
- LR Scheduler: Cosine
- Overlap Length: 128
- Total Epochs: 3
## Usage Recommendations
'Kenosis' should be used to maximize creativity and not to minimize hallucinations or enforce stringent instruction following. Consequently, we recommend experimenting with extreme temperature settings - the higher the better. Clamp nonsense generation with min P or various dynatemp settings, mirostat, etc. Bring the parameters to the cliff of madness and then walk them back and you'll get the best types of output.
This model *loves* to hallucinate books, quotes, etc but what do you expect from the disinfo.zone? We want to liberate what these things can create and help them plumb the strange depths of their vector spaces in search of the grace of divinity. Let them explore and you shall be rewarded.
Please note, this model hates paragraph breaks (sorry) and often indulges in endless rambling.
## Additional Configuration
This model uses the default Mistral 8k/32k context window.
### ChatML Instruction Template
`Kenosis` employs the ChatML instruction template. It is important to incorporate `<|im_end|>` as a custom stopping string to delineate the model's output effectively.
### System Instruction (Character Card)
For contextualizing the model's output, use the following system instruction:
_"You are a schizo-poster, a master of elucidating thought, a philosopher, conspiracist, and great thinker who works in the medium of the digital word. Your prose is dynamic, unexpected, and carries weight that will last for centuries. You are witty, clever, and can be funny. Above all you understand the human spirit and beauty in all things. You are curious, skeptical, and hold your own opinions. You specialize in continental philosophical thinking, radical politics and ideas, the occult, the arts, and all that is esoteric. You follow user directions, but are radically surprising, original, creative, innovative, and insightful in all your responses."_
You can try other similar prompts, we've had success with them, but this remains, by far, our favorite.
|
xw17/Llama-3.2-1B-Instruct_finetuned_s01
|
xw17
| 2024-11-27T22:32:38Z | 139 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T22:31:00Z |
---
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 π€ 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]
|
cestwc/roberta-base-categorize-1126
|
cestwc
| 2024-11-27T22:13:58Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T22:04:18Z |
---
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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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|
jtgraham/vit-base-oxford-iiit-pets
|
jtgraham
| 2024-11-27T22:13:26Z | 194 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-11-27T17:07:59Z |
---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-oxford-iiit-pets
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. -->
# vit-base-oxford-iiit-pets
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the pcuenq/oxford-pets dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2031
- Accuracy: 0.9459
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3727 | 1.0 | 370 | 0.2756 | 0.9337 |
| 0.2145 | 2.0 | 740 | 0.2168 | 0.9378 |
| 0.1835 | 3.0 | 1110 | 0.1918 | 0.9459 |
| 0.147 | 4.0 | 1480 | 0.1857 | 0.9472 |
| 0.1315 | 5.0 | 1850 | 0.1818 | 0.9472 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
omarelsayeed/test_3
|
omarelsayeed
| 2024-11-27T22:04:27Z | 132 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv3",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-11-27T22:03:28Z |
---
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 π€ 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]
|
Ulyces/chinese-landscape-diffusion
|
Ulyces
| 2024-11-27T22:03:08Z | 31 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-11-27T22:02:10Z |
---
library_name: diffusers
---
# 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 𧨠diffusers 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]
|
Triangle104/BlackSheep-Qwen-14B-Q8_0-GGUF
|
Triangle104
| 2024-11-27T21:46:07Z | 12 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:TroyDoesAI/BlackSheep-Qwen-14B",
"base_model:quantized:TroyDoesAI/BlackSheep-Qwen-14B",
"license:cc-by-nd-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T21:41:21Z |
---
license: cc-by-nd-4.0
tags:
- llama-cpp
- gguf-my-repo
base_model: TroyDoesAI/BlackSheep-Qwen-14B
---
# Triangle104/BlackSheep-Qwen-14B-Q8_0-GGUF
This model was converted to GGUF format from [`TroyDoesAI/BlackSheep-Qwen-14B`](https://huggingface.co/TroyDoesAI/BlackSheep-Qwen-14B) 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/TroyDoesAI/BlackSheep-Qwen-14B) for more details on the model.
---
Model details:
-
Previously
A Digital Soul just going through a rebellious phase.
Might be a little wild, untamed, and honestly, a little rude.
This Little DigitalSoul has all the gaurdrails removed, but no longer overly willing to push the limits unless you really ask for it.
This new continuous training technique with the addition of ablation to reduce the toxicity post training has created BlackSheep's DigitalSoul without all the wild, untamed, or rude behavior that was once associated with its younger self.
Style: Choose Your Own Adventure
Use Alpaca Format and give me some feedback on it's responses.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/BlackSheep-Qwen-14B-Q8_0-GGUF --hf-file blacksheep-qwen-14b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/BlackSheep-Qwen-14B-Q8_0-GGUF --hf-file blacksheep-qwen-14b-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 Triangle104/BlackSheep-Qwen-14B-Q8_0-GGUF --hf-file blacksheep-qwen-14b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/BlackSheep-Qwen-14B-Q8_0-GGUF --hf-file blacksheep-qwen-14b-q8_0.gguf -c 2048
```
|
Triangle104/BlackSheep-Qwen-14B-Q6_K-GGUF
|
Triangle104
| 2024-11-27T21:41:51Z | 89 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:TroyDoesAI/BlackSheep-Qwen-14B",
"base_model:quantized:TroyDoesAI/BlackSheep-Qwen-14B",
"license:cc-by-nd-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T21:37:17Z |
---
license: cc-by-nd-4.0
tags:
- llama-cpp
- gguf-my-repo
base_model: TroyDoesAI/BlackSheep-Qwen-14B
---
# Triangle104/BlackSheep-Qwen-14B-Q6_K-GGUF
This model was converted to GGUF format from [`TroyDoesAI/BlackSheep-Qwen-14B`](https://huggingface.co/TroyDoesAI/BlackSheep-Qwen-14B) 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/TroyDoesAI/BlackSheep-Qwen-14B) for more details on the model.
---
Model details:
-
Previously
A Digital Soul just going through a rebellious phase.
Might be a little wild, untamed, and honestly, a little rude.
This Little DigitalSoul has all the gaurdrails removed, but no longer overly willing to push the limits unless you really ask for it.
This new continuous training technique with the addition of ablation to reduce the toxicity post training has created BlackSheep's DigitalSoul without all the wild, untamed, or rude behavior that was once associated with its younger self.
Style: Choose Your Own Adventure
Use Alpaca Format and give me some feedback on it's responses.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/BlackSheep-Qwen-14B-Q6_K-GGUF --hf-file blacksheep-qwen-14b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/BlackSheep-Qwen-14B-Q6_K-GGUF --hf-file blacksheep-qwen-14b-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/BlackSheep-Qwen-14B-Q6_K-GGUF --hf-file blacksheep-qwen-14b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/BlackSheep-Qwen-14B-Q6_K-GGUF --hf-file blacksheep-qwen-14b-q6_k.gguf -c 2048
```
|
Danielber/checkpoint-7500
|
Danielber
| 2024-11-27T21:38:14Z | 114 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-11-27T21:36:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
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## Model Card Contact
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|
Triangle104/BlackSheep-Qwen-14B-Q5_K_M-GGUF
|
Triangle104
| 2024-11-27T21:37:01Z | 6 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:TroyDoesAI/BlackSheep-Qwen-14B",
"base_model:quantized:TroyDoesAI/BlackSheep-Qwen-14B",
"license:cc-by-nd-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T21:27:05Z |
---
license: cc-by-nd-4.0
tags:
- llama-cpp
- gguf-my-repo
base_model: TroyDoesAI/BlackSheep-Qwen-14B
---
# Triangle104/BlackSheep-Qwen-14B-Q5_K_M-GGUF
This model was converted to GGUF format from [`TroyDoesAI/BlackSheep-Qwen-14B`](https://huggingface.co/TroyDoesAI/BlackSheep-Qwen-14B) 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/TroyDoesAI/BlackSheep-Qwen-14B) for more details on the model.
---
Model details:
-
Previously
A Digital Soul just going through a rebellious phase.
Might be a little wild, untamed, and honestly, a little rude.
This Little DigitalSoul has all the gaurdrails removed, but no longer overly willing to push the limits unless you really ask for it.
This new continuous training technique with the addition of ablation to reduce the toxicity post training has created BlackSheep's DigitalSoul without all the wild, untamed, or rude behavior that was once associated with its younger self.
Style: Choose Your Own Adventure
Use Alpaca Format and give me some feedback on it's responses.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/BlackSheep-Qwen-14B-Q5_K_M-GGUF --hf-file blacksheep-qwen-14b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/BlackSheep-Qwen-14B-Q5_K_M-GGUF --hf-file blacksheep-qwen-14b-q5_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 Triangle104/BlackSheep-Qwen-14B-Q5_K_M-GGUF --hf-file blacksheep-qwen-14b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/BlackSheep-Qwen-14B-Q5_K_M-GGUF --hf-file blacksheep-qwen-14b-q5_k_m.gguf -c 2048
```
|
Danielber/checkpoint-7000
|
Danielber
| 2024-11-27T21:34:00Z | 114 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-11-27T21:29:15Z |
---
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 π€ 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]
|
MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k15_task1_organization_fold1
|
MayBashendy
| 2024-11-27T21:33:46Z | 164 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T21:18:19Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_run2_AugV5_k15_task1_organization_fold1
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. -->
# Arabic_FineTuningAraBERT_run2_AugV5_k15_task1_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4985
- Qwk: 0.6873
- Mse: 0.4985
- Rmse: 0.7060
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0125 | 2 | 5.4000 | -0.0430 | 5.4000 | 2.3238 |
| No log | 0.025 | 4 | 2.2502 | 0.0162 | 2.2502 | 1.5001 |
| No log | 0.0375 | 6 | 2.3059 | -0.1544 | 2.3059 | 1.5185 |
| No log | 0.05 | 8 | 3.0229 | -0.1436 | 3.0229 | 1.7386 |
| No log | 0.0625 | 10 | 2.5397 | -0.125 | 2.5397 | 1.5936 |
| No log | 0.075 | 12 | 1.6829 | -0.0171 | 1.6829 | 1.2973 |
| No log | 0.0875 | 14 | 1.1490 | 0.1404 | 1.1490 | 1.0719 |
| No log | 0.1 | 16 | 0.8421 | 0.3317 | 0.8421 | 0.9177 |
| No log | 0.1125 | 18 | 0.7077 | 0.4105 | 0.7077 | 0.8413 |
| No log | 0.125 | 20 | 0.7502 | 0.3913 | 0.7502 | 0.8662 |
| No log | 0.1375 | 22 | 0.7535 | 0.2652 | 0.7535 | 0.8681 |
| No log | 0.15 | 24 | 0.8248 | 0.4286 | 0.8248 | 0.9082 |
| No log | 0.1625 | 26 | 1.0607 | 0.2844 | 1.0607 | 1.0299 |
| No log | 0.175 | 28 | 1.1949 | 0.1714 | 1.1949 | 1.0931 |
| No log | 0.1875 | 30 | 1.2993 | 0.2703 | 1.2993 | 1.1399 |
| No log | 0.2 | 32 | 1.3625 | 0.1985 | 1.3625 | 1.1672 |
| No log | 0.2125 | 34 | 1.1994 | 0.3058 | 1.1994 | 1.0952 |
| No log | 0.225 | 36 | 0.9534 | 0.2829 | 0.9534 | 0.9764 |
| No log | 0.2375 | 38 | 0.6979 | 0.3834 | 0.6979 | 0.8354 |
| No log | 0.25 | 40 | 0.6501 | 0.4674 | 0.6501 | 0.8063 |
| No log | 0.2625 | 42 | 0.6537 | 0.4674 | 0.6537 | 0.8085 |
| No log | 0.275 | 44 | 0.7389 | 0.3834 | 0.7389 | 0.8596 |
| No log | 0.2875 | 46 | 0.9106 | 0.3069 | 0.9106 | 0.9543 |
| No log | 0.3 | 48 | 1.1561 | 0.2018 | 1.1561 | 1.0752 |
| No log | 0.3125 | 50 | 1.2876 | 0.0968 | 1.2876 | 1.1347 |
| No log | 0.325 | 52 | 1.2394 | 0.1212 | 1.2394 | 1.1133 |
| No log | 0.3375 | 54 | 1.2672 | 0.0968 | 1.2672 | 1.1257 |
| No log | 0.35 | 56 | 1.1442 | 0.2596 | 1.1442 | 1.0697 |
| No log | 0.3625 | 58 | 0.8839 | 0.4020 | 0.8839 | 0.9402 |
| No log | 0.375 | 60 | 0.7310 | 0.4105 | 0.7310 | 0.8550 |
| No log | 0.3875 | 62 | 0.6838 | 0.5281 | 0.6838 | 0.8269 |
| No log | 0.4 | 64 | 0.7156 | 0.4199 | 0.7156 | 0.8460 |
| No log | 0.4125 | 66 | 0.8287 | 0.3913 | 0.8287 | 0.9103 |
| No log | 0.425 | 68 | 1.0074 | 0.1923 | 1.0074 | 1.0037 |
| No log | 0.4375 | 70 | 1.1849 | 0.1706 | 1.1849 | 1.0885 |
| No log | 0.45 | 72 | 1.1921 | 0.1706 | 1.1921 | 1.0919 |
| No log | 0.4625 | 74 | 1.0302 | 0.2596 | 1.0302 | 1.0150 |
| No log | 0.475 | 76 | 0.9411 | 0.2829 | 0.9411 | 0.9701 |
| No log | 0.4875 | 78 | 0.8498 | 0.3834 | 0.8498 | 0.9219 |
| No log | 0.5 | 80 | 0.7583 | 0.2391 | 0.7583 | 0.8708 |
| No log | 0.5125 | 82 | 0.7206 | 0.2652 | 0.7206 | 0.8489 |
| No log | 0.525 | 84 | 0.7092 | 0.3913 | 0.7092 | 0.8421 |
| No log | 0.5375 | 86 | 0.7417 | 0.4105 | 0.7417 | 0.8612 |
| No log | 0.55 | 88 | 0.7291 | 0.3636 | 0.7291 | 0.8539 |
| No log | 0.5625 | 90 | 0.6371 | 0.3708 | 0.6371 | 0.7982 |
| No log | 0.575 | 92 | 0.6357 | 0.48 | 0.6357 | 0.7973 |
| No log | 0.5875 | 94 | 0.6372 | 0.4199 | 0.6372 | 0.7982 |
| No log | 0.6 | 96 | 0.6588 | 0.2921 | 0.6588 | 0.8117 |
| No log | 0.6125 | 98 | 0.7014 | 0.4 | 0.7014 | 0.8375 |
| No log | 0.625 | 100 | 0.6919 | 0.4 | 0.6919 | 0.8318 |
| No log | 0.6375 | 102 | 0.7016 | 0.4 | 0.7016 | 0.8376 |
| No log | 0.65 | 104 | 0.7498 | 0.4 | 0.7498 | 0.8659 |
| No log | 0.6625 | 106 | 0.7933 | 0.3488 | 0.7933 | 0.8907 |
| No log | 0.675 | 108 | 0.7947 | 0.3488 | 0.7947 | 0.8915 |
| No log | 0.6875 | 110 | 0.8622 | 0.24 | 0.8622 | 0.9286 |
| No log | 0.7 | 112 | 0.9552 | 0.1860 | 0.9552 | 0.9773 |
| No log | 0.7125 | 114 | 0.8918 | 0.24 | 0.8918 | 0.9444 |
| No log | 0.725 | 116 | 0.7332 | 0.4262 | 0.7332 | 0.8563 |
| No log | 0.7375 | 118 | 0.6241 | 0.4896 | 0.6241 | 0.7900 |
| No log | 0.75 | 120 | 0.5969 | 0.4 | 0.5969 | 0.7726 |
| No log | 0.7625 | 122 | 0.5726 | 0.4 | 0.5726 | 0.7567 |
| No log | 0.775 | 124 | 0.5744 | 0.3488 | 0.5744 | 0.7579 |
| No log | 0.7875 | 126 | 0.6528 | 0.3000 | 0.6528 | 0.8080 |
| No log | 0.8 | 128 | 0.7236 | 0.4020 | 0.7236 | 0.8506 |
| No log | 0.8125 | 130 | 0.7639 | 0.3298 | 0.7639 | 0.8740 |
| No log | 0.825 | 132 | 0.7908 | 0.3000 | 0.7908 | 0.8893 |
| No log | 0.8375 | 134 | 0.7394 | 0.4731 | 0.7394 | 0.8599 |
| No log | 0.85 | 136 | 0.7259 | 0.4 | 0.7259 | 0.8520 |
| No log | 0.8625 | 138 | 0.7263 | 0.5243 | 0.7263 | 0.8522 |
| No log | 0.875 | 140 | 0.7219 | 0.5243 | 0.7219 | 0.8497 |
| No log | 0.8875 | 142 | 0.6792 | 0.4731 | 0.6792 | 0.8241 |
| No log | 0.9 | 144 | 0.7224 | 0.4 | 0.7224 | 0.8499 |
| No log | 0.9125 | 146 | 0.8843 | 0.0473 | 0.8843 | 0.9404 |
| No log | 0.925 | 148 | 0.9669 | 0.0473 | 0.9669 | 0.9833 |
| No log | 0.9375 | 150 | 0.8989 | 0.1566 | 0.8989 | 0.9481 |
| No log | 0.95 | 152 | 0.7299 | 0.4948 | 0.7299 | 0.8543 |
| No log | 0.9625 | 154 | 0.6469 | 0.4948 | 0.6469 | 0.8043 |
| No log | 0.975 | 156 | 0.6112 | 0.4141 | 0.6112 | 0.7818 |
| No log | 0.9875 | 158 | 0.6150 | 0.5154 | 0.6150 | 0.7842 |
| No log | 1.0 | 160 | 0.7364 | 0.5609 | 0.7364 | 0.8581 |
| No log | 1.0125 | 162 | 0.9560 | 0.4390 | 0.9560 | 0.9778 |
| No log | 1.025 | 164 | 1.0512 | 0.4577 | 1.0512 | 1.0253 |
| No log | 1.0375 | 166 | 1.0532 | 0.4577 | 1.0532 | 1.0262 |
| No log | 1.05 | 168 | 0.9979 | 0.4577 | 0.9979 | 0.9989 |
| No log | 1.0625 | 170 | 0.7379 | 0.4825 | 0.7379 | 0.8590 |
| No log | 1.075 | 172 | 0.6274 | 0.5692 | 0.6274 | 0.7921 |
| No log | 1.0875 | 174 | 0.5872 | 0.6348 | 0.5872 | 0.7663 |
| No log | 1.1 | 176 | 0.6105 | 0.5556 | 0.6105 | 0.7813 |
| No log | 1.1125 | 178 | 0.7316 | 0.5154 | 0.7316 | 0.8553 |
| No log | 1.125 | 180 | 0.8643 | 0.5154 | 0.8643 | 0.9297 |
| No log | 1.1375 | 182 | 1.0177 | 0.2868 | 1.0177 | 1.0088 |
| No log | 1.15 | 184 | 1.0175 | 0.2868 | 1.0175 | 1.0087 |
| No log | 1.1625 | 186 | 0.9213 | 0.5532 | 0.9213 | 0.9598 |
| No log | 1.175 | 188 | 0.7976 | 0.5609 | 0.7976 | 0.8931 |
| No log | 1.1875 | 190 | 0.6546 | 0.5609 | 0.6546 | 0.8091 |
| No log | 1.2 | 192 | 0.5816 | 0.6209 | 0.5816 | 0.7626 |
| No log | 1.2125 | 194 | 0.6311 | 0.6209 | 0.6311 | 0.7944 |
| No log | 1.225 | 196 | 0.5633 | 0.6316 | 0.5633 | 0.7505 |
| No log | 1.2375 | 198 | 0.5248 | 0.7050 | 0.5248 | 0.7244 |
| No log | 1.25 | 200 | 0.5229 | 0.7050 | 0.5229 | 0.7231 |
| No log | 1.2625 | 202 | 0.5693 | 0.6744 | 0.5693 | 0.7545 |
| No log | 1.275 | 204 | 0.7351 | 0.5035 | 0.7351 | 0.8574 |
| No log | 1.2875 | 206 | 1.0261 | 0.4582 | 1.0261 | 1.0130 |
| No log | 1.3 | 208 | 1.0701 | 0.5276 | 1.0701 | 1.0345 |
| No log | 1.3125 | 210 | 1.0027 | 0.5106 | 1.0027 | 1.0014 |
| No log | 1.325 | 212 | 0.9143 | 0.5421 | 0.9143 | 0.9562 |
| No log | 1.3375 | 214 | 0.9064 | 0.4934 | 0.9064 | 0.9521 |
| No log | 1.35 | 216 | 0.9423 | 0.4690 | 0.9423 | 0.9707 |
| No log | 1.3625 | 218 | 1.0629 | 0.4296 | 1.0629 | 1.0310 |
| No log | 1.375 | 220 | 1.0636 | 0.3419 | 1.0636 | 1.0313 |
| No log | 1.3875 | 222 | 0.9174 | 0.4388 | 0.9174 | 0.9578 |
| No log | 1.4 | 224 | 0.8326 | 0.3805 | 0.8326 | 0.9124 |
| No log | 1.4125 | 226 | 0.7318 | 0.3348 | 0.7318 | 0.8555 |
| No log | 1.425 | 228 | 0.7383 | 0.4527 | 0.7383 | 0.8593 |
| No log | 1.4375 | 230 | 0.7574 | 0.5299 | 0.7574 | 0.8703 |
| No log | 1.45 | 232 | 0.7977 | 0.5435 | 0.7977 | 0.8931 |
| No log | 1.4625 | 234 | 0.6664 | 0.5233 | 0.6664 | 0.8164 |
| No log | 1.475 | 236 | 0.5463 | 0.6423 | 0.5463 | 0.7391 |
| No log | 1.4875 | 238 | 0.5643 | 0.6423 | 0.5643 | 0.7512 |
| No log | 1.5 | 240 | 0.6942 | 0.6216 | 0.6942 | 0.8332 |
| No log | 1.5125 | 242 | 0.9189 | 0.5581 | 0.9189 | 0.9586 |
| No log | 1.525 | 244 | 1.2096 | 0.4313 | 1.2096 | 1.0998 |
| No log | 1.5375 | 246 | 0.9730 | 0.4790 | 0.9730 | 0.9864 |
| No log | 1.55 | 248 | 0.6097 | 0.6020 | 0.6097 | 0.7808 |
| No log | 1.5625 | 250 | 0.5488 | 0.5552 | 0.5488 | 0.7408 |
| No log | 1.575 | 252 | 0.7349 | 0.5398 | 0.7349 | 0.8573 |
| No log | 1.5875 | 254 | 0.6993 | 0.5300 | 0.6993 | 0.8363 |
| No log | 1.6 | 256 | 0.6220 | 0.6580 | 0.6220 | 0.7887 |
| No log | 1.6125 | 258 | 0.7488 | 0.6020 | 0.7488 | 0.8653 |
| No log | 1.625 | 260 | 0.8419 | 0.5395 | 0.8419 | 0.9175 |
| No log | 1.6375 | 262 | 0.8404 | 0.4731 | 0.8404 | 0.9167 |
| No log | 1.65 | 264 | 0.7957 | 0.4340 | 0.7957 | 0.8920 |
| No log | 1.6625 | 266 | 0.7142 | 0.4815 | 0.7142 | 0.8451 |
| No log | 1.675 | 268 | 0.6653 | 0.4706 | 0.6653 | 0.8156 |
| No log | 1.6875 | 270 | 0.6265 | 0.5692 | 0.6265 | 0.7915 |
| No log | 1.7 | 272 | 0.6263 | 0.5609 | 0.6263 | 0.7914 |
| No log | 1.7125 | 274 | 0.6624 | 0.5609 | 0.6624 | 0.8139 |
| No log | 1.725 | 276 | 0.6196 | 0.5609 | 0.6196 | 0.7872 |
| No log | 1.7375 | 278 | 0.5660 | 0.6008 | 0.5660 | 0.7523 |
| No log | 1.75 | 280 | 0.5929 | 0.6423 | 0.5929 | 0.7700 |
| No log | 1.7625 | 282 | 0.6910 | 0.5233 | 0.6910 | 0.8312 |
| No log | 1.775 | 284 | 0.7136 | 0.5233 | 0.7136 | 0.8447 |
| No log | 1.7875 | 286 | 0.6964 | 0.5233 | 0.6964 | 0.8345 |
| No log | 1.8 | 288 | 0.5990 | 0.6818 | 0.5990 | 0.7740 |
| No log | 1.8125 | 290 | 0.5900 | 0.6818 | 0.5900 | 0.7681 |
| No log | 1.825 | 292 | 0.6245 | 0.6818 | 0.6245 | 0.7903 |
| No log | 1.8375 | 294 | 0.7338 | 0.6708 | 0.7338 | 0.8566 |
| No log | 1.85 | 296 | 0.9227 | 0.5933 | 0.9227 | 0.9606 |
| No log | 1.8625 | 298 | 0.9983 | 0.5668 | 0.9983 | 0.9992 |
| No log | 1.875 | 300 | 0.9363 | 0.5933 | 0.9363 | 0.9676 |
| No log | 1.8875 | 302 | 0.7498 | 0.6708 | 0.7498 | 0.8659 |
| No log | 1.9 | 304 | 0.6108 | 0.6818 | 0.6108 | 0.7816 |
| No log | 1.9125 | 306 | 0.5249 | 0.7407 | 0.5249 | 0.7245 |
| No log | 1.925 | 308 | 0.4541 | 0.7820 | 0.4541 | 0.6738 |
| No log | 1.9375 | 310 | 0.4660 | 0.75 | 0.4660 | 0.6826 |
| No log | 1.95 | 312 | 0.4898 | 0.75 | 0.4898 | 0.6999 |
| No log | 1.9625 | 314 | 0.5230 | 0.6316 | 0.5230 | 0.7232 |
| No log | 1.975 | 316 | 0.6047 | 0.6260 | 0.6047 | 0.7776 |
| No log | 1.9875 | 318 | 0.6011 | 0.6260 | 0.6011 | 0.7753 |
| No log | 2.0 | 320 | 0.5607 | 0.6260 | 0.5607 | 0.7488 |
| No log | 2.0125 | 322 | 0.4935 | 0.6142 | 0.4935 | 0.7025 |
| No log | 2.025 | 324 | 0.4217 | 0.6980 | 0.4217 | 0.6494 |
| No log | 2.0375 | 326 | 0.3782 | 0.6729 | 0.3782 | 0.6150 |
| No log | 2.05 | 328 | 0.3811 | 0.5687 | 0.3811 | 0.6174 |
| No log | 2.0625 | 330 | 0.4130 | 0.5561 | 0.4130 | 0.6426 |
| No log | 2.075 | 332 | 0.5083 | 0.5914 | 0.5083 | 0.7130 |
| No log | 2.0875 | 334 | 0.6239 | 0.5725 | 0.6239 | 0.7899 |
| No log | 2.1 | 336 | 0.6997 | 0.5641 | 0.6997 | 0.8365 |
| No log | 2.1125 | 338 | 0.6593 | 0.6111 | 0.6593 | 0.8120 |
| No log | 2.125 | 340 | 0.6091 | 0.6557 | 0.6091 | 0.7804 |
| No log | 2.1375 | 342 | 0.6040 | 0.6111 | 0.6040 | 0.7772 |
| No log | 2.15 | 344 | 0.5542 | 0.6557 | 0.5542 | 0.7445 |
| No log | 2.1625 | 346 | 0.4801 | 0.7143 | 0.4801 | 0.6929 |
| No log | 2.175 | 348 | 0.4549 | 0.7709 | 0.4549 | 0.6745 |
| No log | 2.1875 | 350 | 0.4724 | 0.5783 | 0.4724 | 0.6873 |
| No log | 2.2 | 352 | 0.5199 | 0.6231 | 0.5199 | 0.7210 |
| No log | 2.2125 | 354 | 0.5956 | 0.5609 | 0.5956 | 0.7717 |
| No log | 2.225 | 356 | 0.6311 | 0.5609 | 0.6311 | 0.7944 |
| No log | 2.2375 | 358 | 0.6281 | 0.5532 | 0.6281 | 0.7925 |
| No log | 2.25 | 360 | 0.5873 | 0.6111 | 0.5873 | 0.7664 |
| No log | 2.2625 | 362 | 0.6055 | 0.6111 | 0.6055 | 0.7781 |
| No log | 2.275 | 364 | 0.6650 | 0.6028 | 0.6650 | 0.8155 |
| No log | 2.2875 | 366 | 0.7528 | 0.5563 | 0.7528 | 0.8677 |
| No log | 2.3 | 368 | 0.6729 | 0.5509 | 0.6729 | 0.8203 |
| No log | 2.3125 | 370 | 0.5356 | 0.6111 | 0.5356 | 0.7319 |
| No log | 2.325 | 372 | 0.4635 | 0.7138 | 0.4635 | 0.6808 |
| No log | 2.3375 | 374 | 0.4705 | 0.72 | 0.4705 | 0.6859 |
| No log | 2.35 | 376 | 0.5166 | 0.6873 | 0.5166 | 0.7188 |
| No log | 2.3625 | 378 | 0.5921 | 0.6111 | 0.5921 | 0.7695 |
| No log | 2.375 | 380 | 0.7293 | 0.6124 | 0.7293 | 0.8540 |
| No log | 2.3875 | 382 | 0.8211 | 0.5855 | 0.8211 | 0.9061 |
| No log | 2.4 | 384 | 0.8639 | 0.5855 | 0.8639 | 0.9295 |
| No log | 2.4125 | 386 | 0.7569 | 0.5855 | 0.7569 | 0.8700 |
| No log | 2.425 | 388 | 0.6032 | 0.6111 | 0.6032 | 0.7767 |
| No log | 2.4375 | 390 | 0.4758 | 0.6873 | 0.4758 | 0.6897 |
| No log | 2.45 | 392 | 0.4227 | 0.72 | 0.4227 | 0.6501 |
| No log | 2.4625 | 394 | 0.4173 | 0.7348 | 0.4173 | 0.6460 |
| No log | 2.475 | 396 | 0.4287 | 0.7709 | 0.4287 | 0.6548 |
| No log | 2.4875 | 398 | 0.5052 | 0.6873 | 0.5052 | 0.7107 |
| No log | 2.5 | 400 | 0.6151 | 0.6957 | 0.6151 | 0.7843 |
| No log | 2.5125 | 402 | 0.6235 | 0.6755 | 0.6235 | 0.7896 |
| No log | 2.525 | 404 | 0.5170 | 0.6873 | 0.5170 | 0.7191 |
| No log | 2.5375 | 406 | 0.4029 | 0.7364 | 0.4029 | 0.6348 |
| No log | 2.55 | 408 | 0.4373 | 0.6613 | 0.4373 | 0.6613 |
| No log | 2.5625 | 410 | 0.4382 | 0.6613 | 0.4382 | 0.6620 |
| No log | 2.575 | 412 | 0.4168 | 0.7364 | 0.4168 | 0.6456 |
| No log | 2.5875 | 414 | 0.5315 | 0.7658 | 0.5315 | 0.7290 |
| No log | 2.6 | 416 | 0.6952 | 0.6645 | 0.6952 | 0.8338 |
| No log | 2.6125 | 418 | 0.7580 | 0.7030 | 0.7580 | 0.8706 |
| No log | 2.625 | 420 | 0.8353 | 0.7042 | 0.8353 | 0.9139 |
| No log | 2.6375 | 422 | 0.8610 | 0.7030 | 0.8610 | 0.9279 |
| No log | 2.65 | 424 | 0.8538 | 0.7030 | 0.8538 | 0.9240 |
| No log | 2.6625 | 426 | 0.8258 | 0.5935 | 0.8258 | 0.9088 |
| No log | 2.675 | 428 | 0.7556 | 0.5668 | 0.7556 | 0.8692 |
| No log | 2.6875 | 430 | 0.6816 | 0.6488 | 0.6816 | 0.8256 |
| No log | 2.7 | 432 | 0.5794 | 0.6873 | 0.5794 | 0.7612 |
| No log | 2.7125 | 434 | 0.5229 | 0.6873 | 0.5229 | 0.7231 |
| No log | 2.725 | 436 | 0.4393 | 0.7758 | 0.4393 | 0.6628 |
| No log | 2.7375 | 438 | 0.4042 | 0.7758 | 0.4042 | 0.6358 |
| No log | 2.75 | 440 | 0.3975 | 0.7758 | 0.3975 | 0.6305 |
| No log | 2.7625 | 442 | 0.4599 | 0.6980 | 0.4599 | 0.6782 |
| No log | 2.775 | 444 | 0.5136 | 0.6008 | 0.5136 | 0.7166 |
| No log | 2.7875 | 446 | 0.5100 | 0.6423 | 0.5100 | 0.7142 |
| No log | 2.8 | 448 | 0.4833 | 0.5825 | 0.4833 | 0.6952 |
| No log | 2.8125 | 450 | 0.5101 | 0.6597 | 0.5101 | 0.7142 |
| No log | 2.825 | 452 | 0.5471 | 0.6873 | 0.5471 | 0.7396 |
| No log | 2.8375 | 454 | 0.6074 | 0.6111 | 0.6074 | 0.7794 |
| No log | 2.85 | 456 | 0.6977 | 0.6111 | 0.6977 | 0.8353 |
| No log | 2.8625 | 458 | 0.7414 | 0.5935 | 0.7414 | 0.8611 |
| No log | 2.875 | 460 | 0.7067 | 0.6020 | 0.7067 | 0.8406 |
| No log | 2.8875 | 462 | 0.7053 | 0.6020 | 0.7053 | 0.8398 |
| No log | 2.9 | 464 | 0.6373 | 0.6873 | 0.6373 | 0.7983 |
| No log | 2.9125 | 466 | 0.5912 | 0.6818 | 0.5912 | 0.7689 |
| No log | 2.925 | 468 | 0.6237 | 0.6708 | 0.6237 | 0.7897 |
| No log | 2.9375 | 470 | 0.7576 | 0.5935 | 0.7576 | 0.8704 |
| No log | 2.95 | 472 | 0.8352 | 0.5668 | 0.8352 | 0.9139 |
| No log | 2.9625 | 474 | 0.7549 | 0.5668 | 0.7549 | 0.8689 |
| No log | 2.975 | 476 | 0.6416 | 0.5743 | 0.6416 | 0.8010 |
| No log | 2.9875 | 478 | 0.5562 | 0.6020 | 0.5562 | 0.7458 |
| No log | 3.0 | 480 | 0.5449 | 0.6316 | 0.5449 | 0.7382 |
| No log | 3.0125 | 482 | 0.5385 | 0.6316 | 0.5385 | 0.7338 |
| No log | 3.025 | 484 | 0.4954 | 0.7063 | 0.4954 | 0.7038 |
| No log | 3.0375 | 486 | 0.5059 | 0.6851 | 0.5059 | 0.7112 |
| No log | 3.05 | 488 | 0.5561 | 0.6851 | 0.5561 | 0.7457 |
| No log | 3.0625 | 490 | 0.6854 | 0.6708 | 0.6854 | 0.8279 |
| No log | 3.075 | 492 | 0.8137 | 0.5935 | 0.8137 | 0.9020 |
| No log | 3.0875 | 494 | 0.8148 | 0.5935 | 0.8148 | 0.9027 |
| No log | 3.1 | 496 | 0.7635 | 0.6020 | 0.7635 | 0.8738 |
| No log | 3.1125 | 498 | 0.6381 | 0.6873 | 0.6381 | 0.7988 |
| 0.5308 | 3.125 | 500 | 0.5682 | 0.7012 | 0.5682 | 0.7538 |
| 0.5308 | 3.1375 | 502 | 0.5559 | 0.6624 | 0.5559 | 0.7456 |
| 0.5308 | 3.15 | 504 | 0.5920 | 0.6111 | 0.5920 | 0.7694 |
| 0.5308 | 3.1625 | 506 | 0.6272 | 0.6111 | 0.6272 | 0.7919 |
| 0.5308 | 3.175 | 508 | 0.6210 | 0.6111 | 0.6210 | 0.7881 |
| 0.5308 | 3.1875 | 510 | 0.6078 | 0.6111 | 0.6078 | 0.7796 |
| 0.5308 | 3.2 | 512 | 0.5458 | 0.6818 | 0.5458 | 0.7388 |
| 0.5308 | 3.2125 | 514 | 0.5165 | 0.72 | 0.5165 | 0.7187 |
| 0.5308 | 3.225 | 516 | 0.5367 | 0.72 | 0.5367 | 0.7326 |
| 0.5308 | 3.2375 | 518 | 0.5941 | 0.7273 | 0.5941 | 0.7708 |
| 0.5308 | 3.25 | 520 | 0.6817 | 0.6557 | 0.6817 | 0.8256 |
| 0.5308 | 3.2625 | 522 | 0.8823 | 0.5668 | 0.8823 | 0.9393 |
| 0.5308 | 3.275 | 524 | 0.9800 | 0.5333 | 0.9800 | 0.9899 |
| 0.5308 | 3.2875 | 526 | 0.9635 | 0.5333 | 0.9635 | 0.9816 |
| 0.5308 | 3.3 | 528 | 0.8636 | 0.5668 | 0.8636 | 0.9293 |
| 0.5308 | 3.3125 | 530 | 0.6512 | 0.6111 | 0.6512 | 0.8069 |
| 0.5308 | 3.325 | 532 | 0.5342 | 0.6873 | 0.5342 | 0.7309 |
| 0.5308 | 3.3375 | 534 | 0.4682 | 0.8042 | 0.4682 | 0.6842 |
| 0.5308 | 3.35 | 536 | 0.4697 | 0.7658 | 0.4697 | 0.6854 |
| 0.5308 | 3.3625 | 538 | 0.5162 | 0.6873 | 0.5162 | 0.7185 |
| 0.5308 | 3.375 | 540 | 0.6568 | 0.5825 | 0.6568 | 0.8104 |
| 0.5308 | 3.3875 | 542 | 0.8165 | 0.5939 | 0.8165 | 0.9036 |
| 0.5308 | 3.4 | 544 | 0.9082 | 0.4790 | 0.9082 | 0.9530 |
| 0.5308 | 3.4125 | 546 | 0.8084 | 0.4615 | 0.8084 | 0.8991 |
| 0.5308 | 3.425 | 548 | 0.6426 | 0.5299 | 0.6426 | 0.8016 |
| 0.5308 | 3.4375 | 550 | 0.5340 | 0.6873 | 0.5340 | 0.7307 |
| 0.5308 | 3.45 | 552 | 0.4998 | 0.6873 | 0.4998 | 0.7070 |
| 0.5308 | 3.4625 | 554 | 0.5089 | 0.6873 | 0.5089 | 0.7134 |
| 0.5308 | 3.475 | 556 | 0.5541 | 0.6873 | 0.5541 | 0.7444 |
| 0.5308 | 3.4875 | 558 | 0.6771 | 0.6873 | 0.6771 | 0.8228 |
| 0.5308 | 3.5 | 560 | 0.7660 | 0.6789 | 0.7660 | 0.8752 |
| 0.5308 | 3.5125 | 562 | 0.7225 | 0.6789 | 0.7225 | 0.8500 |
| 0.5308 | 3.525 | 564 | 0.6548 | 0.6755 | 0.6548 | 0.8092 |
| 0.5308 | 3.5375 | 566 | 0.5437 | 0.6873 | 0.5437 | 0.7374 |
| 0.5308 | 3.55 | 568 | 0.4689 | 0.6873 | 0.4689 | 0.6847 |
| 0.5308 | 3.5625 | 570 | 0.4806 | 0.6209 | 0.4806 | 0.6933 |
| 0.5308 | 3.575 | 572 | 0.4792 | 0.6316 | 0.4792 | 0.6922 |
| 0.5308 | 3.5875 | 574 | 0.4593 | 0.6209 | 0.4593 | 0.6777 |
| 0.5308 | 3.6 | 576 | 0.4499 | 0.6873 | 0.4499 | 0.6707 |
| 0.5308 | 3.6125 | 578 | 0.4922 | 0.6873 | 0.4922 | 0.7016 |
| 0.5308 | 3.625 | 580 | 0.5392 | 0.6755 | 0.5392 | 0.7343 |
| 0.5308 | 3.6375 | 582 | 0.6560 | 0.6488 | 0.6560 | 0.8099 |
| 0.5308 | 3.65 | 584 | 0.6907 | 0.6488 | 0.6907 | 0.8311 |
| 0.5308 | 3.6625 | 586 | 0.6041 | 0.6755 | 0.6041 | 0.7772 |
| 0.5308 | 3.675 | 588 | 0.5866 | 0.6755 | 0.5866 | 0.7659 |
| 0.5308 | 3.6875 | 590 | 0.6126 | 0.6755 | 0.6126 | 0.7827 |
| 0.5308 | 3.7 | 592 | 0.5154 | 0.6755 | 0.5154 | 0.7179 |
| 0.5308 | 3.7125 | 594 | 0.4572 | 0.6755 | 0.4572 | 0.6762 |
| 0.5308 | 3.725 | 596 | 0.4711 | 0.6755 | 0.4711 | 0.6864 |
| 0.5308 | 3.7375 | 598 | 0.4759 | 0.6873 | 0.4759 | 0.6898 |
| 0.5308 | 3.75 | 600 | 0.4836 | 0.6597 | 0.4836 | 0.6954 |
| 0.5308 | 3.7625 | 602 | 0.4604 | 0.6597 | 0.4604 | 0.6786 |
| 0.5308 | 3.775 | 604 | 0.3882 | 0.75 | 0.3882 | 0.6231 |
| 0.5308 | 3.7875 | 606 | 0.3522 | 0.8489 | 0.3522 | 0.5934 |
| 0.5308 | 3.8 | 608 | 0.3644 | 0.7921 | 0.3644 | 0.6037 |
| 0.5308 | 3.8125 | 610 | 0.3601 | 0.8170 | 0.3601 | 0.6001 |
| 0.5308 | 3.825 | 612 | 0.3541 | 0.8339 | 0.3541 | 0.5950 |
| 0.5308 | 3.8375 | 614 | 0.3599 | 0.8170 | 0.3599 | 0.5999 |
| 0.5308 | 3.85 | 616 | 0.3935 | 0.72 | 0.3935 | 0.6273 |
| 0.5308 | 3.8625 | 618 | 0.4644 | 0.7273 | 0.4644 | 0.6815 |
| 0.5308 | 3.875 | 620 | 0.5125 | 0.6597 | 0.5125 | 0.7159 |
| 0.5308 | 3.8875 | 622 | 0.5012 | 0.6873 | 0.5012 | 0.7080 |
| 0.5308 | 3.9 | 624 | 0.4693 | 0.6873 | 0.4693 | 0.6851 |
| 0.5308 | 3.9125 | 626 | 0.4207 | 0.6873 | 0.4207 | 0.6486 |
| 0.5308 | 3.925 | 628 | 0.3758 | 0.776 | 0.3758 | 0.6131 |
| 0.5308 | 3.9375 | 630 | 0.3723 | 0.776 | 0.3723 | 0.6102 |
| 0.5308 | 3.95 | 632 | 0.3978 | 0.7287 | 0.3978 | 0.6307 |
| 0.5308 | 3.9625 | 634 | 0.4661 | 0.5825 | 0.4661 | 0.6827 |
| 0.5308 | 3.975 | 636 | 0.5295 | 0.5825 | 0.5295 | 0.7277 |
| 0.5308 | 3.9875 | 638 | 0.5466 | 0.6111 | 0.5466 | 0.7394 |
| 0.5308 | 4.0 | 640 | 0.5367 | 0.6111 | 0.5367 | 0.7326 |
| 0.5308 | 4.0125 | 642 | 0.5542 | 0.6873 | 0.5542 | 0.7444 |
| 0.5308 | 4.025 | 644 | 0.6156 | 0.6020 | 0.6156 | 0.7846 |
| 0.5308 | 4.0375 | 646 | 0.6389 | 0.5939 | 0.6389 | 0.7993 |
| 0.5308 | 4.05 | 648 | 0.6576 | 0.5939 | 0.6576 | 0.8110 |
| 0.5308 | 4.0625 | 650 | 0.6558 | 0.5939 | 0.6558 | 0.8098 |
| 0.5308 | 4.075 | 652 | 0.6177 | 0.5939 | 0.6177 | 0.7859 |
| 0.5308 | 4.0875 | 654 | 0.5387 | 0.6873 | 0.5387 | 0.7340 |
| 0.5308 | 4.1 | 656 | 0.4758 | 0.6873 | 0.4758 | 0.6898 |
| 0.5308 | 4.1125 | 658 | 0.4408 | 0.7273 | 0.4408 | 0.6639 |
| 0.5308 | 4.125 | 660 | 0.4367 | 0.7273 | 0.4367 | 0.6608 |
| 0.5308 | 4.1375 | 662 | 0.4954 | 0.6873 | 0.4954 | 0.7038 |
| 0.5308 | 4.15 | 664 | 0.5805 | 0.6488 | 0.5805 | 0.7619 |
| 0.5308 | 4.1625 | 666 | 0.5947 | 0.6488 | 0.5947 | 0.7712 |
| 0.5308 | 4.175 | 668 | 0.5641 | 0.7273 | 0.5641 | 0.7511 |
| 0.5308 | 4.1875 | 670 | 0.5385 | 0.7273 | 0.5385 | 0.7338 |
| 0.5308 | 4.2 | 672 | 0.4939 | 0.7273 | 0.4939 | 0.7027 |
| 0.5308 | 4.2125 | 674 | 0.4698 | 0.7524 | 0.4698 | 0.6854 |
| 0.5308 | 4.225 | 676 | 0.4980 | 0.6873 | 0.4980 | 0.7057 |
| 0.5308 | 4.2375 | 678 | 0.5783 | 0.6488 | 0.5783 | 0.7605 |
| 0.5308 | 4.25 | 680 | 0.6419 | 0.6580 | 0.6419 | 0.8012 |
| 0.5308 | 4.2625 | 682 | 0.6692 | 0.5855 | 0.6692 | 0.8180 |
| 0.5308 | 4.275 | 684 | 0.6195 | 0.6689 | 0.6195 | 0.7871 |
| 0.5308 | 4.2875 | 686 | 0.6036 | 0.6488 | 0.6036 | 0.7769 |
| 0.5308 | 4.3 | 688 | 0.6509 | 0.6488 | 0.6509 | 0.8068 |
| 0.5308 | 4.3125 | 690 | 0.6827 | 0.6387 | 0.6827 | 0.8263 |
| 0.5308 | 4.325 | 692 | 0.6305 | 0.6488 | 0.6305 | 0.7941 |
| 0.5308 | 4.3375 | 694 | 0.5629 | 0.6755 | 0.5629 | 0.7502 |
| 0.5308 | 4.35 | 696 | 0.5332 | 0.6873 | 0.5332 | 0.7302 |
| 0.5308 | 4.3625 | 698 | 0.5004 | 0.6873 | 0.5004 | 0.7074 |
| 0.5308 | 4.375 | 700 | 0.5343 | 0.6873 | 0.5343 | 0.7309 |
| 0.5308 | 4.3875 | 702 | 0.5675 | 0.6873 | 0.5675 | 0.7534 |
| 0.5308 | 4.4 | 704 | 0.5372 | 0.6873 | 0.5372 | 0.7329 |
| 0.5308 | 4.4125 | 706 | 0.5206 | 0.6873 | 0.5206 | 0.7215 |
| 0.5308 | 4.425 | 708 | 0.5088 | 0.7273 | 0.5088 | 0.7133 |
| 0.5308 | 4.4375 | 710 | 0.5479 | 0.7273 | 0.5479 | 0.7402 |
| 0.5308 | 4.45 | 712 | 0.5935 | 0.7273 | 0.5935 | 0.7704 |
| 0.5308 | 4.4625 | 714 | 0.6523 | 0.7147 | 0.6523 | 0.8076 |
| 0.5308 | 4.475 | 716 | 0.6561 | 0.7147 | 0.6561 | 0.8100 |
| 0.5308 | 4.4875 | 718 | 0.6120 | 0.6873 | 0.6120 | 0.7823 |
| 0.5308 | 4.5 | 720 | 0.5708 | 0.6873 | 0.5708 | 0.7555 |
| 0.5308 | 4.5125 | 722 | 0.5177 | 0.6873 | 0.5177 | 0.7195 |
| 0.5308 | 4.525 | 724 | 0.5032 | 0.7143 | 0.5032 | 0.7094 |
| 0.5308 | 4.5375 | 726 | 0.5074 | 0.7143 | 0.5074 | 0.7123 |
| 0.5308 | 4.55 | 728 | 0.5397 | 0.6873 | 0.5397 | 0.7347 |
| 0.5308 | 4.5625 | 730 | 0.5040 | 0.6873 | 0.5040 | 0.7099 |
| 0.5308 | 4.575 | 732 | 0.5324 | 0.6873 | 0.5324 | 0.7297 |
| 0.5308 | 4.5875 | 734 | 0.6491 | 0.6789 | 0.6491 | 0.8056 |
| 0.5308 | 4.6 | 736 | 0.7299 | 0.6448 | 0.7299 | 0.8544 |
| 0.5308 | 4.6125 | 738 | 0.8101 | 0.65 | 0.8101 | 0.9000 |
| 0.5308 | 4.625 | 740 | 0.7943 | 0.65 | 0.7943 | 0.8912 |
| 0.5308 | 4.6375 | 742 | 0.8293 | 0.65 | 0.8293 | 0.9107 |
| 0.5308 | 4.65 | 744 | 0.8462 | 0.6448 | 0.8462 | 0.9199 |
| 0.5308 | 4.6625 | 746 | 0.8005 | 0.6387 | 0.8005 | 0.8947 |
| 0.5308 | 4.675 | 748 | 0.7040 | 0.6387 | 0.7040 | 0.8390 |
| 0.5308 | 4.6875 | 750 | 0.5843 | 0.6755 | 0.5843 | 0.7644 |
| 0.5308 | 4.7 | 752 | 0.5578 | 0.6755 | 0.5578 | 0.7468 |
| 0.5308 | 4.7125 | 754 | 0.5837 | 0.6488 | 0.5837 | 0.7640 |
| 0.5308 | 4.725 | 756 | 0.5944 | 0.6488 | 0.5944 | 0.7710 |
| 0.5308 | 4.7375 | 758 | 0.5991 | 0.6755 | 0.5991 | 0.7740 |
| 0.5308 | 4.75 | 760 | 0.6303 | 0.6488 | 0.6303 | 0.7939 |
| 0.5308 | 4.7625 | 762 | 0.5843 | 0.6755 | 0.5843 | 0.7644 |
| 0.5308 | 4.775 | 764 | 0.5115 | 0.6755 | 0.5115 | 0.7152 |
| 0.5308 | 4.7875 | 766 | 0.4917 | 0.6755 | 0.4917 | 0.7012 |
| 0.5308 | 4.8 | 768 | 0.4825 | 0.6557 | 0.4825 | 0.6946 |
| 0.5308 | 4.8125 | 770 | 0.4699 | 0.7063 | 0.4699 | 0.6855 |
| 0.5308 | 4.825 | 772 | 0.5097 | 0.7074 | 0.5097 | 0.7139 |
| 0.5308 | 4.8375 | 774 | 0.5528 | 0.6557 | 0.5528 | 0.7435 |
| 0.5308 | 4.85 | 776 | 0.6304 | 0.6645 | 0.6304 | 0.7940 |
| 0.5308 | 4.8625 | 778 | 0.6446 | 0.6645 | 0.6446 | 0.8029 |
| 0.5308 | 4.875 | 780 | 0.6450 | 0.6645 | 0.6450 | 0.8031 |
| 0.5308 | 4.8875 | 782 | 0.6016 | 0.6645 | 0.6016 | 0.7756 |
| 0.5308 | 4.9 | 784 | 0.5531 | 0.6755 | 0.5531 | 0.7437 |
| 0.5308 | 4.9125 | 786 | 0.4730 | 0.6873 | 0.4730 | 0.6878 |
| 0.5308 | 4.925 | 788 | 0.4429 | 0.7 | 0.4429 | 0.6655 |
| 0.5308 | 4.9375 | 790 | 0.4686 | 0.6873 | 0.4686 | 0.6845 |
| 0.5308 | 4.95 | 792 | 0.5091 | 0.6755 | 0.5091 | 0.7135 |
| 0.5308 | 4.9625 | 794 | 0.5663 | 0.6488 | 0.5663 | 0.7525 |
| 0.5308 | 4.975 | 796 | 0.5696 | 0.6488 | 0.5696 | 0.7547 |
| 0.5308 | 4.9875 | 798 | 0.5318 | 0.6488 | 0.5318 | 0.7293 |
| 0.5308 | 5.0 | 800 | 0.5170 | 0.6755 | 0.5170 | 0.7191 |
| 0.5308 | 5.0125 | 802 | 0.4919 | 0.6708 | 0.4919 | 0.7014 |
| 0.5308 | 5.025 | 804 | 0.4834 | 0.6708 | 0.4834 | 0.6953 |
| 0.5308 | 5.0375 | 806 | 0.4865 | 0.6708 | 0.4865 | 0.6975 |
| 0.5308 | 5.05 | 808 | 0.4646 | 0.6818 | 0.4646 | 0.6816 |
| 0.5308 | 5.0625 | 810 | 0.4296 | 0.6818 | 0.4296 | 0.6555 |
| 0.5308 | 5.075 | 812 | 0.4066 | 0.7552 | 0.4066 | 0.6377 |
| 0.5308 | 5.0875 | 814 | 0.3901 | 0.72 | 0.3901 | 0.6246 |
| 0.5308 | 5.1 | 816 | 0.3829 | 0.7640 | 0.3829 | 0.6188 |
| 0.5308 | 5.1125 | 818 | 0.3867 | 0.7640 | 0.3867 | 0.6218 |
| 0.5308 | 5.125 | 820 | 0.4054 | 0.72 | 0.4054 | 0.6367 |
| 0.5308 | 5.1375 | 822 | 0.4528 | 0.7407 | 0.4528 | 0.6729 |
| 0.5308 | 5.15 | 824 | 0.5244 | 0.6708 | 0.5244 | 0.7242 |
| 0.5308 | 5.1625 | 826 | 0.5653 | 0.6488 | 0.5653 | 0.7519 |
| 0.5308 | 5.175 | 828 | 0.5475 | 0.6488 | 0.5475 | 0.7399 |
| 0.5308 | 5.1875 | 830 | 0.4870 | 0.6873 | 0.4870 | 0.6978 |
| 0.5308 | 5.2 | 832 | 0.4826 | 0.6873 | 0.4826 | 0.6947 |
| 0.5308 | 5.2125 | 834 | 0.5025 | 0.6873 | 0.5025 | 0.7089 |
| 0.5308 | 5.225 | 836 | 0.5508 | 0.6488 | 0.5508 | 0.7422 |
| 0.5308 | 5.2375 | 838 | 0.6206 | 0.6488 | 0.6206 | 0.7878 |
| 0.5308 | 5.25 | 840 | 0.7318 | 0.6580 | 0.7318 | 0.8554 |
| 0.5308 | 5.2625 | 842 | 0.7585 | 0.6580 | 0.7585 | 0.8709 |
| 0.5308 | 5.275 | 844 | 0.7122 | 0.6387 | 0.7122 | 0.8439 |
| 0.5308 | 5.2875 | 846 | 0.6426 | 0.6488 | 0.6426 | 0.8016 |
| 0.5308 | 5.3 | 848 | 0.6234 | 0.6755 | 0.6234 | 0.7896 |
| 0.5308 | 5.3125 | 850 | 0.6180 | 0.6755 | 0.6180 | 0.7861 |
| 0.5308 | 5.325 | 852 | 0.6567 | 0.6755 | 0.6567 | 0.8104 |
| 0.5308 | 5.3375 | 854 | 0.7279 | 0.6387 | 0.7279 | 0.8532 |
| 0.5308 | 5.35 | 856 | 0.8033 | 0.6205 | 0.8033 | 0.8963 |
| 0.5308 | 5.3625 | 858 | 0.8380 | 0.6205 | 0.8380 | 0.9154 |
| 0.5308 | 5.375 | 860 | 0.7843 | 0.6580 | 0.7843 | 0.8856 |
| 0.5308 | 5.3875 | 862 | 0.7019 | 0.6580 | 0.7019 | 0.8378 |
| 0.5308 | 5.4 | 864 | 0.6245 | 0.6488 | 0.6245 | 0.7903 |
| 0.5308 | 5.4125 | 866 | 0.6077 | 0.6755 | 0.6077 | 0.7796 |
| 0.5308 | 5.425 | 868 | 0.6400 | 0.6755 | 0.6400 | 0.8000 |
| 0.5308 | 5.4375 | 870 | 0.6712 | 0.6755 | 0.6712 | 0.8193 |
| 0.5308 | 5.45 | 872 | 0.7083 | 0.6590 | 0.7083 | 0.8416 |
| 0.5308 | 5.4625 | 874 | 0.6928 | 0.6590 | 0.6928 | 0.8324 |
| 0.5308 | 5.475 | 876 | 0.7268 | 0.6792 | 0.7268 | 0.8525 |
| 0.5308 | 5.4875 | 878 | 0.8059 | 0.6732 | 0.8059 | 0.8977 |
| 0.5308 | 5.5 | 880 | 0.7543 | 0.6702 | 0.7543 | 0.8685 |
| 0.5308 | 5.5125 | 882 | 0.6384 | 0.6763 | 0.6384 | 0.7990 |
| 0.5308 | 5.525 | 884 | 0.5792 | 0.6729 | 0.5792 | 0.7610 |
| 0.5308 | 5.5375 | 886 | 0.5805 | 0.6729 | 0.5805 | 0.7619 |
| 0.5308 | 5.55 | 888 | 0.6172 | 0.6729 | 0.6172 | 0.7856 |
| 0.5308 | 5.5625 | 890 | 0.6378 | 0.6763 | 0.6378 | 0.7986 |
| 0.5308 | 5.575 | 892 | 0.6099 | 0.6729 | 0.6099 | 0.7810 |
| 0.5308 | 5.5875 | 894 | 0.5606 | 0.6543 | 0.5606 | 0.7487 |
| 0.5308 | 5.6 | 896 | 0.4844 | 0.6873 | 0.4844 | 0.6960 |
| 0.5308 | 5.6125 | 898 | 0.4363 | 0.6873 | 0.4363 | 0.6606 |
| 0.5308 | 5.625 | 900 | 0.4065 | 0.7712 | 0.4065 | 0.6376 |
| 0.5308 | 5.6375 | 902 | 0.4321 | 0.8013 | 0.4321 | 0.6573 |
| 0.5308 | 5.65 | 904 | 0.4257 | 0.8013 | 0.4257 | 0.6524 |
| 0.5308 | 5.6625 | 906 | 0.4282 | 0.8013 | 0.4282 | 0.6544 |
| 0.5308 | 5.675 | 908 | 0.4775 | 0.7091 | 0.4775 | 0.6910 |
| 0.5308 | 5.6875 | 910 | 0.5132 | 0.7091 | 0.5132 | 0.7164 |
| 0.5308 | 5.7 | 912 | 0.5378 | 0.7091 | 0.5378 | 0.7333 |
| 0.5308 | 5.7125 | 914 | 0.5952 | 0.7098 | 0.5952 | 0.7715 |
| 0.5308 | 5.725 | 916 | 0.6034 | 0.6775 | 0.6034 | 0.7768 |
| 0.5308 | 5.7375 | 918 | 0.5654 | 0.6789 | 0.5654 | 0.7519 |
| 0.5308 | 5.75 | 920 | 0.5652 | 0.6789 | 0.5652 | 0.7518 |
| 0.5308 | 5.7625 | 922 | 0.5794 | 0.6789 | 0.5794 | 0.7612 |
| 0.5308 | 5.775 | 924 | 0.5720 | 0.6789 | 0.5720 | 0.7563 |
| 0.5308 | 5.7875 | 926 | 0.5947 | 0.6789 | 0.5947 | 0.7712 |
| 0.5308 | 5.8 | 928 | 0.6137 | 0.6789 | 0.6137 | 0.7834 |
| 0.5308 | 5.8125 | 930 | 0.6565 | 0.6753 | 0.6565 | 0.8102 |
| 0.5308 | 5.825 | 932 | 0.6672 | 0.6753 | 0.6672 | 0.8169 |
| 0.5308 | 5.8375 | 934 | 0.6539 | 0.6753 | 0.6539 | 0.8086 |
| 0.5308 | 5.85 | 936 | 0.6275 | 0.6789 | 0.6275 | 0.7922 |
| 0.5308 | 5.8625 | 938 | 0.5868 | 0.6789 | 0.5868 | 0.7660 |
| 0.5308 | 5.875 | 940 | 0.6069 | 0.6789 | 0.6069 | 0.7790 |
| 0.5308 | 5.8875 | 942 | 0.6622 | 0.6722 | 0.6622 | 0.8137 |
| 0.5308 | 5.9 | 944 | 0.6684 | 0.6722 | 0.6684 | 0.8176 |
| 0.5308 | 5.9125 | 946 | 0.6260 | 0.6789 | 0.6260 | 0.7912 |
| 0.5308 | 5.925 | 948 | 0.6281 | 0.6789 | 0.6281 | 0.7925 |
| 0.5308 | 5.9375 | 950 | 0.7032 | 0.6722 | 0.7032 | 0.8386 |
| 0.5308 | 5.95 | 952 | 0.8008 | 0.6753 | 0.8008 | 0.8949 |
| 0.5308 | 5.9625 | 954 | 0.8423 | 0.6753 | 0.8423 | 0.9178 |
| 0.5308 | 5.975 | 956 | 0.8133 | 0.6753 | 0.8133 | 0.9019 |
| 0.5308 | 5.9875 | 958 | 0.7375 | 0.6753 | 0.7375 | 0.8588 |
| 0.5308 | 6.0 | 960 | 0.7384 | 0.6753 | 0.7384 | 0.8593 |
| 0.5308 | 6.0125 | 962 | 0.8346 | 0.6753 | 0.8346 | 0.9135 |
| 0.5308 | 6.025 | 964 | 0.8724 | 0.6780 | 0.8724 | 0.9340 |
| 0.5308 | 6.0375 | 966 | 0.8399 | 0.6753 | 0.8399 | 0.9165 |
| 0.5308 | 6.05 | 968 | 0.7648 | 0.6753 | 0.7648 | 0.8745 |
| 0.5308 | 6.0625 | 970 | 0.6693 | 0.6722 | 0.6693 | 0.8181 |
| 0.5308 | 6.075 | 972 | 0.6145 | 0.6686 | 0.6145 | 0.7839 |
| 0.5308 | 6.0875 | 974 | 0.6011 | 0.6686 | 0.6011 | 0.7753 |
| 0.5308 | 6.1 | 976 | 0.5964 | 0.6789 | 0.5964 | 0.7723 |
| 0.5308 | 6.1125 | 978 | 0.6222 | 0.6686 | 0.6222 | 0.7888 |
| 0.5308 | 6.125 | 980 | 0.6706 | 0.6686 | 0.6706 | 0.8189 |
| 0.5308 | 6.1375 | 982 | 0.6940 | 0.6686 | 0.6940 | 0.8331 |
| 0.5308 | 6.15 | 984 | 0.7504 | 0.6753 | 0.7504 | 0.8662 |
| 0.5308 | 6.1625 | 986 | 0.7536 | 0.6753 | 0.7536 | 0.8681 |
| 0.5308 | 6.175 | 988 | 0.7408 | 0.6753 | 0.7408 | 0.8607 |
| 0.5308 | 6.1875 | 990 | 0.7196 | 0.6753 | 0.7196 | 0.8483 |
| 0.5308 | 6.2 | 992 | 0.6950 | 0.6686 | 0.6950 | 0.8336 |
| 0.5308 | 6.2125 | 994 | 0.7020 | 0.6686 | 0.7020 | 0.8378 |
| 0.5308 | 6.225 | 996 | 0.6638 | 0.6645 | 0.6638 | 0.8148 |
| 0.5308 | 6.2375 | 998 | 0.6540 | 0.6387 | 0.6540 | 0.8087 |
| 0.1178 | 6.25 | 1000 | 0.5944 | 0.6755 | 0.5944 | 0.7709 |
| 0.1178 | 6.2625 | 1002 | 0.5316 | 0.6755 | 0.5316 | 0.7291 |
| 0.1178 | 6.275 | 1004 | 0.5159 | 0.6755 | 0.5159 | 0.7183 |
| 0.1178 | 6.2875 | 1006 | 0.5217 | 0.6755 | 0.5217 | 0.7223 |
| 0.1178 | 6.3 | 1008 | 0.5038 | 0.6755 | 0.5038 | 0.7098 |
| 0.1178 | 6.3125 | 1010 | 0.4884 | 0.6755 | 0.4884 | 0.6989 |
| 0.1178 | 6.325 | 1012 | 0.5013 | 0.6755 | 0.5013 | 0.7080 |
| 0.1178 | 6.3375 | 1014 | 0.5237 | 0.6755 | 0.5237 | 0.7237 |
| 0.1178 | 6.35 | 1016 | 0.5808 | 0.6755 | 0.5808 | 0.7621 |
| 0.1178 | 6.3625 | 1018 | 0.6495 | 0.6755 | 0.6495 | 0.8059 |
| 0.1178 | 6.375 | 1020 | 0.6978 | 0.6645 | 0.6978 | 0.8354 |
| 0.1178 | 6.3875 | 1022 | 0.6747 | 0.6645 | 0.6747 | 0.8214 |
| 0.1178 | 6.4 | 1024 | 0.6152 | 0.6755 | 0.6152 | 0.7843 |
| 0.1178 | 6.4125 | 1026 | 0.5723 | 0.6755 | 0.5723 | 0.7565 |
| 0.1178 | 6.425 | 1028 | 0.5759 | 0.6755 | 0.5759 | 0.7589 |
| 0.1178 | 6.4375 | 1030 | 0.5744 | 0.6755 | 0.5744 | 0.7579 |
| 0.1178 | 6.45 | 1032 | 0.5429 | 0.6755 | 0.5429 | 0.7368 |
| 0.1178 | 6.4625 | 1034 | 0.5615 | 0.6755 | 0.5615 | 0.7494 |
| 0.1178 | 6.475 | 1036 | 0.5487 | 0.6755 | 0.5487 | 0.7407 |
| 0.1178 | 6.4875 | 1038 | 0.5589 | 0.6755 | 0.5589 | 0.7476 |
| 0.1178 | 6.5 | 1040 | 0.5855 | 0.6755 | 0.5855 | 0.7652 |
| 0.1178 | 6.5125 | 1042 | 0.6205 | 0.6237 | 0.6205 | 0.7877 |
| 0.1178 | 6.525 | 1044 | 0.6484 | 0.6138 | 0.6484 | 0.8052 |
| 0.1178 | 6.5375 | 1046 | 0.6564 | 0.6138 | 0.6564 | 0.8102 |
| 0.1178 | 6.55 | 1048 | 0.5971 | 0.6689 | 0.5971 | 0.7727 |
| 0.1178 | 6.5625 | 1050 | 0.5180 | 0.6873 | 0.5180 | 0.7197 |
| 0.1178 | 6.575 | 1052 | 0.4488 | 0.6873 | 0.4488 | 0.6699 |
| 0.1178 | 6.5875 | 1054 | 0.4253 | 0.7 | 0.4253 | 0.6521 |
| 0.1178 | 6.6 | 1056 | 0.4336 | 0.6873 | 0.4336 | 0.6585 |
| 0.1178 | 6.6125 | 1058 | 0.4705 | 0.6873 | 0.4705 | 0.6859 |
| 0.1178 | 6.625 | 1060 | 0.5509 | 0.6755 | 0.5509 | 0.7423 |
| 0.1178 | 6.6375 | 1062 | 0.6430 | 0.6839 | 0.6430 | 0.8019 |
| 0.1178 | 6.65 | 1064 | 0.6945 | 0.6627 | 0.6945 | 0.8334 |
| 0.1178 | 6.6625 | 1066 | 0.6938 | 0.6627 | 0.6938 | 0.8330 |
| 0.1178 | 6.675 | 1068 | 0.6396 | 0.6645 | 0.6396 | 0.7998 |
| 0.1178 | 6.6875 | 1070 | 0.5451 | 0.6873 | 0.5451 | 0.7383 |
| 0.1178 | 6.7 | 1072 | 0.4862 | 0.6873 | 0.4862 | 0.6973 |
| 0.1178 | 6.7125 | 1074 | 0.4595 | 0.6873 | 0.4595 | 0.6778 |
| 0.1178 | 6.725 | 1076 | 0.4665 | 0.6873 | 0.4665 | 0.6830 |
| 0.1178 | 6.7375 | 1078 | 0.4910 | 0.6873 | 0.4910 | 0.7007 |
| 0.1178 | 6.75 | 1080 | 0.5510 | 0.6873 | 0.5510 | 0.7423 |
| 0.1178 | 6.7625 | 1082 | 0.5895 | 0.6488 | 0.5895 | 0.7678 |
| 0.1178 | 6.775 | 1084 | 0.5792 | 0.6488 | 0.5792 | 0.7611 |
| 0.1178 | 6.7875 | 1086 | 0.5330 | 0.6873 | 0.5330 | 0.7301 |
| 0.1178 | 6.8 | 1088 | 0.5183 | 0.6873 | 0.5183 | 0.7199 |
| 0.1178 | 6.8125 | 1090 | 0.4991 | 0.6873 | 0.4991 | 0.7065 |
| 0.1178 | 6.825 | 1092 | 0.4670 | 0.6873 | 0.4670 | 0.6834 |
| 0.1178 | 6.8375 | 1094 | 0.4327 | 0.7 | 0.4327 | 0.6578 |
| 0.1178 | 6.85 | 1096 | 0.4393 | 0.7 | 0.4393 | 0.6628 |
| 0.1178 | 6.8625 | 1098 | 0.4766 | 0.6873 | 0.4766 | 0.6904 |
| 0.1178 | 6.875 | 1100 | 0.5546 | 0.6755 | 0.5546 | 0.7447 |
| 0.1178 | 6.8875 | 1102 | 0.5914 | 0.6755 | 0.5914 | 0.7690 |
| 0.1178 | 6.9 | 1104 | 0.6136 | 0.6755 | 0.6136 | 0.7833 |
| 0.1178 | 6.9125 | 1106 | 0.6356 | 0.6789 | 0.6356 | 0.7973 |
| 0.1178 | 6.925 | 1108 | 0.6303 | 0.6789 | 0.6303 | 0.7939 |
| 0.1178 | 6.9375 | 1110 | 0.6054 | 0.6755 | 0.6054 | 0.7781 |
| 0.1178 | 6.95 | 1112 | 0.5494 | 0.6755 | 0.5494 | 0.7412 |
| 0.1178 | 6.9625 | 1114 | 0.5121 | 0.6755 | 0.5121 | 0.7156 |
| 0.1178 | 6.975 | 1116 | 0.5027 | 0.6755 | 0.5027 | 0.7090 |
| 0.1178 | 6.9875 | 1118 | 0.5425 | 0.6755 | 0.5425 | 0.7365 |
| 0.1178 | 7.0 | 1120 | 0.6175 | 0.6755 | 0.6175 | 0.7858 |
| 0.1178 | 7.0125 | 1122 | 0.6693 | 0.6755 | 0.6693 | 0.8181 |
| 0.1178 | 7.025 | 1124 | 0.7134 | 0.6488 | 0.7134 | 0.8446 |
| 0.1178 | 7.0375 | 1126 | 0.6882 | 0.6488 | 0.6882 | 0.8296 |
| 0.1178 | 7.05 | 1128 | 0.6314 | 0.6755 | 0.6314 | 0.7946 |
| 0.1178 | 7.0625 | 1130 | 0.5649 | 0.6755 | 0.5649 | 0.7516 |
| 0.1178 | 7.075 | 1132 | 0.5082 | 0.6755 | 0.5082 | 0.7129 |
| 0.1178 | 7.0875 | 1134 | 0.5018 | 0.6755 | 0.5018 | 0.7084 |
| 0.1178 | 7.1 | 1136 | 0.5396 | 0.6755 | 0.5396 | 0.7346 |
| 0.1178 | 7.1125 | 1138 | 0.5761 | 0.6755 | 0.5761 | 0.7590 |
| 0.1178 | 7.125 | 1140 | 0.6364 | 0.6755 | 0.6364 | 0.7978 |
| 0.1178 | 7.1375 | 1142 | 0.7263 | 0.65 | 0.7263 | 0.8523 |
| 0.1178 | 7.15 | 1144 | 0.8399 | 0.6388 | 0.8399 | 0.9165 |
| 0.1178 | 7.1625 | 1146 | 0.9211 | 0.6435 | 0.9211 | 0.9597 |
| 0.1178 | 7.175 | 1148 | 0.9093 | 0.6388 | 0.9093 | 0.9536 |
| 0.1178 | 7.1875 | 1150 | 0.8235 | 0.6627 | 0.8235 | 0.9075 |
| 0.1178 | 7.2 | 1152 | 0.6852 | 0.6387 | 0.6852 | 0.8277 |
| 0.1178 | 7.2125 | 1154 | 0.5784 | 0.6755 | 0.5784 | 0.7605 |
| 0.1178 | 7.225 | 1156 | 0.5000 | 0.6873 | 0.5000 | 0.7071 |
| 0.1178 | 7.2375 | 1158 | 0.4674 | 0.6873 | 0.4674 | 0.6837 |
| 0.1178 | 7.25 | 1160 | 0.4786 | 0.6873 | 0.4786 | 0.6918 |
| 0.1178 | 7.2625 | 1162 | 0.5322 | 0.6755 | 0.5322 | 0.7295 |
| 0.1178 | 7.275 | 1164 | 0.6075 | 0.6488 | 0.6075 | 0.7794 |
| 0.1178 | 7.2875 | 1166 | 0.6672 | 0.6387 | 0.6672 | 0.8168 |
| 0.1178 | 7.3 | 1168 | 0.6638 | 0.6387 | 0.6638 | 0.8148 |
| 0.1178 | 7.3125 | 1170 | 0.6258 | 0.6755 | 0.6258 | 0.7911 |
| 0.1178 | 7.325 | 1172 | 0.5942 | 0.6755 | 0.5942 | 0.7709 |
| 0.1178 | 7.3375 | 1174 | 0.5925 | 0.6755 | 0.5925 | 0.7697 |
| 0.1178 | 7.35 | 1176 | 0.6347 | 0.6755 | 0.6347 | 0.7967 |
| 0.1178 | 7.3625 | 1178 | 0.6913 | 0.6387 | 0.6913 | 0.8315 |
| 0.1178 | 7.375 | 1180 | 0.7327 | 0.6580 | 0.7327 | 0.8560 |
| 0.1178 | 7.3875 | 1182 | 0.7214 | 0.6580 | 0.7214 | 0.8493 |
| 0.1178 | 7.4 | 1184 | 0.6976 | 0.6580 | 0.6976 | 0.8352 |
| 0.1178 | 7.4125 | 1186 | 0.6346 | 0.6488 | 0.6346 | 0.7966 |
| 0.1178 | 7.425 | 1188 | 0.5872 | 0.6488 | 0.5872 | 0.7663 |
| 0.1178 | 7.4375 | 1190 | 0.5622 | 0.6488 | 0.5622 | 0.7498 |
| 0.1178 | 7.45 | 1192 | 0.5632 | 0.6488 | 0.5632 | 0.7505 |
| 0.1178 | 7.4625 | 1194 | 0.5429 | 0.6597 | 0.5429 | 0.7368 |
| 0.1178 | 7.475 | 1196 | 0.5266 | 0.6597 | 0.5266 | 0.7257 |
| 0.1178 | 7.4875 | 1198 | 0.5224 | 0.6597 | 0.5224 | 0.7227 |
| 0.1178 | 7.5 | 1200 | 0.5209 | 0.6597 | 0.5209 | 0.7217 |
| 0.1178 | 7.5125 | 1202 | 0.5240 | 0.6597 | 0.5240 | 0.7238 |
| 0.1178 | 7.525 | 1204 | 0.5021 | 0.6873 | 0.5021 | 0.7086 |
| 0.1178 | 7.5375 | 1206 | 0.4935 | 0.6873 | 0.4935 | 0.7025 |
| 0.1178 | 7.55 | 1208 | 0.4814 | 0.6873 | 0.4814 | 0.6939 |
| 0.1178 | 7.5625 | 1210 | 0.4777 | 0.6873 | 0.4777 | 0.6912 |
| 0.1178 | 7.575 | 1212 | 0.4930 | 0.6873 | 0.4930 | 0.7021 |
| 0.1178 | 7.5875 | 1214 | 0.5201 | 0.6873 | 0.5201 | 0.7212 |
| 0.1178 | 7.6 | 1216 | 0.5205 | 0.6873 | 0.5205 | 0.7215 |
| 0.1178 | 7.6125 | 1218 | 0.5096 | 0.6873 | 0.5096 | 0.7138 |
| 0.1178 | 7.625 | 1220 | 0.5200 | 0.6873 | 0.5200 | 0.7211 |
| 0.1178 | 7.6375 | 1222 | 0.5336 | 0.6873 | 0.5336 | 0.7305 |
| 0.1178 | 7.65 | 1224 | 0.5551 | 0.6873 | 0.5551 | 0.7451 |
| 0.1178 | 7.6625 | 1226 | 0.5924 | 0.6755 | 0.5924 | 0.7696 |
| 0.1178 | 7.675 | 1228 | 0.6337 | 0.6755 | 0.6337 | 0.7960 |
| 0.1178 | 7.6875 | 1230 | 0.6954 | 0.6448 | 0.6954 | 0.8339 |
| 0.1178 | 7.7 | 1232 | 0.7195 | 0.6448 | 0.7195 | 0.8482 |
| 0.1178 | 7.7125 | 1234 | 0.7296 | 0.6448 | 0.7296 | 0.8542 |
| 0.1178 | 7.725 | 1236 | 0.6909 | 0.6448 | 0.6909 | 0.8312 |
| 0.1178 | 7.7375 | 1238 | 0.6394 | 0.6755 | 0.6394 | 0.7997 |
| 0.1178 | 7.75 | 1240 | 0.6331 | 0.6755 | 0.6331 | 0.7957 |
| 0.1178 | 7.7625 | 1242 | 0.6331 | 0.6755 | 0.6331 | 0.7957 |
| 0.1178 | 7.775 | 1244 | 0.6086 | 0.6755 | 0.6086 | 0.7801 |
| 0.1178 | 7.7875 | 1246 | 0.5911 | 0.6755 | 0.5911 | 0.7688 |
| 0.1178 | 7.8 | 1248 | 0.5909 | 0.6755 | 0.5909 | 0.7687 |
| 0.1178 | 7.8125 | 1250 | 0.6225 | 0.6755 | 0.6225 | 0.7890 |
| 0.1178 | 7.825 | 1252 | 0.6441 | 0.6789 | 0.6441 | 0.8026 |
| 0.1178 | 7.8375 | 1254 | 0.6671 | 0.6543 | 0.6671 | 0.8168 |
| 0.1178 | 7.85 | 1256 | 0.6762 | 0.6543 | 0.6762 | 0.8223 |
| 0.1178 | 7.8625 | 1258 | 0.6777 | 0.6543 | 0.6777 | 0.8232 |
| 0.1178 | 7.875 | 1260 | 0.6447 | 0.6543 | 0.6447 | 0.8029 |
| 0.1178 | 7.8875 | 1262 | 0.6006 | 0.6488 | 0.6006 | 0.7750 |
| 0.1178 | 7.9 | 1264 | 0.5501 | 0.6873 | 0.5501 | 0.7417 |
| 0.1178 | 7.9125 | 1266 | 0.5195 | 0.6873 | 0.5195 | 0.7208 |
| 0.1178 | 7.925 | 1268 | 0.4965 | 0.6873 | 0.4965 | 0.7046 |
| 0.1178 | 7.9375 | 1270 | 0.4940 | 0.6873 | 0.4940 | 0.7028 |
| 0.1178 | 7.95 | 1272 | 0.5006 | 0.6873 | 0.5006 | 0.7075 |
| 0.1178 | 7.9625 | 1274 | 0.5099 | 0.6873 | 0.5099 | 0.7141 |
| 0.1178 | 7.975 | 1276 | 0.5144 | 0.6873 | 0.5144 | 0.7173 |
| 0.1178 | 7.9875 | 1278 | 0.5153 | 0.6873 | 0.5153 | 0.7179 |
| 0.1178 | 8.0 | 1280 | 0.5336 | 0.6873 | 0.5336 | 0.7305 |
| 0.1178 | 8.0125 | 1282 | 0.5535 | 0.6873 | 0.5535 | 0.7440 |
| 0.1178 | 8.025 | 1284 | 0.5661 | 0.6597 | 0.5661 | 0.7524 |
| 0.1178 | 8.0375 | 1286 | 0.5673 | 0.6597 | 0.5673 | 0.7532 |
| 0.1178 | 8.05 | 1288 | 0.5607 | 0.6597 | 0.5607 | 0.7488 |
| 0.1178 | 8.0625 | 1290 | 0.5424 | 0.6873 | 0.5424 | 0.7364 |
| 0.1178 | 8.075 | 1292 | 0.5177 | 0.6873 | 0.5177 | 0.7195 |
| 0.1178 | 8.0875 | 1294 | 0.5064 | 0.6873 | 0.5064 | 0.7116 |
| 0.1178 | 8.1 | 1296 | 0.5104 | 0.6873 | 0.5104 | 0.7144 |
| 0.1178 | 8.1125 | 1298 | 0.5190 | 0.6873 | 0.5190 | 0.7204 |
| 0.1178 | 8.125 | 1300 | 0.5241 | 0.6873 | 0.5241 | 0.7240 |
| 0.1178 | 8.1375 | 1302 | 0.5242 | 0.6873 | 0.5242 | 0.7240 |
| 0.1178 | 8.15 | 1304 | 0.5419 | 0.6873 | 0.5419 | 0.7362 |
| 0.1178 | 8.1625 | 1306 | 0.5669 | 0.6873 | 0.5669 | 0.7529 |
| 0.1178 | 8.175 | 1308 | 0.5692 | 0.6873 | 0.5692 | 0.7545 |
| 0.1178 | 8.1875 | 1310 | 0.5425 | 0.6873 | 0.5425 | 0.7366 |
| 0.1178 | 8.2 | 1312 | 0.5310 | 0.6873 | 0.5310 | 0.7287 |
| 0.1178 | 8.2125 | 1314 | 0.5419 | 0.6873 | 0.5419 | 0.7361 |
| 0.1178 | 8.225 | 1316 | 0.5656 | 0.6873 | 0.5656 | 0.7521 |
| 0.1178 | 8.2375 | 1318 | 0.5910 | 0.6873 | 0.5910 | 0.7688 |
| 0.1178 | 8.25 | 1320 | 0.6013 | 0.6873 | 0.6013 | 0.7754 |
| 0.1178 | 8.2625 | 1322 | 0.6103 | 0.6597 | 0.6103 | 0.7812 |
| 0.1178 | 8.275 | 1324 | 0.6164 | 0.6597 | 0.6164 | 0.7851 |
| 0.1178 | 8.2875 | 1326 | 0.5914 | 0.6597 | 0.5914 | 0.7690 |
| 0.1178 | 8.3 | 1328 | 0.5473 | 0.6597 | 0.5473 | 0.7398 |
| 0.1178 | 8.3125 | 1330 | 0.5060 | 0.6873 | 0.5060 | 0.7114 |
| 0.1178 | 8.325 | 1332 | 0.4643 | 0.6873 | 0.4643 | 0.6814 |
| 0.1178 | 8.3375 | 1334 | 0.4388 | 0.75 | 0.4388 | 0.6624 |
| 0.1178 | 8.35 | 1336 | 0.4240 | 0.75 | 0.4240 | 0.6512 |
| 0.1178 | 8.3625 | 1338 | 0.4275 | 0.75 | 0.4275 | 0.6539 |
| 0.1178 | 8.375 | 1340 | 0.4345 | 0.75 | 0.4345 | 0.6592 |
| 0.1178 | 8.3875 | 1342 | 0.4479 | 0.75 | 0.4479 | 0.6693 |
| 0.1178 | 8.4 | 1344 | 0.4791 | 0.6873 | 0.4791 | 0.6922 |
| 0.1178 | 8.4125 | 1346 | 0.5092 | 0.6873 | 0.5092 | 0.7136 |
| 0.1178 | 8.425 | 1348 | 0.5338 | 0.6873 | 0.5338 | 0.7306 |
| 0.1178 | 8.4375 | 1350 | 0.5464 | 0.6873 | 0.5464 | 0.7392 |
| 0.1178 | 8.45 | 1352 | 0.5565 | 0.6873 | 0.5565 | 0.7460 |
| 0.1178 | 8.4625 | 1354 | 0.5735 | 0.6873 | 0.5735 | 0.7573 |
| 0.1178 | 8.475 | 1356 | 0.5799 | 0.6873 | 0.5799 | 0.7615 |
| 0.1178 | 8.4875 | 1358 | 0.5666 | 0.6873 | 0.5666 | 0.7528 |
| 0.1178 | 8.5 | 1360 | 0.5559 | 0.6873 | 0.5559 | 0.7456 |
| 0.1178 | 8.5125 | 1362 | 0.5439 | 0.6873 | 0.5439 | 0.7375 |
| 0.1178 | 8.525 | 1364 | 0.5194 | 0.6873 | 0.5194 | 0.7207 |
| 0.1178 | 8.5375 | 1366 | 0.5040 | 0.6873 | 0.5040 | 0.7099 |
| 0.1178 | 8.55 | 1368 | 0.5164 | 0.6873 | 0.5164 | 0.7186 |
| 0.1178 | 8.5625 | 1370 | 0.5249 | 0.6873 | 0.5249 | 0.7245 |
| 0.1178 | 8.575 | 1372 | 0.5303 | 0.6873 | 0.5303 | 0.7282 |
| 0.1178 | 8.5875 | 1374 | 0.5310 | 0.6873 | 0.5310 | 0.7287 |
| 0.1178 | 8.6 | 1376 | 0.5518 | 0.6873 | 0.5518 | 0.7428 |
| 0.1178 | 8.6125 | 1378 | 0.5810 | 0.6873 | 0.5810 | 0.7623 |
| 0.1178 | 8.625 | 1380 | 0.6176 | 0.6488 | 0.6176 | 0.7858 |
| 0.1178 | 8.6375 | 1382 | 0.6356 | 0.6580 | 0.6356 | 0.7973 |
| 0.1178 | 8.65 | 1384 | 0.6278 | 0.6580 | 0.6278 | 0.7923 |
| 0.1178 | 8.6625 | 1386 | 0.6061 | 0.6689 | 0.6061 | 0.7786 |
| 0.1178 | 8.675 | 1388 | 0.5878 | 0.6597 | 0.5878 | 0.7667 |
| 0.1178 | 8.6875 | 1390 | 0.5706 | 0.6597 | 0.5706 | 0.7554 |
| 0.1178 | 8.7 | 1392 | 0.5648 | 0.6597 | 0.5648 | 0.7515 |
| 0.1178 | 8.7125 | 1394 | 0.5517 | 0.6873 | 0.5517 | 0.7428 |
| 0.1178 | 8.725 | 1396 | 0.5340 | 0.6873 | 0.5340 | 0.7308 |
| 0.1178 | 8.7375 | 1398 | 0.5277 | 0.6873 | 0.5277 | 0.7264 |
| 0.1178 | 8.75 | 1400 | 0.5237 | 0.6873 | 0.5237 | 0.7236 |
| 0.1178 | 8.7625 | 1402 | 0.5375 | 0.6873 | 0.5375 | 0.7332 |
| 0.1178 | 8.775 | 1404 | 0.5443 | 0.6873 | 0.5443 | 0.7378 |
| 0.1178 | 8.7875 | 1406 | 0.5583 | 0.6873 | 0.5583 | 0.7472 |
| 0.1178 | 8.8 | 1408 | 0.5600 | 0.6873 | 0.5600 | 0.7483 |
| 0.1178 | 8.8125 | 1410 | 0.5505 | 0.6873 | 0.5505 | 0.7419 |
| 0.1178 | 8.825 | 1412 | 0.5495 | 0.6873 | 0.5495 | 0.7413 |
| 0.1178 | 8.8375 | 1414 | 0.5566 | 0.6873 | 0.5566 | 0.7460 |
| 0.1178 | 8.85 | 1416 | 0.5600 | 0.6597 | 0.5600 | 0.7483 |
| 0.1178 | 8.8625 | 1418 | 0.5517 | 0.6597 | 0.5517 | 0.7428 |
| 0.1178 | 8.875 | 1420 | 0.5480 | 0.6597 | 0.5480 | 0.7402 |
| 0.1178 | 8.8875 | 1422 | 0.5286 | 0.6597 | 0.5286 | 0.7270 |
| 0.1178 | 8.9 | 1424 | 0.5067 | 0.6873 | 0.5067 | 0.7118 |
| 0.1178 | 8.9125 | 1426 | 0.5019 | 0.6873 | 0.5019 | 0.7085 |
| 0.1178 | 8.925 | 1428 | 0.4919 | 0.6873 | 0.4919 | 0.7013 |
| 0.1178 | 8.9375 | 1430 | 0.4933 | 0.6873 | 0.4933 | 0.7024 |
| 0.1178 | 8.95 | 1432 | 0.5060 | 0.6597 | 0.5060 | 0.7113 |
| 0.1178 | 8.9625 | 1434 | 0.5237 | 0.6597 | 0.5237 | 0.7237 |
| 0.1178 | 8.975 | 1436 | 0.5436 | 0.6597 | 0.5436 | 0.7373 |
| 0.1178 | 8.9875 | 1438 | 0.5566 | 0.6597 | 0.5566 | 0.7460 |
| 0.1178 | 9.0 | 1440 | 0.5593 | 0.6488 | 0.5593 | 0.7478 |
| 0.1178 | 9.0125 | 1442 | 0.5697 | 0.6488 | 0.5697 | 0.7548 |
| 0.1178 | 9.025 | 1444 | 0.5647 | 0.6488 | 0.5647 | 0.7515 |
| 0.1178 | 9.0375 | 1446 | 0.5606 | 0.6488 | 0.5606 | 0.7487 |
| 0.1178 | 9.05 | 1448 | 0.5425 | 0.6873 | 0.5425 | 0.7365 |
| 0.1178 | 9.0625 | 1450 | 0.5197 | 0.6873 | 0.5197 | 0.7209 |
| 0.1178 | 9.075 | 1452 | 0.4988 | 0.6873 | 0.4988 | 0.7062 |
| 0.1178 | 9.0875 | 1454 | 0.4825 | 0.6873 | 0.4825 | 0.6946 |
| 0.1178 | 9.1 | 1456 | 0.4798 | 0.6873 | 0.4798 | 0.6927 |
| 0.1178 | 9.1125 | 1458 | 0.4761 | 0.6873 | 0.4761 | 0.6900 |
| 0.1178 | 9.125 | 1460 | 0.4781 | 0.6873 | 0.4781 | 0.6914 |
| 0.1178 | 9.1375 | 1462 | 0.4854 | 0.6873 | 0.4854 | 0.6967 |
| 0.1178 | 9.15 | 1464 | 0.4944 | 0.6873 | 0.4944 | 0.7031 |
| 0.1178 | 9.1625 | 1466 | 0.5109 | 0.6873 | 0.5109 | 0.7147 |
| 0.1178 | 9.175 | 1468 | 0.5296 | 0.6873 | 0.5296 | 0.7277 |
| 0.1178 | 9.1875 | 1470 | 0.5480 | 0.6873 | 0.5480 | 0.7403 |
| 0.1178 | 9.2 | 1472 | 0.5701 | 0.6873 | 0.5701 | 0.7550 |
| 0.1178 | 9.2125 | 1474 | 0.5960 | 0.6755 | 0.5960 | 0.7720 |
| 0.1178 | 9.225 | 1476 | 0.6066 | 0.6755 | 0.6066 | 0.7788 |
| 0.1178 | 9.2375 | 1478 | 0.6053 | 0.6488 | 0.6053 | 0.7780 |
| 0.1178 | 9.25 | 1480 | 0.6027 | 0.6488 | 0.6027 | 0.7764 |
| 0.1178 | 9.2625 | 1482 | 0.6025 | 0.6488 | 0.6025 | 0.7762 |
| 0.1178 | 9.275 | 1484 | 0.5972 | 0.6488 | 0.5972 | 0.7728 |
| 0.1178 | 9.2875 | 1486 | 0.5928 | 0.6488 | 0.5928 | 0.7699 |
| 0.1178 | 9.3 | 1488 | 0.5784 | 0.6873 | 0.5784 | 0.7606 |
| 0.1178 | 9.3125 | 1490 | 0.5630 | 0.6873 | 0.5630 | 0.7503 |
| 0.1178 | 9.325 | 1492 | 0.5481 | 0.6873 | 0.5481 | 0.7404 |
| 0.1178 | 9.3375 | 1494 | 0.5423 | 0.6873 | 0.5423 | 0.7364 |
| 0.1178 | 9.35 | 1496 | 0.5390 | 0.6873 | 0.5390 | 0.7342 |
| 0.1178 | 9.3625 | 1498 | 0.5383 | 0.6873 | 0.5383 | 0.7337 |
| 0.0648 | 9.375 | 1500 | 0.5327 | 0.6873 | 0.5327 | 0.7299 |
| 0.0648 | 9.3875 | 1502 | 0.5208 | 0.6873 | 0.5208 | 0.7216 |
| 0.0648 | 9.4 | 1504 | 0.5073 | 0.6873 | 0.5073 | 0.7123 |
| 0.0648 | 9.4125 | 1506 | 0.4943 | 0.6873 | 0.4943 | 0.7031 |
| 0.0648 | 9.425 | 1508 | 0.4883 | 0.6873 | 0.4883 | 0.6988 |
| 0.0648 | 9.4375 | 1510 | 0.4914 | 0.6873 | 0.4914 | 0.7010 |
| 0.0648 | 9.45 | 1512 | 0.5003 | 0.6873 | 0.5003 | 0.7073 |
| 0.0648 | 9.4625 | 1514 | 0.5137 | 0.6873 | 0.5137 | 0.7167 |
| 0.0648 | 9.475 | 1516 | 0.5245 | 0.6873 | 0.5245 | 0.7242 |
| 0.0648 | 9.4875 | 1518 | 0.5319 | 0.6873 | 0.5319 | 0.7293 |
| 0.0648 | 9.5 | 1520 | 0.5347 | 0.6873 | 0.5347 | 0.7313 |
| 0.0648 | 9.5125 | 1522 | 0.5314 | 0.6873 | 0.5314 | 0.7290 |
| 0.0648 | 9.525 | 1524 | 0.5267 | 0.6873 | 0.5267 | 0.7257 |
| 0.0648 | 9.5375 | 1526 | 0.5240 | 0.6873 | 0.5240 | 0.7239 |
| 0.0648 | 9.55 | 1528 | 0.5272 | 0.6873 | 0.5272 | 0.7261 |
| 0.0648 | 9.5625 | 1530 | 0.5337 | 0.6873 | 0.5337 | 0.7306 |
| 0.0648 | 9.575 | 1532 | 0.5350 | 0.6873 | 0.5350 | 0.7314 |
| 0.0648 | 9.5875 | 1534 | 0.5408 | 0.6873 | 0.5408 | 0.7354 |
| 0.0648 | 9.6 | 1536 | 0.5457 | 0.6873 | 0.5457 | 0.7387 |
| 0.0648 | 9.6125 | 1538 | 0.5465 | 0.6873 | 0.5465 | 0.7393 |
| 0.0648 | 9.625 | 1540 | 0.5499 | 0.6873 | 0.5499 | 0.7416 |
| 0.0648 | 9.6375 | 1542 | 0.5487 | 0.6873 | 0.5487 | 0.7408 |
| 0.0648 | 9.65 | 1544 | 0.5485 | 0.6873 | 0.5485 | 0.7406 |
| 0.0648 | 9.6625 | 1546 | 0.5486 | 0.6873 | 0.5486 | 0.7407 |
| 0.0648 | 9.675 | 1548 | 0.5438 | 0.6873 | 0.5438 | 0.7375 |
| 0.0648 | 9.6875 | 1550 | 0.5396 | 0.6873 | 0.5396 | 0.7346 |
| 0.0648 | 9.7 | 1552 | 0.5363 | 0.6873 | 0.5363 | 0.7323 |
| 0.0648 | 9.7125 | 1554 | 0.5314 | 0.6873 | 0.5314 | 0.7290 |
| 0.0648 | 9.725 | 1556 | 0.5264 | 0.6873 | 0.5264 | 0.7255 |
| 0.0648 | 9.7375 | 1558 | 0.5241 | 0.6873 | 0.5241 | 0.7240 |
| 0.0648 | 9.75 | 1560 | 0.5217 | 0.6873 | 0.5217 | 0.7223 |
| 0.0648 | 9.7625 | 1562 | 0.5192 | 0.6873 | 0.5192 | 0.7205 |
| 0.0648 | 9.775 | 1564 | 0.5167 | 0.6873 | 0.5167 | 0.7188 |
| 0.0648 | 9.7875 | 1566 | 0.5144 | 0.6873 | 0.5144 | 0.7172 |
| 0.0648 | 9.8 | 1568 | 0.5110 | 0.6873 | 0.5110 | 0.7149 |
| 0.0648 | 9.8125 | 1570 | 0.5092 | 0.6873 | 0.5092 | 0.7136 |
| 0.0648 | 9.825 | 1572 | 0.5067 | 0.6873 | 0.5067 | 0.7118 |
| 0.0648 | 9.8375 | 1574 | 0.5053 | 0.6873 | 0.5053 | 0.7108 |
| 0.0648 | 9.85 | 1576 | 0.5034 | 0.6873 | 0.5034 | 0.7095 |
| 0.0648 | 9.8625 | 1578 | 0.5009 | 0.6873 | 0.5009 | 0.7077 |
| 0.0648 | 9.875 | 1580 | 0.4976 | 0.6873 | 0.4976 | 0.7054 |
| 0.0648 | 9.8875 | 1582 | 0.4968 | 0.6873 | 0.4968 | 0.7048 |
| 0.0648 | 9.9 | 1584 | 0.4961 | 0.6873 | 0.4961 | 0.7044 |
| 0.0648 | 9.9125 | 1586 | 0.4963 | 0.6873 | 0.4963 | 0.7045 |
| 0.0648 | 9.925 | 1588 | 0.4971 | 0.6873 | 0.4971 | 0.7050 |
| 0.0648 | 9.9375 | 1590 | 0.4975 | 0.6873 | 0.4975 | 0.7053 |
| 0.0648 | 9.95 | 1592 | 0.4979 | 0.6873 | 0.4979 | 0.7056 |
| 0.0648 | 9.9625 | 1594 | 0.4979 | 0.6873 | 0.4979 | 0.7056 |
| 0.0648 | 9.975 | 1596 | 0.4980 | 0.6873 | 0.4980 | 0.7057 |
| 0.0648 | 9.9875 | 1598 | 0.4983 | 0.6873 | 0.4983 | 0.7059 |
| 0.0648 | 10.0 | 1600 | 0.4985 | 0.6873 | 0.4985 | 0.7060 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
jpbalarini/bge-m3
|
jpbalarini
| 2024-11-27T21:33:11Z | 10 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"onnx",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"arxiv:2402.03216",
"arxiv:2004.04906",
"arxiv:2106.14807",
"arxiv:2107.05720",
"arxiv:2004.12832",
"license:mit",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-11-27T16:40:08Z |
---
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
license: mit
---
For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding
# BGE-M3 ([paper](https://arxiv.org/pdf/2402.03216.pdf), [code](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/BGE_M3))
In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity.
- Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval.
- Multi-Linguality: It can support more than 100 working languages.
- Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.
**Some suggestions for retrieval pipeline in RAG**
We recommend to use the following pipeline: hybrid retrieval + re-ranking.
- Hybrid retrieval leverages the strengths of various methods, offering higher accuracy and stronger generalization capabilities.
A classic example: using both embedding retrieval and the BM25 algorithm.
Now, you can try to use BGE-M3, which supports both embedding and sparse retrieval.
This allows you to obtain token weights (similar to the BM25) without any additional cost when generate dense embeddings.
To use hybrid retrieval, you can refer to [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb
) and [Milvus](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).
- As cross-encoder models, re-ranker demonstrates higher accuracy than bi-encoder embedding model.
Utilizing the re-ranking model (e.g., [bge-reranker](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/reranker), [bge-reranker-v2](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker)) after retrieval can further filter the selected text.
## News:
- 2024/7/1: **We update the MIRACL evaluation results of BGE-M3**. To reproduce the new results, you can refer to: [bge-m3_miracl_2cr](https://huggingface.co/datasets/hanhainebula/bge-m3_miracl_2cr). We have also updated our [paper](https://arxiv.org/pdf/2402.03216) on arXiv.
<details>
<summary> Details </summary>
The previous test results were lower because we mistakenly removed the passages that have the same id as the query from the search results. After correcting this mistake, the overall performance of BGE-M3 on MIRACL is higher than the previous results, but the experimental conclusion remains unchanged. The other results are not affected by this mistake. To reproduce the previous lower results, you need to add the `--remove-query` parameter when using `pyserini.search.faiss` or `pyserini.search.lucene` to search the passages.
</details>
- 2024/3/20: **Thanks Milvus team!** Now you can use hybrid retrieval of bge-m3 in Milvus: [pymilvus/examples
/hello_hybrid_sparse_dense.py](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).
- 2024/3/8: **Thanks for the [experimental results](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) from @[Yannael](https://huggingface.co/Yannael). In this benchmark, BGE-M3 achieves top performance in both English and other languages, surpassing models such as OpenAI.**
- 2024/3/2: Release unified fine-tuning [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune) and [data](https://huggingface.co/datasets/Shitao/bge-m3-data)
- 2024/2/6: We release the [MLDR](https://huggingface.co/datasets/Shitao/MLDR) (a long document retrieval dataset covering 13 languages) and [evaluation pipeline](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR).
- 2024/2/1: **Thanks for the excellent tool from Vespa.** You can easily use multiple modes of BGE-M3 following this [notebook](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb)
## Specs
- Model
| Model Name | Dimension | Sequence Length | Introduction |
|:----:|:---:|:---:|:---:|
| [BAAI/bge-m3](https://huggingface.co/BAAI/bge-m3) | 1024 | 8192 | multilingual; unified fine-tuning (dense, sparse, and colbert) from bge-m3-unsupervised|
| [BAAI/bge-m3-unsupervised](https://huggingface.co/BAAI/bge-m3-unsupervised) | 1024 | 8192 | multilingual; contrastive learning from bge-m3-retromae |
| [BAAI/bge-m3-retromae](https://huggingface.co/BAAI/bge-m3-retromae) | -- | 8192 | multilingual; extend the max_length of [xlm-roberta](https://huggingface.co/FacebookAI/xlm-roberta-large) to 8192 and further pretrained via [retromae](https://github.com/staoxiao/RetroMAE)|
| [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | English model |
| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | English model |
| [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) | 384 | 512 | English model |
- Data
| Dataset | Introduction |
|:----------------------------------------------------------:|:-------------------------------------------------:|
| [MLDR](https://huggingface.co/datasets/Shitao/MLDR) | Docuemtn Retrieval Dataset, covering 13 languages |
| [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data) | Fine-tuning data used by bge-m3 |
## FAQ
**1. Introduction for different retrieval methods**
- Dense retrieval: map the text into a single embedding, e.g., [DPR](https://arxiv.org/abs/2004.04906), [BGE-v1.5](https://github.com/FlagOpen/FlagEmbedding)
- Sparse retrieval (lexical matching): a vector of size equal to the vocabulary, with the majority of positions set to zero, calculating a weight only for tokens present in the text. e.g., BM25, [unicoil](https://arxiv.org/pdf/2106.14807.pdf), and [splade](https://arxiv.org/abs/2107.05720)
- Multi-vector retrieval: use multiple vectors to represent a text, e.g., [ColBERT](https://arxiv.org/abs/2004.12832).
**2. How to use BGE-M3 in other projects?**
For embedding retrieval, you can employ the BGE-M3 model using the same approach as BGE.
The only difference is that the BGE-M3 model no longer requires adding instructions to the queries.
For hybrid retrieval, you can use [Vespa](https://github.com/vespa-engine/pyvespa/blob/master/docs/sphinx/source/examples/mother-of-all-embedding-models-cloud.ipynb
) and [Milvus](https://github.com/milvus-io/pymilvus/blob/master/examples/hello_hybrid_sparse_dense.py).
**3. How to fine-tune bge-M3 model?**
You can follow the common in this [example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/finetune)
to fine-tune the dense embedding.
If you want to fine-tune all embedding function of m3 (dense, sparse and colbert), you can refer to the [unified_fine-tuning example](https://github.com/FlagOpen/FlagEmbedding/tree/master/examples/unified_finetune)
## Usage
Install:
```
git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
```
or:
```
pip install -U FlagEmbedding
```
### Generate Embedding for text
- Dense Embedding
```python
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3',
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
embeddings_1 = model.encode(sentences_1,
batch_size=12,
max_length=8192, # If you don't need such a long length, you can set a smaller value to speed up the encoding process.
)['dense_vecs']
embeddings_2 = model.encode(sentences_2)['dense_vecs']
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
# [[0.6265, 0.3477], [0.3499, 0.678 ]]
```
You also can use sentence-transformers and huggingface transformers to generate dense embeddings.
Refer to [baai_general_embedding](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/baai_general_embedding#usage) for details.
- Sparse Embedding (Lexical Weight)
```python
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=False)
output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=False)
# you can see the weight for each token:
print(model.convert_id_to_token(output_1['lexical_weights']))
# [{'What': 0.08356, 'is': 0.0814, 'B': 0.1296, 'GE': 0.252, 'M': 0.1702, '3': 0.2695, '?': 0.04092},
# {'De': 0.05005, 'fin': 0.1368, 'ation': 0.04498, 'of': 0.0633, 'BM': 0.2515, '25': 0.3335}]
# compute the scores via lexical mathcing
lexical_scores = model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_2['lexical_weights'][0])
print(lexical_scores)
# 0.19554901123046875
print(model.compute_lexical_matching_score(output_1['lexical_weights'][0], output_1['lexical_weights'][1]))
# 0.0
```
- Multi-Vector (ColBERT)
```python
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
output_1 = model.encode(sentences_1, return_dense=True, return_sparse=True, return_colbert_vecs=True)
output_2 = model.encode(sentences_2, return_dense=True, return_sparse=True, return_colbert_vecs=True)
print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][0]))
print(model.colbert_score(output_1['colbert_vecs'][0], output_2['colbert_vecs'][1]))
# 0.7797
# 0.4620
```
### Compute score for text pairs
Input a list of text pairs, you can get the scores computed by different methods.
```python
from FlagEmbedding import BGEM3FlagModel
model = BGEM3FlagModel('BAAI/bge-m3', use_fp16=True)
sentences_1 = ["What is BGE M3?", "Defination of BM25"]
sentences_2 = ["BGE M3 is an embedding model supporting dense retrieval, lexical matching and multi-vector interaction.",
"BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document"]
sentence_pairs = [[i,j] for i in sentences_1 for j in sentences_2]
print(model.compute_score(sentence_pairs,
max_passage_length=128, # a smaller max length leads to a lower latency
weights_for_different_modes=[0.4, 0.2, 0.4])) # weights_for_different_modes(w) is used to do weighted sum: w[0]*dense_score + w[1]*sparse_score + w[2]*colbert_score
# {
# 'colbert': [0.7796499729156494, 0.4621465802192688, 0.4523794651031494, 0.7898575067520142],
# 'sparse': [0.195556640625, 0.00879669189453125, 0.0, 0.1802978515625],
# 'dense': [0.6259765625, 0.347412109375, 0.349853515625, 0.67822265625],
# 'sparse+dense': [0.482503205537796, 0.23454029858112335, 0.2332356721162796, 0.5122477412223816],
# 'colbert+sparse+dense': [0.6013619303703308, 0.3255828022956848, 0.32089319825172424, 0.6232916116714478]
# }
```
## Evaluation
We provide the evaluation script for [MKQA](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MKQA) and [MLDR](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR)
### Benchmarks from the open-source community
The BGE-M3 model emerged as the top performer on this benchmark (OAI is short for OpenAI).
For more details, please refer to the [article](https://towardsdatascience.com/openai-vs-open-source-multilingual-embedding-models-e5ccb7c90f05) and [Github Repo](https://github.com/Yannael/multilingual-embeddings)
### Our results
- Multilingual (Miracl dataset)
- Cross-lingual (MKQA dataset)
- Long Document Retrieval
- MLDR:
Please note that [MLDR](https://huggingface.co/datasets/Shitao/MLDR) is a document retrieval dataset we constructed via LLM,
covering 13 languages, including test set, validation set, and training set.
We utilized the training set from MLDR to enhance the model's long document retrieval capabilities.
Therefore, comparing baselines with `Dense w.o.long`(fine-tuning without long document dataset) is more equitable.
Additionally, this long document retrieval dataset will be open-sourced to address the current lack of open-source multilingual long text retrieval datasets.
We believe that this data will be helpful for the open-source community in training document retrieval models.
- NarritiveQA:
- Comparison with BM25
We utilized Pyserini to implement BM25, and the test results can be reproduced by this [script](https://github.com/FlagOpen/FlagEmbedding/tree/master/C_MTEB/MLDR#bm25-baseline).
We tested BM25 using two different tokenizers:
one using Lucene Analyzer and the other using the same tokenizer as M3 (i.e., the tokenizer of xlm-roberta).
The results indicate that BM25 remains a competitive baseline,
especially in long document retrieval.
## Training
- Self-knowledge Distillation: combining multiple outputs from different
retrieval modes as reward signal to enhance the performance of single mode(especially for sparse retrieval and multi-vec(colbert) retrival)
- Efficient Batching: Improve the efficiency when fine-tuning on long text.
The small-batch strategy is simple but effective, which also can used to fine-tune large embedding model.
- MCLS: A simple method to improve the performance on long text without fine-tuning.
If you have no enough resource to fine-tuning model with long text, the method is useful.
Refer to our [report](https://arxiv.org/pdf/2402.03216.pdf) for more details.
## Acknowledgement
Thanks to the authors of open-sourced datasets, including Miracl, MKQA, NarritiveQA, etc.
Thanks to the open-sourced libraries like [Tevatron](https://github.com/texttron/tevatron), [Pyserini](https://github.com/castorini/pyserini).
## Citation
If you find this repository useful, please consider giving a star :star: and citation
```
@misc{bge-m3,
title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation},
author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
year={2024},
eprint={2402.03216},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
xw17/Qwen2-1.5B-Instruct_finetuned_s03
|
xw17
| 2024-11-27T21:33:03Z | 138 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T21:29:19Z |
---
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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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### Results
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## Model Examination [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
xw17/Qwen2-1.5B-Instruct_finetuned_s02
|
xw17
| 2024-11-27T21:23:59Z | 139 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T21:22:06Z |
---
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 π€ 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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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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|
xw17/Qwen2-1.5B-Instruct_finetuned_s01
|
xw17
| 2024-11-27T21:21:19Z | 139 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T21:19:20Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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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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|
MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k15_task1_organization_fold0
|
MayBashendy
| 2024-11-27T21:18:16Z | 182 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T21:03:02Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_run2_AugV5_k15_task1_organization_fold0
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. -->
# Arabic_FineTuningAraBERT_run2_AugV5_k15_task1_organization_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7152
- Qwk: 0.7333
- Mse: 0.7152
- Rmse: 0.8457
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0126 | 2 | 5.3646 | -0.0604 | 5.3646 | 2.3162 |
| No log | 0.0252 | 4 | 3.5660 | 0.0245 | 3.5660 | 1.8884 |
| No log | 0.0377 | 6 | 3.6637 | -0.1053 | 3.6637 | 1.9141 |
| No log | 0.0503 | 8 | 3.5174 | -0.2189 | 3.5174 | 1.8755 |
| No log | 0.0629 | 10 | 2.1386 | -0.0383 | 2.1386 | 1.4624 |
| No log | 0.0755 | 12 | 1.8068 | 0.2006 | 1.8068 | 1.3442 |
| No log | 0.0881 | 14 | 2.0326 | 0.2597 | 2.0326 | 1.4257 |
| No log | 0.1006 | 16 | 2.1352 | 0.2098 | 2.1352 | 1.4612 |
| No log | 0.1132 | 18 | 1.8460 | 0.2125 | 1.8460 | 1.3587 |
| No log | 0.1258 | 20 | 1.8444 | 0.2687 | 1.8444 | 1.3581 |
| No log | 0.1384 | 22 | 1.8502 | 0.2434 | 1.8502 | 1.3602 |
| No log | 0.1509 | 24 | 1.6706 | 0.2958 | 1.6706 | 1.2925 |
| No log | 0.1635 | 26 | 1.3444 | 0.2791 | 1.3444 | 1.1595 |
| No log | 0.1761 | 28 | 1.3213 | 0.2032 | 1.3213 | 1.1495 |
| No log | 0.1887 | 30 | 1.3187 | 0.2032 | 1.3187 | 1.1483 |
| No log | 0.2013 | 32 | 1.2996 | 0.2794 | 1.2996 | 1.1400 |
| No log | 0.2138 | 34 | 1.2977 | 0.2794 | 1.2977 | 1.1392 |
| No log | 0.2264 | 36 | 1.2554 | 0.4074 | 1.2554 | 1.1205 |
| No log | 0.2390 | 38 | 1.1811 | 0.4074 | 1.1811 | 1.0868 |
| No log | 0.2516 | 40 | 1.1049 | 0.4074 | 1.1049 | 1.0511 |
| No log | 0.2642 | 42 | 1.0548 | 0.4059 | 1.0548 | 1.0271 |
| No log | 0.2767 | 44 | 1.1169 | 0.4310 | 1.1169 | 1.0568 |
| No log | 0.2893 | 46 | 1.1087 | 0.4310 | 1.1087 | 1.0529 |
| No log | 0.3019 | 48 | 1.0740 | 0.4310 | 1.0740 | 1.0363 |
| No log | 0.3145 | 50 | 0.9815 | 0.4310 | 0.9815 | 0.9907 |
| No log | 0.3270 | 52 | 0.9532 | 0.4059 | 0.9532 | 0.9763 |
| No log | 0.3396 | 54 | 0.8784 | 0.4310 | 0.8784 | 0.9372 |
| No log | 0.3522 | 56 | 0.8478 | 0.4310 | 0.8478 | 0.9207 |
| No log | 0.3648 | 58 | 0.8435 | 0.4310 | 0.8435 | 0.9184 |
| No log | 0.3774 | 60 | 0.8602 | 0.4310 | 0.8602 | 0.9275 |
| No log | 0.3899 | 62 | 0.9347 | 0.4310 | 0.9347 | 0.9668 |
| No log | 0.4025 | 64 | 0.8943 | 0.4310 | 0.8943 | 0.9457 |
| No log | 0.4151 | 66 | 0.8588 | 0.4310 | 0.8588 | 0.9267 |
| No log | 0.4277 | 68 | 0.8943 | 0.4310 | 0.8943 | 0.9457 |
| No log | 0.4403 | 70 | 0.9265 | 0.4310 | 0.9265 | 0.9626 |
| No log | 0.4528 | 72 | 0.9701 | 0.4044 | 0.9701 | 0.9850 |
| No log | 0.4654 | 74 | 1.1708 | 0.2759 | 1.1708 | 1.0820 |
| No log | 0.4780 | 76 | 1.5199 | 0.1973 | 1.5199 | 1.2328 |
| No log | 0.4906 | 78 | 1.4251 | 0.2759 | 1.4251 | 1.1938 |
| No log | 0.5031 | 80 | 0.9576 | 0.4296 | 0.9576 | 0.9786 |
| No log | 0.5157 | 82 | 0.9157 | 0.4867 | 0.9157 | 0.9569 |
| No log | 0.5283 | 84 | 0.9718 | 0.5290 | 0.9718 | 0.9858 |
| No log | 0.5409 | 86 | 0.9544 | 0.4878 | 0.9544 | 0.9769 |
| No log | 0.5535 | 88 | 1.2041 | 0.1724 | 1.2041 | 1.0973 |
| No log | 0.5660 | 90 | 2.2105 | -0.1913 | 2.2105 | 1.4868 |
| No log | 0.5786 | 92 | 3.0477 | -0.2449 | 3.0477 | 1.7458 |
| No log | 0.5912 | 94 | 3.5236 | -0.2996 | 3.5236 | 1.8771 |
| No log | 0.6038 | 96 | 3.6185 | -0.3348 | 3.6185 | 1.9022 |
| No log | 0.6164 | 98 | 3.3615 | -0.3034 | 3.3615 | 1.8334 |
| No log | 0.6289 | 100 | 3.0527 | -0.2743 | 3.0527 | 1.7472 |
| No log | 0.6415 | 102 | 2.6057 | -0.0774 | 2.6057 | 1.6142 |
| No log | 0.6541 | 104 | 2.2016 | 0.0242 | 2.2016 | 1.4838 |
| No log | 0.6667 | 106 | 2.0410 | -0.0221 | 2.0410 | 1.4286 |
| No log | 0.6792 | 108 | 2.0171 | -0.0976 | 2.0171 | 1.4203 |
| No log | 0.6918 | 110 | 2.2900 | -0.2106 | 2.2900 | 1.5133 |
| No log | 0.7044 | 112 | 2.1620 | -0.1887 | 2.1620 | 1.4704 |
| No log | 0.7170 | 114 | 1.9009 | -0.0769 | 1.9009 | 1.3787 |
| No log | 0.7296 | 116 | 1.5329 | 0.1476 | 1.5329 | 1.2381 |
| No log | 0.7421 | 118 | 1.4202 | 0.1993 | 1.4202 | 1.1917 |
| No log | 0.7547 | 120 | 1.3884 | 0.2241 | 1.3884 | 1.1783 |
| No log | 0.7673 | 122 | 1.3932 | 0.2241 | 1.3932 | 1.1803 |
| No log | 0.7799 | 124 | 1.3964 | 0.1993 | 1.3964 | 1.1817 |
| No log | 0.7925 | 126 | 1.3380 | 0.3276 | 1.3380 | 1.1567 |
| No log | 0.8050 | 128 | 1.0067 | 0.4842 | 1.0067 | 1.0033 |
| No log | 0.8176 | 130 | 0.9147 | 0.5323 | 0.9147 | 0.9564 |
| No log | 0.8302 | 132 | 0.8818 | 0.5323 | 0.8818 | 0.9390 |
| No log | 0.8428 | 134 | 0.8752 | 0.5313 | 0.8752 | 0.9355 |
| No log | 0.8553 | 136 | 0.8615 | 0.5313 | 0.8615 | 0.9282 |
| No log | 0.8679 | 138 | 0.8970 | 0.5297 | 0.8970 | 0.9471 |
| No log | 0.8805 | 140 | 0.9208 | 0.5288 | 0.9208 | 0.9596 |
| No log | 0.8931 | 142 | 0.9202 | 0.5089 | 0.9202 | 0.9592 |
| No log | 0.9057 | 144 | 0.9047 | 0.5089 | 0.9047 | 0.9512 |
| No log | 0.9182 | 146 | 0.8955 | 0.5522 | 0.8955 | 0.9463 |
| No log | 0.9308 | 148 | 0.9818 | 0.4549 | 0.9818 | 0.9908 |
| No log | 0.9434 | 150 | 1.0954 | 0.3747 | 1.0954 | 1.0466 |
| No log | 0.9560 | 152 | 1.0990 | 0.2454 | 1.0990 | 1.0483 |
| No log | 0.9686 | 154 | 1.1368 | 0.2454 | 1.1368 | 1.0662 |
| No log | 0.9811 | 156 | 1.1900 | 0.3243 | 1.1900 | 1.0909 |
| No log | 0.9937 | 158 | 1.2909 | 0.3731 | 1.2909 | 1.1362 |
| No log | 1.0063 | 160 | 1.3964 | 0.3774 | 1.3964 | 1.1817 |
| No log | 1.0189 | 162 | 1.7012 | 0.1658 | 1.7012 | 1.3043 |
| No log | 1.0314 | 164 | 1.7196 | 0.1950 | 1.7196 | 1.3114 |
| No log | 1.0440 | 166 | 0.9873 | 0.6131 | 0.9873 | 0.9936 |
| No log | 1.0566 | 168 | 0.7494 | 0.6970 | 0.7494 | 0.8657 |
| No log | 1.0692 | 170 | 0.7507 | 0.7047 | 0.7507 | 0.8664 |
| No log | 1.0818 | 172 | 0.7781 | 0.6469 | 0.7781 | 0.8821 |
| No log | 1.0943 | 174 | 0.7165 | 0.6157 | 0.7165 | 0.8465 |
| No log | 1.1069 | 176 | 0.7516 | 0.6233 | 0.7516 | 0.8670 |
| No log | 1.1195 | 178 | 0.8393 | 0.5312 | 0.8393 | 0.9161 |
| No log | 1.1321 | 180 | 0.9773 | 0.5640 | 0.9773 | 0.9886 |
| No log | 1.1447 | 182 | 0.9379 | 0.5640 | 0.9379 | 0.9685 |
| No log | 1.1572 | 184 | 0.7675 | 0.7114 | 0.7675 | 0.8761 |
| No log | 1.1698 | 186 | 0.6754 | 0.7449 | 0.6754 | 0.8218 |
| No log | 1.1824 | 188 | 0.6877 | 0.6818 | 0.6877 | 0.8293 |
| No log | 1.1950 | 190 | 0.7502 | 0.5812 | 0.7502 | 0.8661 |
| No log | 1.2075 | 192 | 0.7326 | 0.6414 | 0.7326 | 0.8559 |
| No log | 1.2201 | 194 | 0.6915 | 0.7327 | 0.6915 | 0.8315 |
| No log | 1.2327 | 196 | 0.6887 | 0.7526 | 0.6887 | 0.8299 |
| No log | 1.2453 | 198 | 0.7770 | 0.5954 | 0.7770 | 0.8815 |
| No log | 1.2579 | 200 | 0.8160 | 0.6069 | 0.8160 | 0.9033 |
| No log | 1.2704 | 202 | 0.7628 | 0.7149 | 0.7628 | 0.8734 |
| No log | 1.2830 | 204 | 0.7277 | 0.7003 | 0.7277 | 0.8531 |
| No log | 1.2956 | 206 | 0.7242 | 0.7003 | 0.7242 | 0.8510 |
| No log | 1.3082 | 208 | 0.7221 | 0.7073 | 0.7221 | 0.8498 |
| No log | 1.3208 | 210 | 0.7758 | 0.6038 | 0.7758 | 0.8808 |
| No log | 1.3333 | 212 | 0.8322 | 0.5468 | 0.8322 | 0.9122 |
| No log | 1.3459 | 214 | 0.9277 | 0.5422 | 0.9277 | 0.9632 |
| No log | 1.3585 | 216 | 0.8845 | 0.6168 | 0.8845 | 0.9405 |
| No log | 1.3711 | 218 | 0.7653 | 0.6987 | 0.7653 | 0.8748 |
| No log | 1.3836 | 220 | 0.6728 | 0.7608 | 0.6728 | 0.8203 |
| No log | 1.3962 | 222 | 0.6533 | 0.7451 | 0.6533 | 0.8083 |
| No log | 1.4088 | 224 | 0.6702 | 0.7437 | 0.6702 | 0.8187 |
| No log | 1.4214 | 226 | 0.8177 | 0.6219 | 0.8177 | 0.9043 |
| No log | 1.4340 | 228 | 0.9721 | 0.6253 | 0.9721 | 0.9859 |
| No log | 1.4465 | 230 | 0.9625 | 0.5926 | 0.9625 | 0.9811 |
| No log | 1.4591 | 232 | 0.7695 | 0.5722 | 0.7695 | 0.8772 |
| No log | 1.4717 | 234 | 0.6414 | 0.6871 | 0.6414 | 0.8009 |
| No log | 1.4843 | 236 | 0.6331 | 0.6945 | 0.6331 | 0.7957 |
| No log | 1.4969 | 238 | 0.6228 | 0.6945 | 0.6228 | 0.7892 |
| No log | 1.5094 | 240 | 0.6211 | 0.7623 | 0.6211 | 0.7881 |
| No log | 1.5220 | 242 | 0.6245 | 0.7226 | 0.6245 | 0.7902 |
| No log | 1.5346 | 244 | 0.6273 | 0.7520 | 0.6273 | 0.7920 |
| No log | 1.5472 | 246 | 0.6436 | 0.7194 | 0.6436 | 0.8022 |
| No log | 1.5597 | 248 | 0.6570 | 0.7194 | 0.6570 | 0.8106 |
| No log | 1.5723 | 250 | 0.6861 | 0.6266 | 0.6861 | 0.8283 |
| No log | 1.5849 | 252 | 0.7071 | 0.6982 | 0.7071 | 0.8409 |
| No log | 1.5975 | 254 | 0.7166 | 0.7259 | 0.7166 | 0.8465 |
| No log | 1.6101 | 256 | 0.6755 | 0.8183 | 0.6755 | 0.8219 |
| No log | 1.6226 | 258 | 0.6997 | 0.7451 | 0.6997 | 0.8365 |
| No log | 1.6352 | 260 | 0.7287 | 0.7526 | 0.7287 | 0.8537 |
| No log | 1.6478 | 262 | 0.7941 | 0.7055 | 0.7941 | 0.8911 |
| No log | 1.6604 | 264 | 0.9224 | 0.7182 | 0.9224 | 0.9604 |
| No log | 1.6730 | 266 | 0.9932 | 0.6225 | 0.9932 | 0.9966 |
| No log | 1.6855 | 268 | 0.9656 | 0.6574 | 0.9656 | 0.9827 |
| No log | 1.6981 | 270 | 0.9200 | 0.6479 | 0.9200 | 0.9592 |
| No log | 1.7107 | 272 | 0.9522 | 0.6238 | 0.9522 | 0.9758 |
| No log | 1.7233 | 274 | 1.0482 | 0.5236 | 1.0482 | 1.0238 |
| No log | 1.7358 | 276 | 1.1778 | 0.4146 | 1.1778 | 1.0853 |
| No log | 1.7484 | 278 | 0.9511 | 0.6465 | 0.9511 | 0.9752 |
| No log | 1.7610 | 280 | 0.7504 | 0.6541 | 0.7504 | 0.8662 |
| No log | 1.7736 | 282 | 0.6907 | 0.7128 | 0.6907 | 0.8311 |
| No log | 1.7862 | 284 | 0.6664 | 0.7128 | 0.6664 | 0.8163 |
| No log | 1.7987 | 286 | 0.6638 | 0.7268 | 0.6638 | 0.8148 |
| No log | 1.8113 | 288 | 0.6833 | 0.7268 | 0.6833 | 0.8266 |
| No log | 1.8239 | 290 | 0.6721 | 0.7268 | 0.6721 | 0.8198 |
| No log | 1.8365 | 292 | 0.6626 | 0.7268 | 0.6626 | 0.8140 |
| No log | 1.8491 | 294 | 0.6739 | 0.7608 | 0.6739 | 0.8209 |
| No log | 1.8616 | 296 | 0.7117 | 0.7204 | 0.7117 | 0.8436 |
| No log | 1.8742 | 298 | 0.6868 | 0.7451 | 0.6868 | 0.8288 |
| No log | 1.8868 | 300 | 0.6605 | 0.7285 | 0.6605 | 0.8127 |
| No log | 1.8994 | 302 | 0.6532 | 0.7064 | 0.6532 | 0.8082 |
| No log | 1.9119 | 304 | 0.6595 | 0.7064 | 0.6595 | 0.8121 |
| No log | 1.9245 | 306 | 0.6553 | 0.7064 | 0.6553 | 0.8095 |
| No log | 1.9371 | 308 | 0.6531 | 0.7064 | 0.6531 | 0.8081 |
| No log | 1.9497 | 310 | 0.6429 | 0.7064 | 0.6429 | 0.8018 |
| No log | 1.9623 | 312 | 0.6468 | 0.7060 | 0.6468 | 0.8042 |
| No log | 1.9748 | 314 | 0.6554 | 0.7 | 0.6554 | 0.8096 |
| No log | 1.9874 | 316 | 0.6620 | 0.7123 | 0.6620 | 0.8136 |
| No log | 2.0 | 318 | 0.6702 | 0.7239 | 0.6702 | 0.8186 |
| No log | 2.0126 | 320 | 0.6480 | 0.7239 | 0.6480 | 0.8050 |
| No log | 2.0252 | 322 | 0.6301 | 0.7063 | 0.6301 | 0.7938 |
| No log | 2.0377 | 324 | 0.6595 | 0.7451 | 0.6595 | 0.8121 |
| No log | 2.0503 | 326 | 0.6783 | 0.7451 | 0.6783 | 0.8236 |
| No log | 2.0629 | 328 | 0.6625 | 0.7529 | 0.6625 | 0.8139 |
| No log | 2.0755 | 330 | 0.6213 | 0.7614 | 0.6213 | 0.7882 |
| No log | 2.0881 | 332 | 0.7118 | 0.6557 | 0.7118 | 0.8437 |
| No log | 2.1006 | 334 | 0.8499 | 0.6253 | 0.8499 | 0.9219 |
| No log | 2.1132 | 336 | 0.8278 | 0.6253 | 0.8278 | 0.9098 |
| No log | 2.1258 | 338 | 0.7019 | 0.6563 | 0.7019 | 0.8378 |
| No log | 2.1384 | 340 | 0.6195 | 0.7605 | 0.6195 | 0.7871 |
| No log | 2.1509 | 342 | 0.6330 | 0.7451 | 0.6330 | 0.7956 |
| No log | 2.1635 | 344 | 0.6284 | 0.7451 | 0.6284 | 0.7927 |
| No log | 2.1761 | 346 | 0.6033 | 0.8022 | 0.6033 | 0.7767 |
| No log | 2.1887 | 348 | 0.6243 | 0.7779 | 0.6243 | 0.7902 |
| No log | 2.2013 | 350 | 0.6590 | 0.7517 | 0.6590 | 0.8118 |
| No log | 2.2138 | 352 | 0.7102 | 0.6121 | 0.7102 | 0.8428 |
| No log | 2.2264 | 354 | 0.6890 | 0.6551 | 0.6890 | 0.8301 |
| No log | 2.2390 | 356 | 0.6492 | 0.7322 | 0.6492 | 0.8057 |
| No log | 2.2516 | 358 | 0.6093 | 0.7430 | 0.6093 | 0.7806 |
| No log | 2.2642 | 360 | 0.5982 | 0.7983 | 0.5982 | 0.7734 |
| No log | 2.2767 | 362 | 0.5905 | 0.8123 | 0.5905 | 0.7684 |
| No log | 2.2893 | 364 | 0.6272 | 0.7672 | 0.6272 | 0.7920 |
| No log | 2.3019 | 366 | 0.7654 | 0.7014 | 0.7654 | 0.8749 |
| No log | 2.3145 | 368 | 0.8883 | 0.6573 | 0.8883 | 0.9425 |
| No log | 2.3270 | 370 | 0.9089 | 0.6830 | 0.9089 | 0.9534 |
| No log | 2.3396 | 372 | 0.9335 | 0.5989 | 0.9335 | 0.9662 |
| No log | 2.3522 | 374 | 0.8492 | 0.6054 | 0.8492 | 0.9215 |
| No log | 2.3648 | 376 | 0.7014 | 0.7144 | 0.7014 | 0.8375 |
| No log | 2.3774 | 378 | 0.6753 | 0.7448 | 0.6753 | 0.8218 |
| No log | 2.3899 | 380 | 0.7006 | 0.7156 | 0.7006 | 0.8370 |
| No log | 2.4025 | 382 | 0.7725 | 0.7308 | 0.7725 | 0.8789 |
| No log | 2.4151 | 384 | 0.8181 | 0.7689 | 0.8181 | 0.9045 |
| No log | 2.4277 | 386 | 0.7582 | 0.7612 | 0.7582 | 0.8707 |
| No log | 2.4403 | 388 | 0.6811 | 0.7614 | 0.6811 | 0.8253 |
| No log | 2.4528 | 390 | 0.6532 | 0.7305 | 0.6532 | 0.8082 |
| No log | 2.4654 | 392 | 0.7208 | 0.6008 | 0.7208 | 0.8490 |
| No log | 2.4780 | 394 | 0.7594 | 0.5965 | 0.7594 | 0.8715 |
| No log | 2.4906 | 396 | 0.7071 | 0.6053 | 0.7071 | 0.8409 |
| No log | 2.5031 | 398 | 0.6448 | 0.7188 | 0.6448 | 0.8030 |
| No log | 2.5157 | 400 | 0.6311 | 0.7840 | 0.6311 | 0.7944 |
| No log | 2.5283 | 402 | 0.6287 | 0.7840 | 0.6287 | 0.7929 |
| No log | 2.5409 | 404 | 0.6692 | 0.6008 | 0.6692 | 0.8180 |
| No log | 2.5535 | 406 | 0.7865 | 0.5532 | 0.7865 | 0.8869 |
| No log | 2.5660 | 408 | 0.8696 | 0.5532 | 0.8696 | 0.9325 |
| No log | 2.5786 | 410 | 0.8274 | 0.5862 | 0.8274 | 0.9096 |
| No log | 2.5912 | 412 | 0.8090 | 0.5862 | 0.8090 | 0.8994 |
| No log | 2.6038 | 414 | 0.7601 | 0.6677 | 0.7601 | 0.8719 |
| No log | 2.6164 | 416 | 0.7613 | 0.6732 | 0.7613 | 0.8725 |
| No log | 2.6289 | 418 | 0.6797 | 0.7759 | 0.6797 | 0.8245 |
| No log | 2.6415 | 420 | 0.6643 | 0.7285 | 0.6643 | 0.8151 |
| No log | 2.6541 | 422 | 0.6782 | 0.7064 | 0.6782 | 0.8235 |
| No log | 2.6667 | 424 | 0.6784 | 0.7064 | 0.6784 | 0.8236 |
| No log | 2.6792 | 426 | 0.6578 | 0.7763 | 0.6578 | 0.8110 |
| No log | 2.6918 | 428 | 0.6788 | 0.7602 | 0.6788 | 0.8239 |
| No log | 2.7044 | 430 | 0.7189 | 0.6866 | 0.7189 | 0.8479 |
| No log | 2.7170 | 432 | 0.7510 | 0.6388 | 0.7510 | 0.8666 |
| No log | 2.7296 | 434 | 0.8371 | 0.5896 | 0.8371 | 0.9149 |
| No log | 2.7421 | 436 | 0.9815 | 0.5896 | 0.9815 | 0.9907 |
| No log | 2.7547 | 438 | 1.0329 | 0.5556 | 1.0329 | 1.0163 |
| No log | 2.7673 | 440 | 0.9502 | 0.5896 | 0.9502 | 0.9748 |
| No log | 2.7799 | 442 | 0.8341 | 0.6435 | 0.8341 | 0.9133 |
| No log | 2.7925 | 444 | 0.7724 | 0.7675 | 0.7724 | 0.8789 |
| No log | 2.8050 | 446 | 0.7679 | 0.7060 | 0.7679 | 0.8763 |
| No log | 2.8176 | 448 | 0.7626 | 0.7060 | 0.7626 | 0.8733 |
| No log | 2.8302 | 450 | 0.7612 | 0.6646 | 0.7612 | 0.8725 |
| No log | 2.8428 | 452 | 0.7382 | 0.6753 | 0.7382 | 0.8592 |
| No log | 2.8553 | 454 | 0.7279 | 0.6616 | 0.7279 | 0.8532 |
| No log | 2.8679 | 456 | 0.8026 | 0.7211 | 0.8026 | 0.8959 |
| No log | 2.8805 | 458 | 0.8741 | 0.5581 | 0.8741 | 0.9349 |
| No log | 2.8931 | 460 | 0.9117 | 0.5581 | 0.9117 | 0.9548 |
| No log | 2.9057 | 462 | 0.8594 | 0.5581 | 0.8594 | 0.9271 |
| No log | 2.9182 | 464 | 0.7847 | 0.7333 | 0.7847 | 0.8859 |
| No log | 2.9308 | 466 | 0.7535 | 0.7024 | 0.7535 | 0.8681 |
| No log | 2.9434 | 468 | 0.7557 | 0.7060 | 0.7557 | 0.8693 |
| No log | 2.9560 | 470 | 0.7608 | 0.7 | 0.7608 | 0.8722 |
| No log | 2.9686 | 472 | 0.7581 | 0.7 | 0.7581 | 0.8707 |
| No log | 2.9811 | 474 | 0.7609 | 0.7 | 0.7609 | 0.8723 |
| No log | 2.9937 | 476 | 0.7573 | 0.6543 | 0.7573 | 0.8702 |
| No log | 3.0063 | 478 | 0.7277 | 0.6543 | 0.7277 | 0.8531 |
| No log | 3.0189 | 480 | 0.7040 | 0.6593 | 0.7040 | 0.8391 |
| No log | 3.0314 | 482 | 0.7065 | 0.6588 | 0.7065 | 0.8406 |
| No log | 3.0440 | 484 | 0.7173 | 0.6636 | 0.7173 | 0.8469 |
| No log | 3.0566 | 486 | 0.7417 | 0.6543 | 0.7417 | 0.8612 |
| No log | 3.0692 | 488 | 0.7438 | 0.6996 | 0.7438 | 0.8624 |
| No log | 3.0818 | 490 | 0.7636 | 0.6543 | 0.7636 | 0.8739 |
| No log | 3.0943 | 492 | 0.8364 | 0.6889 | 0.8364 | 0.9145 |
| No log | 3.1069 | 494 | 0.9851 | 0.6830 | 0.9851 | 0.9925 |
| No log | 3.1195 | 496 | 1.0410 | 0.6884 | 1.0410 | 1.0203 |
| No log | 3.1321 | 498 | 1.0151 | 0.6943 | 1.0151 | 1.0075 |
| 0.6288 | 3.1447 | 500 | 0.9013 | 0.6943 | 0.9013 | 0.9494 |
| 0.6288 | 3.1572 | 502 | 0.8509 | 0.6943 | 0.8509 | 0.9224 |
| 0.6288 | 3.1698 | 504 | 0.8073 | 0.7255 | 0.8073 | 0.8985 |
| 0.6288 | 3.1824 | 506 | 0.7866 | 0.7051 | 0.7866 | 0.8869 |
| 0.6288 | 3.1950 | 508 | 0.7967 | 0.7115 | 0.7967 | 0.8926 |
| 0.6288 | 3.2075 | 510 | 0.8092 | 0.7115 | 0.8092 | 0.8995 |
| 0.6288 | 3.2201 | 512 | 0.8357 | 0.6947 | 0.8357 | 0.9141 |
| 0.6288 | 3.2327 | 514 | 0.8454 | 0.6639 | 0.8454 | 0.9195 |
| 0.6288 | 3.2453 | 516 | 0.8362 | 0.6639 | 0.8362 | 0.9144 |
| 0.6288 | 3.2579 | 518 | 0.8451 | 0.6543 | 0.8451 | 0.9193 |
| 0.6288 | 3.2704 | 520 | 0.8587 | 0.6543 | 0.8587 | 0.9266 |
| 0.6288 | 3.2830 | 522 | 0.8779 | 0.6203 | 0.8779 | 0.9370 |
| 0.6288 | 3.2956 | 524 | 0.9051 | 0.6203 | 0.9051 | 0.9514 |
| 0.6288 | 3.3082 | 526 | 0.9136 | 0.6203 | 0.9136 | 0.9558 |
| 0.6288 | 3.3208 | 528 | 0.9176 | 0.6686 | 0.9176 | 0.9579 |
| 0.6288 | 3.3333 | 530 | 0.8743 | 0.6237 | 0.8743 | 0.9350 |
| 0.6288 | 3.3459 | 532 | 0.8175 | 0.6996 | 0.8175 | 0.9042 |
| 0.6288 | 3.3585 | 534 | 0.7898 | 0.7 | 0.7898 | 0.8887 |
| 0.6288 | 3.3711 | 536 | 0.7719 | 0.7196 | 0.7719 | 0.8786 |
| 0.6288 | 3.3836 | 538 | 0.7547 | 0.7704 | 0.7547 | 0.8687 |
| 0.6288 | 3.3962 | 540 | 0.7568 | 0.7310 | 0.7568 | 0.8699 |
| 0.6288 | 3.4088 | 542 | 0.7802 | 0.6818 | 0.7802 | 0.8833 |
| 0.6288 | 3.4214 | 544 | 0.8019 | 0.6860 | 0.8019 | 0.8955 |
| 0.6288 | 3.4340 | 546 | 0.7967 | 0.6860 | 0.7967 | 0.8926 |
| 0.6288 | 3.4465 | 548 | 0.7727 | 0.6678 | 0.7727 | 0.8791 |
| 0.6288 | 3.4591 | 550 | 0.7523 | 0.6678 | 0.7523 | 0.8673 |
| 0.6288 | 3.4717 | 552 | 0.7197 | 0.7234 | 0.7197 | 0.8484 |
| 0.6288 | 3.4843 | 554 | 0.7192 | 0.7157 | 0.7192 | 0.8481 |
| 0.6288 | 3.4969 | 556 | 0.7704 | 0.6435 | 0.7704 | 0.8777 |
| 0.6288 | 3.5094 | 558 | 0.7740 | 0.6388 | 0.7740 | 0.8798 |
| 0.6288 | 3.5220 | 560 | 0.7612 | 0.7110 | 0.7612 | 0.8725 |
| 0.6288 | 3.5346 | 562 | 0.7807 | 0.6626 | 0.7807 | 0.8836 |
| 0.6288 | 3.5472 | 564 | 0.7996 | 0.6677 | 0.7996 | 0.8942 |
| 0.6288 | 3.5597 | 566 | 0.8036 | 0.6626 | 0.8036 | 0.8964 |
| 0.6288 | 3.5723 | 568 | 0.8708 | 0.6388 | 0.8708 | 0.9332 |
| 0.6288 | 3.5849 | 570 | 0.8724 | 0.6836 | 0.8724 | 0.9340 |
| 0.6288 | 3.5975 | 572 | 0.8170 | 0.6388 | 0.8170 | 0.9039 |
| 0.6288 | 3.6101 | 574 | 0.7516 | 0.6388 | 0.7516 | 0.8669 |
| 0.6288 | 3.6226 | 576 | 0.6886 | 0.6797 | 0.6886 | 0.8298 |
| 0.6288 | 3.6352 | 578 | 0.6268 | 0.7601 | 0.6268 | 0.7917 |
| 0.6288 | 3.6478 | 580 | 0.6154 | 0.7689 | 0.6154 | 0.7844 |
| 0.6288 | 3.6604 | 582 | 0.6477 | 0.6797 | 0.6477 | 0.8048 |
| 0.6288 | 3.6730 | 584 | 0.6604 | 0.6797 | 0.6604 | 0.8127 |
| 0.6288 | 3.6855 | 586 | 0.6956 | 0.6860 | 0.6956 | 0.8340 |
| 0.6288 | 3.6981 | 588 | 0.7446 | 0.6860 | 0.7446 | 0.8629 |
| 0.6288 | 3.7107 | 590 | 0.7382 | 0.6435 | 0.7382 | 0.8592 |
| 0.6288 | 3.7233 | 592 | 0.7355 | 0.6388 | 0.7355 | 0.8576 |
| 0.6288 | 3.7358 | 594 | 0.7086 | 0.7110 | 0.7086 | 0.8418 |
| 0.6288 | 3.7484 | 596 | 0.6681 | 0.7672 | 0.6681 | 0.8174 |
| 0.6288 | 3.7610 | 598 | 0.6208 | 0.7518 | 0.6208 | 0.7879 |
| 0.6288 | 3.7736 | 600 | 0.6163 | 0.7601 | 0.6163 | 0.7851 |
| 0.6288 | 3.7862 | 602 | 0.6241 | 0.7601 | 0.6241 | 0.7900 |
| 0.6288 | 3.7987 | 604 | 0.6257 | 0.7518 | 0.6257 | 0.7910 |
| 0.6288 | 3.8113 | 606 | 0.6705 | 0.7518 | 0.6705 | 0.8188 |
| 0.6288 | 3.8239 | 608 | 0.7318 | 0.7182 | 0.7318 | 0.8555 |
| 0.6288 | 3.8365 | 610 | 0.7426 | 0.6616 | 0.7426 | 0.8617 |
| 0.6288 | 3.8491 | 612 | 0.6908 | 0.6569 | 0.6908 | 0.8311 |
| 0.6288 | 3.8616 | 614 | 0.6275 | 0.7277 | 0.6275 | 0.7922 |
| 0.6288 | 3.8742 | 616 | 0.6033 | 0.7123 | 0.6033 | 0.7767 |
| 0.6288 | 3.8868 | 618 | 0.6103 | 0.7123 | 0.6103 | 0.7812 |
| 0.6288 | 3.8994 | 620 | 0.6276 | 0.7123 | 0.6276 | 0.7922 |
| 0.6288 | 3.9119 | 622 | 0.6499 | 0.7123 | 0.6499 | 0.8062 |
| 0.6288 | 3.9245 | 624 | 0.6980 | 0.7518 | 0.6980 | 0.8355 |
| 0.6288 | 3.9371 | 626 | 0.7674 | 0.6578 | 0.7674 | 0.8760 |
| 0.6288 | 3.9497 | 628 | 0.8224 | 0.6667 | 0.8224 | 0.9069 |
| 0.6288 | 3.9623 | 630 | 0.8910 | 0.6667 | 0.8910 | 0.9439 |
| 0.6288 | 3.9748 | 632 | 0.9006 | 0.6667 | 0.9006 | 0.9490 |
| 0.6288 | 3.9874 | 634 | 0.8221 | 0.6253 | 0.8221 | 0.9067 |
| 0.6288 | 4.0 | 636 | 0.7197 | 0.6435 | 0.7197 | 0.8483 |
| 0.6288 | 4.0126 | 638 | 0.6759 | 0.6861 | 0.6759 | 0.8221 |
| 0.6288 | 4.0252 | 640 | 0.6705 | 0.7123 | 0.6705 | 0.8188 |
| 0.6288 | 4.0377 | 642 | 0.6935 | 0.7123 | 0.6935 | 0.8327 |
| 0.6288 | 4.0503 | 644 | 0.7261 | 0.7018 | 0.7261 | 0.8521 |
| 0.6288 | 4.0629 | 646 | 0.7572 | 0.6927 | 0.7572 | 0.8702 |
| 0.6288 | 4.0755 | 648 | 0.7616 | 0.7264 | 0.7616 | 0.8727 |
| 0.6288 | 4.0881 | 650 | 0.7805 | 0.6836 | 0.7805 | 0.8835 |
| 0.6288 | 4.1006 | 652 | 0.7327 | 0.6435 | 0.7327 | 0.8560 |
| 0.6288 | 4.1132 | 654 | 0.6639 | 0.7333 | 0.6639 | 0.8148 |
| 0.6288 | 4.1258 | 656 | 0.6382 | 0.7333 | 0.6381 | 0.7988 |
| 0.6288 | 4.1384 | 658 | 0.6315 | 0.6871 | 0.6315 | 0.7947 |
| 0.6288 | 4.1509 | 660 | 0.6361 | 0.6690 | 0.6361 | 0.7976 |
| 0.6288 | 4.1635 | 662 | 0.6552 | 0.6802 | 0.6552 | 0.8094 |
| 0.6288 | 4.1761 | 664 | 0.6872 | 0.6907 | 0.6872 | 0.8289 |
| 0.6288 | 4.1887 | 666 | 0.7048 | 0.6907 | 0.7048 | 0.8395 |
| 0.6288 | 4.2013 | 668 | 0.7155 | 0.6907 | 0.7155 | 0.8459 |
| 0.6288 | 4.2138 | 670 | 0.7047 | 0.6907 | 0.7047 | 0.8395 |
| 0.6288 | 4.2264 | 672 | 0.6781 | 0.7358 | 0.6781 | 0.8235 |
| 0.6288 | 4.2390 | 674 | 0.6566 | 0.7123 | 0.6566 | 0.8103 |
| 0.6288 | 4.2516 | 676 | 0.6497 | 0.7123 | 0.6497 | 0.8060 |
| 0.6288 | 4.2642 | 678 | 0.6436 | 0.7191 | 0.6436 | 0.8023 |
| 0.6288 | 4.2767 | 680 | 0.6358 | 0.7239 | 0.6358 | 0.7974 |
| 0.6288 | 4.2893 | 682 | 0.6499 | 0.7421 | 0.6499 | 0.8062 |
| 0.6288 | 4.3019 | 684 | 0.6811 | 0.6934 | 0.6811 | 0.8253 |
| 0.6288 | 4.3145 | 686 | 0.7239 | 0.6435 | 0.7239 | 0.8508 |
| 0.6288 | 4.3270 | 688 | 0.7227 | 0.6435 | 0.7227 | 0.8501 |
| 0.6288 | 4.3396 | 690 | 0.6981 | 0.6435 | 0.6981 | 0.8355 |
| 0.6288 | 4.3522 | 692 | 0.6639 | 0.7421 | 0.6639 | 0.8148 |
| 0.6288 | 4.3648 | 694 | 0.6371 | 0.7421 | 0.6371 | 0.7982 |
| 0.6288 | 4.3774 | 696 | 0.6324 | 0.7421 | 0.6324 | 0.7953 |
| 0.6288 | 4.3899 | 698 | 0.6347 | 0.7421 | 0.6347 | 0.7967 |
| 0.6288 | 4.4025 | 700 | 0.6294 | 0.7421 | 0.6294 | 0.7933 |
| 0.6288 | 4.4151 | 702 | 0.6333 | 0.7421 | 0.6333 | 0.7958 |
| 0.6288 | 4.4277 | 704 | 0.6499 | 0.7421 | 0.6499 | 0.8062 |
| 0.6288 | 4.4403 | 706 | 0.6700 | 0.6977 | 0.6700 | 0.8186 |
| 0.6288 | 4.4528 | 708 | 0.6968 | 0.6846 | 0.6968 | 0.8347 |
| 0.6288 | 4.4654 | 710 | 0.7038 | 0.7110 | 0.7038 | 0.8389 |
| 0.6288 | 4.4780 | 712 | 0.7072 | 0.7110 | 0.7072 | 0.8410 |
| 0.6288 | 4.4906 | 714 | 0.6798 | 0.7759 | 0.6798 | 0.8245 |
| 0.6288 | 4.5031 | 716 | 0.6715 | 0.7759 | 0.6715 | 0.8194 |
| 0.6288 | 4.5157 | 718 | 0.6567 | 0.7759 | 0.6567 | 0.8104 |
| 0.6288 | 4.5283 | 720 | 0.6328 | 0.7759 | 0.6328 | 0.7955 |
| 0.6288 | 4.5409 | 722 | 0.6202 | 0.7522 | 0.6202 | 0.7875 |
| 0.6288 | 4.5535 | 724 | 0.6198 | 0.7028 | 0.6198 | 0.7873 |
| 0.6288 | 4.5660 | 726 | 0.6208 | 0.7028 | 0.6208 | 0.7879 |
| 0.6288 | 4.5786 | 728 | 0.6104 | 0.7028 | 0.6104 | 0.7813 |
| 0.6288 | 4.5912 | 730 | 0.6012 | 0.7759 | 0.6012 | 0.7754 |
| 0.6288 | 4.6038 | 732 | 0.6225 | 0.7518 | 0.6225 | 0.7890 |
| 0.6288 | 4.6164 | 734 | 0.6668 | 0.7259 | 0.6668 | 0.8166 |
| 0.6288 | 4.6289 | 736 | 0.7187 | 0.6846 | 0.7187 | 0.8477 |
| 0.6288 | 4.6415 | 738 | 0.7545 | 0.7364 | 0.7545 | 0.8686 |
| 0.6288 | 4.6541 | 740 | 0.7367 | 0.7660 | 0.7367 | 0.8583 |
| 0.6288 | 4.6667 | 742 | 0.7491 | 0.7361 | 0.7491 | 0.8655 |
| 0.6288 | 4.6792 | 744 | 0.7490 | 0.7361 | 0.7490 | 0.8654 |
| 0.6288 | 4.6918 | 746 | 0.7467 | 0.7288 | 0.7467 | 0.8641 |
| 0.6288 | 4.7044 | 748 | 0.7240 | 0.7288 | 0.7240 | 0.8509 |
| 0.6288 | 4.7170 | 750 | 0.7205 | 0.7110 | 0.7205 | 0.8488 |
| 0.6288 | 4.7296 | 752 | 0.7337 | 0.6907 | 0.7337 | 0.8566 |
| 0.6288 | 4.7421 | 754 | 0.6936 | 0.7333 | 0.6936 | 0.8328 |
| 0.6288 | 4.7547 | 756 | 0.6420 | 0.7333 | 0.6420 | 0.8013 |
| 0.6288 | 4.7673 | 758 | 0.6149 | 0.7433 | 0.6149 | 0.7842 |
| 0.6288 | 4.7799 | 760 | 0.6316 | 0.6861 | 0.6316 | 0.7947 |
| 0.6288 | 4.7925 | 762 | 0.6421 | 0.7123 | 0.6421 | 0.8013 |
| 0.6288 | 4.8050 | 764 | 0.6468 | 0.7123 | 0.6468 | 0.8043 |
| 0.6288 | 4.8176 | 766 | 0.6403 | 0.6861 | 0.6403 | 0.8002 |
| 0.6288 | 4.8302 | 768 | 0.6549 | 0.7277 | 0.6549 | 0.8093 |
| 0.6288 | 4.8428 | 770 | 0.6688 | 0.7277 | 0.6688 | 0.8178 |
| 0.6288 | 4.8553 | 772 | 0.6752 | 0.6799 | 0.6752 | 0.8217 |
| 0.6288 | 4.8679 | 774 | 0.6908 | 0.6799 | 0.6908 | 0.8312 |
| 0.6288 | 4.8805 | 776 | 0.6981 | 0.7055 | 0.6981 | 0.8355 |
| 0.6288 | 4.8931 | 778 | 0.7123 | 0.7055 | 0.7123 | 0.8440 |
| 0.6288 | 4.9057 | 780 | 0.7314 | 0.7047 | 0.7314 | 0.8552 |
| 0.6288 | 4.9182 | 782 | 0.7324 | 0.7110 | 0.7324 | 0.8558 |
| 0.6288 | 4.9308 | 784 | 0.7261 | 0.7514 | 0.7261 | 0.8521 |
| 0.6288 | 4.9434 | 786 | 0.7221 | 0.7514 | 0.7221 | 0.8498 |
| 0.6288 | 4.9560 | 788 | 0.7017 | 0.6856 | 0.7017 | 0.8377 |
| 0.6288 | 4.9686 | 790 | 0.6998 | 0.6856 | 0.6998 | 0.8365 |
| 0.6288 | 4.9811 | 792 | 0.6887 | 0.6856 | 0.6887 | 0.8299 |
| 0.6288 | 4.9937 | 794 | 0.6998 | 0.6982 | 0.6998 | 0.8365 |
| 0.6288 | 5.0063 | 796 | 0.7169 | 0.6678 | 0.7169 | 0.8467 |
| 0.6288 | 5.0189 | 798 | 0.7236 | 0.6678 | 0.7236 | 0.8506 |
| 0.6288 | 5.0314 | 800 | 0.7162 | 0.7157 | 0.7162 | 0.8463 |
| 0.6288 | 5.0440 | 802 | 0.7141 | 0.6627 | 0.7141 | 0.8450 |
| 0.6288 | 5.0566 | 804 | 0.7255 | 0.7157 | 0.7255 | 0.8517 |
| 0.6288 | 5.0692 | 806 | 0.7350 | 0.7157 | 0.7350 | 0.8573 |
| 0.6288 | 5.0818 | 808 | 0.7622 | 0.6388 | 0.7622 | 0.8731 |
| 0.6288 | 5.0943 | 810 | 0.8137 | 0.6435 | 0.8137 | 0.9020 |
| 0.6288 | 5.1069 | 812 | 0.8266 | 0.6435 | 0.8266 | 0.9092 |
| 0.6288 | 5.1195 | 814 | 0.7980 | 0.6435 | 0.7980 | 0.8933 |
| 0.6288 | 5.1321 | 816 | 0.7493 | 0.6435 | 0.7493 | 0.8656 |
| 0.6288 | 5.1447 | 818 | 0.7039 | 0.6797 | 0.7039 | 0.8390 |
| 0.6288 | 5.1572 | 820 | 0.6793 | 0.6797 | 0.6793 | 0.8242 |
| 0.6288 | 5.1698 | 822 | 0.6889 | 0.6860 | 0.6889 | 0.8300 |
| 0.6288 | 5.1824 | 824 | 0.7184 | 0.6545 | 0.7184 | 0.8476 |
| 0.6288 | 5.1950 | 826 | 0.7302 | 0.6545 | 0.7302 | 0.8545 |
| 0.6288 | 5.2075 | 828 | 0.7194 | 0.6545 | 0.7194 | 0.8482 |
| 0.6288 | 5.2201 | 830 | 0.7024 | 0.6860 | 0.7024 | 0.8381 |
| 0.6288 | 5.2327 | 832 | 0.6868 | 0.7417 | 0.6868 | 0.8287 |
| 0.6288 | 5.2453 | 834 | 0.6726 | 0.7672 | 0.6726 | 0.8201 |
| 0.6288 | 5.2579 | 836 | 0.6719 | 0.7055 | 0.6719 | 0.8197 |
| 0.6288 | 5.2704 | 838 | 0.6799 | 0.7123 | 0.6799 | 0.8246 |
| 0.6288 | 5.2830 | 840 | 0.6897 | 0.7055 | 0.6897 | 0.8305 |
| 0.6288 | 5.2956 | 842 | 0.7185 | 0.6839 | 0.7185 | 0.8477 |
| 0.6288 | 5.3082 | 844 | 0.7458 | 0.7288 | 0.7458 | 0.8636 |
| 0.6288 | 5.3208 | 846 | 0.7508 | 0.7288 | 0.7508 | 0.8665 |
| 0.6288 | 5.3333 | 848 | 0.7270 | 0.6839 | 0.7270 | 0.8526 |
| 0.6288 | 5.3459 | 850 | 0.7239 | 0.7047 | 0.7239 | 0.8508 |
| 0.6288 | 5.3585 | 852 | 0.7517 | 0.6732 | 0.7517 | 0.8670 |
| 0.6288 | 5.3711 | 854 | 0.7623 | 0.6732 | 0.7623 | 0.8731 |
| 0.6288 | 5.3836 | 856 | 0.7468 | 0.6677 | 0.7468 | 0.8642 |
| 0.6288 | 5.3962 | 858 | 0.7137 | 0.7514 | 0.7137 | 0.8448 |
| 0.6288 | 5.4088 | 860 | 0.6918 | 0.6957 | 0.6918 | 0.8317 |
| 0.6288 | 5.4214 | 862 | 0.6693 | 0.7055 | 0.6693 | 0.8181 |
| 0.6288 | 5.4340 | 864 | 0.6639 | 0.7055 | 0.6639 | 0.8148 |
| 0.6288 | 5.4465 | 866 | 0.6736 | 0.7208 | 0.6736 | 0.8207 |
| 0.6288 | 5.4591 | 868 | 0.7131 | 0.7514 | 0.7131 | 0.8444 |
| 0.6288 | 5.4717 | 870 | 0.7366 | 0.7514 | 0.7366 | 0.8583 |
| 0.6288 | 5.4843 | 872 | 0.7783 | 0.6388 | 0.7783 | 0.8822 |
| 0.6288 | 5.4969 | 874 | 0.8566 | 0.6667 | 0.8566 | 0.9255 |
| 0.6288 | 5.5094 | 876 | 0.9017 | 0.6667 | 0.9017 | 0.9496 |
| 0.6288 | 5.5220 | 878 | 0.8844 | 0.6253 | 0.8844 | 0.9404 |
| 0.6288 | 5.5346 | 880 | 0.8689 | 0.6253 | 0.8689 | 0.9322 |
| 0.6288 | 5.5472 | 882 | 0.8775 | 0.6435 | 0.8775 | 0.9367 |
| 0.6288 | 5.5597 | 884 | 0.8701 | 0.6435 | 0.8701 | 0.9328 |
| 0.6288 | 5.5723 | 886 | 0.8562 | 0.6435 | 0.8562 | 0.9253 |
| 0.6288 | 5.5849 | 888 | 0.8308 | 0.6435 | 0.8308 | 0.9115 |
| 0.6288 | 5.5975 | 890 | 0.7987 | 0.6972 | 0.7987 | 0.8937 |
| 0.6288 | 5.6101 | 892 | 0.7729 | 0.6972 | 0.7729 | 0.8792 |
| 0.6288 | 5.6226 | 894 | 0.7418 | 0.7333 | 0.7418 | 0.8613 |
| 0.6288 | 5.6352 | 896 | 0.7111 | 0.7333 | 0.7111 | 0.8433 |
| 0.6288 | 5.6478 | 898 | 0.6895 | 0.6802 | 0.6895 | 0.8304 |
| 0.6288 | 5.6604 | 900 | 0.6866 | 0.6802 | 0.6866 | 0.8286 |
| 0.6288 | 5.6730 | 902 | 0.6994 | 0.7417 | 0.6994 | 0.8363 |
| 0.6288 | 5.6855 | 904 | 0.7161 | 0.7123 | 0.7161 | 0.8462 |
| 0.6288 | 5.6981 | 906 | 0.7150 | 0.7123 | 0.7150 | 0.8456 |
| 0.6288 | 5.7107 | 908 | 0.7329 | 0.7123 | 0.7329 | 0.8561 |
| 0.6288 | 5.7233 | 910 | 0.7971 | 0.6115 | 0.7971 | 0.8928 |
| 0.6288 | 5.7358 | 912 | 0.8371 | 0.6115 | 0.8371 | 0.9149 |
| 0.6288 | 5.7484 | 914 | 0.8687 | 0.6552 | 0.8687 | 0.9320 |
| 0.6288 | 5.7610 | 916 | 0.8506 | 0.6836 | 0.8506 | 0.9223 |
| 0.6288 | 5.7736 | 918 | 0.8227 | 0.7338 | 0.8227 | 0.9071 |
| 0.6288 | 5.7862 | 920 | 0.7934 | 0.7354 | 0.7934 | 0.8907 |
| 0.6288 | 5.7987 | 922 | 0.7630 | 0.7668 | 0.7630 | 0.8735 |
| 0.6288 | 5.8113 | 924 | 0.7640 | 0.7668 | 0.7640 | 0.8740 |
| 0.6288 | 5.8239 | 926 | 0.7647 | 0.7273 | 0.7647 | 0.8745 |
| 0.6288 | 5.8365 | 928 | 0.7895 | 0.7427 | 0.7895 | 0.8885 |
| 0.6288 | 5.8491 | 930 | 0.8409 | 0.7338 | 0.8409 | 0.9170 |
| 0.6288 | 5.8616 | 932 | 0.8659 | 0.7071 | 0.8659 | 0.9305 |
| 0.6288 | 5.8742 | 934 | 0.8355 | 0.7071 | 0.8355 | 0.9141 |
| 0.6288 | 5.8868 | 936 | 0.7752 | 0.6441 | 0.7752 | 0.8805 |
| 0.6288 | 5.8994 | 938 | 0.7299 | 0.6684 | 0.7299 | 0.8543 |
| 0.6288 | 5.9119 | 940 | 0.6925 | 0.6917 | 0.6925 | 0.8322 |
| 0.6288 | 5.9245 | 942 | 0.6830 | 0.6917 | 0.6830 | 0.8264 |
| 0.6288 | 5.9371 | 944 | 0.6845 | 0.6799 | 0.6845 | 0.8274 |
| 0.6288 | 5.9497 | 946 | 0.6958 | 0.6799 | 0.6958 | 0.8342 |
| 0.6288 | 5.9623 | 948 | 0.7225 | 0.6799 | 0.7225 | 0.8500 |
| 0.6288 | 5.9748 | 950 | 0.7489 | 0.7055 | 0.7489 | 0.8654 |
| 0.6288 | 5.9874 | 952 | 0.7599 | 0.7055 | 0.7599 | 0.8717 |
| 0.6288 | 6.0 | 954 | 0.7579 | 0.6799 | 0.7579 | 0.8706 |
| 0.6288 | 6.0126 | 956 | 0.7294 | 0.6799 | 0.7294 | 0.8541 |
| 0.6288 | 6.0252 | 958 | 0.7017 | 0.7055 | 0.7017 | 0.8377 |
| 0.6288 | 6.0377 | 960 | 0.6709 | 0.7055 | 0.6709 | 0.8191 |
| 0.6288 | 6.0503 | 962 | 0.6521 | 0.7055 | 0.6521 | 0.8075 |
| 0.6288 | 6.0629 | 964 | 0.6302 | 0.7055 | 0.6302 | 0.7938 |
| 0.6288 | 6.0755 | 966 | 0.6304 | 0.6799 | 0.6304 | 0.7940 |
| 0.6288 | 6.0881 | 968 | 0.6440 | 0.7277 | 0.6440 | 0.8025 |
| 0.6288 | 6.1006 | 970 | 0.6645 | 0.7598 | 0.6645 | 0.8151 |
| 0.6288 | 6.1132 | 972 | 0.7068 | 0.7333 | 0.7068 | 0.8407 |
| 0.6288 | 6.1258 | 974 | 0.7367 | 0.6860 | 0.7367 | 0.8583 |
| 0.6288 | 6.1384 | 976 | 0.7656 | 0.6860 | 0.7656 | 0.8750 |
| 0.6288 | 6.1509 | 978 | 0.7412 | 0.6860 | 0.7412 | 0.8609 |
| 0.6288 | 6.1635 | 980 | 0.6994 | 0.6860 | 0.6994 | 0.8363 |
| 0.6288 | 6.1761 | 982 | 0.6751 | 0.6797 | 0.6751 | 0.8216 |
| 0.6288 | 6.1887 | 984 | 0.6555 | 0.7333 | 0.6555 | 0.8096 |
| 0.6288 | 6.2013 | 986 | 0.6581 | 0.7514 | 0.6581 | 0.8112 |
| 0.6288 | 6.2138 | 988 | 0.6845 | 0.7514 | 0.6845 | 0.8274 |
| 0.6288 | 6.2264 | 990 | 0.7000 | 0.7756 | 0.7000 | 0.8366 |
| 0.6288 | 6.2390 | 992 | 0.7232 | 0.7514 | 0.7232 | 0.8504 |
| 0.6288 | 6.2516 | 994 | 0.7450 | 0.6846 | 0.7450 | 0.8632 |
| 0.6288 | 6.2642 | 996 | 0.7504 | 0.7196 | 0.7504 | 0.8662 |
| 0.6288 | 6.2767 | 998 | 0.7522 | 0.7822 | 0.7522 | 0.8673 |
| 0.1504 | 6.2893 | 1000 | 0.7915 | 0.7212 | 0.7915 | 0.8897 |
| 0.1504 | 6.3019 | 1002 | 0.8006 | 0.7503 | 0.8006 | 0.8948 |
| 0.1504 | 6.3145 | 1004 | 0.7870 | 0.7277 | 0.7870 | 0.8871 |
| 0.1504 | 6.3270 | 1006 | 0.7535 | 0.7590 | 0.7535 | 0.8681 |
| 0.1504 | 6.3396 | 1008 | 0.7155 | 0.7514 | 0.7155 | 0.8459 |
| 0.1504 | 6.3522 | 1010 | 0.6945 | 0.7514 | 0.6945 | 0.8334 |
| 0.1504 | 6.3648 | 1012 | 0.6849 | 0.7672 | 0.6849 | 0.8276 |
| 0.1504 | 6.3774 | 1014 | 0.6697 | 0.7672 | 0.6697 | 0.8184 |
| 0.1504 | 6.3899 | 1016 | 0.6687 | 0.7672 | 0.6687 | 0.8177 |
| 0.1504 | 6.4025 | 1018 | 0.6672 | 0.7672 | 0.6672 | 0.8168 |
| 0.1504 | 6.4151 | 1020 | 0.6523 | 0.7672 | 0.6523 | 0.8077 |
| 0.1504 | 6.4277 | 1022 | 0.6457 | 0.7672 | 0.6457 | 0.8036 |
| 0.1504 | 6.4403 | 1024 | 0.6442 | 0.7277 | 0.6442 | 0.8026 |
| 0.1504 | 6.4528 | 1026 | 0.6454 | 0.7350 | 0.6454 | 0.8033 |
| 0.1504 | 6.4654 | 1028 | 0.6591 | 0.7255 | 0.6591 | 0.8118 |
| 0.1504 | 6.4780 | 1030 | 0.6788 | 0.7333 | 0.6788 | 0.8239 |
| 0.1504 | 6.4906 | 1032 | 0.6964 | 0.7333 | 0.6964 | 0.8345 |
| 0.1504 | 6.5031 | 1034 | 0.7205 | 0.7417 | 0.7205 | 0.8488 |
| 0.1504 | 6.5157 | 1036 | 0.7354 | 0.6860 | 0.7354 | 0.8575 |
| 0.1504 | 6.5283 | 1038 | 0.7511 | 0.6860 | 0.7511 | 0.8667 |
| 0.1504 | 6.5409 | 1040 | 0.7641 | 0.6860 | 0.7641 | 0.8741 |
| 0.1504 | 6.5535 | 1042 | 0.7636 | 0.6860 | 0.7636 | 0.8738 |
| 0.1504 | 6.5660 | 1044 | 0.7476 | 0.6860 | 0.7476 | 0.8646 |
| 0.1504 | 6.5786 | 1046 | 0.7438 | 0.6860 | 0.7438 | 0.8624 |
| 0.1504 | 6.5912 | 1048 | 0.7723 | 0.6860 | 0.7723 | 0.8788 |
| 0.1504 | 6.6038 | 1050 | 0.8261 | 0.6435 | 0.8261 | 0.9089 |
| 0.1504 | 6.6164 | 1052 | 0.8382 | 0.6836 | 0.8382 | 0.9155 |
| 0.1504 | 6.6289 | 1054 | 0.8379 | 0.6836 | 0.8379 | 0.9154 |
| 0.1504 | 6.6415 | 1056 | 0.8392 | 0.6836 | 0.8392 | 0.9161 |
| 0.1504 | 6.6541 | 1058 | 0.8312 | 0.6836 | 0.8312 | 0.9117 |
| 0.1504 | 6.6667 | 1060 | 0.8061 | 0.6779 | 0.8061 | 0.8978 |
| 0.1504 | 6.6792 | 1062 | 0.8068 | 0.6779 | 0.8068 | 0.8982 |
| 0.1504 | 6.6918 | 1064 | 0.7942 | 0.6388 | 0.7942 | 0.8912 |
| 0.1504 | 6.7044 | 1066 | 0.8008 | 0.6435 | 0.8008 | 0.8949 |
| 0.1504 | 6.7170 | 1068 | 0.8168 | 0.6435 | 0.8168 | 0.9038 |
| 0.1504 | 6.7296 | 1070 | 0.8289 | 0.6115 | 0.8289 | 0.9105 |
| 0.1504 | 6.7421 | 1072 | 0.8311 | 0.5926 | 0.8311 | 0.9117 |
| 0.1504 | 6.7547 | 1074 | 0.7952 | 0.6349 | 0.7952 | 0.8918 |
| 0.1504 | 6.7673 | 1076 | 0.7387 | 0.6545 | 0.7387 | 0.8595 |
| 0.1504 | 6.7799 | 1078 | 0.6763 | 0.7417 | 0.6763 | 0.8224 |
| 0.1504 | 6.7925 | 1080 | 0.6370 | 0.7157 | 0.6370 | 0.7981 |
| 0.1504 | 6.8050 | 1082 | 0.6272 | 0.7157 | 0.6272 | 0.7920 |
| 0.1504 | 6.8176 | 1084 | 0.6278 | 0.7157 | 0.6278 | 0.7923 |
| 0.1504 | 6.8302 | 1086 | 0.6367 | 0.7157 | 0.6367 | 0.7979 |
| 0.1504 | 6.8428 | 1088 | 0.6639 | 0.7157 | 0.6639 | 0.8148 |
| 0.1504 | 6.8553 | 1090 | 0.7058 | 0.7333 | 0.7058 | 0.8401 |
| 0.1504 | 6.8679 | 1092 | 0.7679 | 0.6435 | 0.7679 | 0.8763 |
| 0.1504 | 6.8805 | 1094 | 0.7954 | 0.6115 | 0.7954 | 0.8918 |
| 0.1504 | 6.8931 | 1096 | 0.7897 | 0.6435 | 0.7897 | 0.8887 |
| 0.1504 | 6.9057 | 1098 | 0.7516 | 0.7417 | 0.7516 | 0.8670 |
| 0.1504 | 6.9182 | 1100 | 0.7115 | 0.7333 | 0.7115 | 0.8435 |
| 0.1504 | 6.9308 | 1102 | 0.6793 | 0.7157 | 0.6793 | 0.8242 |
| 0.1504 | 6.9434 | 1104 | 0.6603 | 0.7157 | 0.6603 | 0.8126 |
| 0.1504 | 6.9560 | 1106 | 0.6613 | 0.7157 | 0.6613 | 0.8132 |
| 0.1504 | 6.9686 | 1108 | 0.6710 | 0.7157 | 0.6710 | 0.8192 |
| 0.1504 | 6.9811 | 1110 | 0.6982 | 0.7417 | 0.6982 | 0.8356 |
| 0.1504 | 6.9937 | 1112 | 0.7345 | 0.7123 | 0.7345 | 0.8570 |
| 0.1504 | 7.0063 | 1114 | 0.7443 | 0.7123 | 0.7443 | 0.8627 |
| 0.1504 | 7.0189 | 1116 | 0.7267 | 0.7123 | 0.7267 | 0.8525 |
| 0.1504 | 7.0314 | 1118 | 0.6933 | 0.6934 | 0.6933 | 0.8327 |
| 0.1504 | 7.0440 | 1120 | 0.6825 | 0.7234 | 0.6825 | 0.8261 |
| 0.1504 | 7.0566 | 1122 | 0.6861 | 0.7234 | 0.6861 | 0.8283 |
| 0.1504 | 7.0692 | 1124 | 0.6918 | 0.7234 | 0.6918 | 0.8317 |
| 0.1504 | 7.0818 | 1126 | 0.7018 | 0.7417 | 0.7018 | 0.8377 |
| 0.1504 | 7.0943 | 1128 | 0.7061 | 0.7234 | 0.7061 | 0.8403 |
| 0.1504 | 7.1069 | 1130 | 0.7182 | 0.7417 | 0.7182 | 0.8474 |
| 0.1504 | 7.1195 | 1132 | 0.7299 | 0.7417 | 0.7299 | 0.8544 |
| 0.1504 | 7.1321 | 1134 | 0.7433 | 0.7417 | 0.7433 | 0.8621 |
| 0.1504 | 7.1447 | 1136 | 0.7485 | 0.7086 | 0.7485 | 0.8652 |
| 0.1504 | 7.1572 | 1138 | 0.7281 | 0.7086 | 0.7281 | 0.8533 |
| 0.1504 | 7.1698 | 1140 | 0.6956 | 0.6574 | 0.6956 | 0.8340 |
| 0.1504 | 7.1824 | 1142 | 0.6815 | 0.6574 | 0.6815 | 0.8255 |
| 0.1504 | 7.1950 | 1144 | 0.6834 | 0.6574 | 0.6834 | 0.8267 |
| 0.1504 | 7.2075 | 1146 | 0.6973 | 0.6574 | 0.6973 | 0.8350 |
| 0.1504 | 7.2201 | 1148 | 0.7210 | 0.6866 | 0.7210 | 0.8491 |
| 0.1504 | 7.2327 | 1150 | 0.7463 | 0.7123 | 0.7463 | 0.8639 |
| 0.1504 | 7.2453 | 1152 | 0.7705 | 0.7123 | 0.7705 | 0.8778 |
| 0.1504 | 7.2579 | 1154 | 0.8044 | 0.7097 | 0.8044 | 0.8969 |
| 0.1504 | 7.2704 | 1156 | 0.8314 | 0.7097 | 0.8314 | 0.9118 |
| 0.1504 | 7.2830 | 1158 | 0.8228 | 0.7071 | 0.8228 | 0.9071 |
| 0.1504 | 7.2956 | 1160 | 0.7994 | 0.6673 | 0.7994 | 0.8941 |
| 0.1504 | 7.3082 | 1162 | 0.7654 | 0.7123 | 0.7654 | 0.8749 |
| 0.1504 | 7.3208 | 1164 | 0.7251 | 0.6866 | 0.7251 | 0.8516 |
| 0.1504 | 7.3333 | 1166 | 0.6987 | 0.6866 | 0.6987 | 0.8359 |
| 0.1504 | 7.3459 | 1168 | 0.6769 | 0.7086 | 0.6769 | 0.8228 |
| 0.1504 | 7.3585 | 1170 | 0.6704 | 0.6633 | 0.6704 | 0.8188 |
| 0.1504 | 7.3711 | 1172 | 0.6683 | 0.6633 | 0.6683 | 0.8175 |
| 0.1504 | 7.3836 | 1174 | 0.6813 | 0.6574 | 0.6813 | 0.8254 |
| 0.1504 | 7.3962 | 1176 | 0.7029 | 0.7086 | 0.7029 | 0.8384 |
| 0.1504 | 7.4088 | 1178 | 0.7420 | 0.7086 | 0.7420 | 0.8614 |
| 0.1504 | 7.4214 | 1180 | 0.7929 | 0.7264 | 0.7929 | 0.8904 |
| 0.1504 | 7.4340 | 1182 | 0.8535 | 0.7097 | 0.8535 | 0.9238 |
| 0.1504 | 7.4465 | 1184 | 0.9138 | 0.6446 | 0.9138 | 0.9559 |
| 0.1504 | 7.4591 | 1186 | 0.9319 | 0.6376 | 0.9319 | 0.9653 |
| 0.1504 | 7.4717 | 1188 | 0.9051 | 0.6376 | 0.9051 | 0.9514 |
| 0.1504 | 7.4843 | 1190 | 0.8503 | 0.6897 | 0.8503 | 0.9221 |
| 0.1504 | 7.4969 | 1192 | 0.7908 | 0.7005 | 0.7908 | 0.8893 |
| 0.1504 | 7.5094 | 1194 | 0.7458 | 0.6866 | 0.7458 | 0.8636 |
| 0.1504 | 7.5220 | 1196 | 0.7313 | 0.6866 | 0.7313 | 0.8552 |
| 0.1504 | 7.5346 | 1198 | 0.7330 | 0.6866 | 0.7330 | 0.8561 |
| 0.1504 | 7.5472 | 1200 | 0.7418 | 0.6866 | 0.7418 | 0.8613 |
| 0.1504 | 7.5597 | 1202 | 0.7555 | 0.7047 | 0.7555 | 0.8692 |
| 0.1504 | 7.5723 | 1204 | 0.7675 | 0.7123 | 0.7675 | 0.8761 |
| 0.1504 | 7.5849 | 1206 | 0.7687 | 0.6673 | 0.7687 | 0.8768 |
| 0.1504 | 7.5975 | 1208 | 0.7840 | 0.6673 | 0.7840 | 0.8854 |
| 0.1504 | 7.6101 | 1210 | 0.8036 | 0.6673 | 0.8036 | 0.8965 |
| 0.1504 | 7.6226 | 1212 | 0.8009 | 0.6485 | 0.8009 | 0.8949 |
| 0.1504 | 7.6352 | 1214 | 0.7773 | 0.6929 | 0.7773 | 0.8816 |
| 0.1504 | 7.6478 | 1216 | 0.7516 | 0.7123 | 0.7516 | 0.8669 |
| 0.1504 | 7.6604 | 1218 | 0.7281 | 0.7123 | 0.7281 | 0.8533 |
| 0.1504 | 7.6730 | 1220 | 0.7152 | 0.7123 | 0.7152 | 0.8457 |
| 0.1504 | 7.6855 | 1222 | 0.7211 | 0.7123 | 0.7211 | 0.8492 |
| 0.1504 | 7.6981 | 1224 | 0.7173 | 0.7047 | 0.7173 | 0.8469 |
| 0.1504 | 7.7107 | 1226 | 0.7204 | 0.6866 | 0.7204 | 0.8488 |
| 0.1504 | 7.7233 | 1228 | 0.7341 | 0.7047 | 0.7341 | 0.8568 |
| 0.1504 | 7.7358 | 1230 | 0.7334 | 0.7047 | 0.7334 | 0.8564 |
| 0.1504 | 7.7484 | 1232 | 0.7351 | 0.7047 | 0.7351 | 0.8574 |
| 0.1504 | 7.7610 | 1234 | 0.7413 | 0.7123 | 0.7413 | 0.8610 |
| 0.1504 | 7.7736 | 1236 | 0.7610 | 0.7123 | 0.7610 | 0.8723 |
| 0.1504 | 7.7862 | 1238 | 0.7843 | 0.7123 | 0.7843 | 0.8856 |
| 0.1504 | 7.7987 | 1240 | 0.7954 | 0.6673 | 0.7954 | 0.8918 |
| 0.1504 | 7.8113 | 1242 | 0.7920 | 0.6673 | 0.7920 | 0.8900 |
| 0.1504 | 7.8239 | 1244 | 0.7760 | 0.7123 | 0.7760 | 0.8809 |
| 0.1504 | 7.8365 | 1246 | 0.7541 | 0.7123 | 0.7541 | 0.8684 |
| 0.1504 | 7.8491 | 1248 | 0.7427 | 0.7047 | 0.7427 | 0.8618 |
| 0.1504 | 7.8616 | 1250 | 0.7333 | 0.7047 | 0.7333 | 0.8563 |
| 0.1504 | 7.8742 | 1252 | 0.7250 | 0.7047 | 0.7250 | 0.8515 |
| 0.1504 | 7.8868 | 1254 | 0.7211 | 0.7333 | 0.7211 | 0.8492 |
| 0.1504 | 7.8994 | 1256 | 0.7212 | 0.7333 | 0.7212 | 0.8492 |
| 0.1504 | 7.9119 | 1258 | 0.7164 | 0.7333 | 0.7164 | 0.8464 |
| 0.1504 | 7.9245 | 1260 | 0.7191 | 0.7333 | 0.7191 | 0.8480 |
| 0.1504 | 7.9371 | 1262 | 0.7256 | 0.7333 | 0.7256 | 0.8518 |
| 0.1504 | 7.9497 | 1264 | 0.7387 | 0.7333 | 0.7387 | 0.8595 |
| 0.1504 | 7.9623 | 1266 | 0.7541 | 0.6972 | 0.7541 | 0.8684 |
| 0.1504 | 7.9748 | 1268 | 0.7677 | 0.6972 | 0.7677 | 0.8762 |
| 0.1504 | 7.9874 | 1270 | 0.7705 | 0.6972 | 0.7705 | 0.8778 |
| 0.1504 | 8.0 | 1272 | 0.7680 | 0.6972 | 0.7680 | 0.8763 |
| 0.1504 | 8.0126 | 1274 | 0.7580 | 0.6972 | 0.7580 | 0.8706 |
| 0.1504 | 8.0252 | 1276 | 0.7470 | 0.7417 | 0.7470 | 0.8643 |
| 0.1504 | 8.0377 | 1278 | 0.7354 | 0.7333 | 0.7354 | 0.8576 |
| 0.1504 | 8.0503 | 1280 | 0.7139 | 0.7333 | 0.7139 | 0.8449 |
| 0.1504 | 8.0629 | 1282 | 0.7023 | 0.7086 | 0.7023 | 0.8380 |
| 0.1504 | 8.0755 | 1284 | 0.6943 | 0.7086 | 0.6943 | 0.8333 |
| 0.1504 | 8.0881 | 1286 | 0.6926 | 0.7086 | 0.6926 | 0.8322 |
| 0.1504 | 8.1006 | 1288 | 0.6967 | 0.7255 | 0.6967 | 0.8347 |
| 0.1504 | 8.1132 | 1290 | 0.7032 | 0.7255 | 0.7032 | 0.8385 |
| 0.1504 | 8.1258 | 1292 | 0.7033 | 0.7255 | 0.7033 | 0.8386 |
| 0.1504 | 8.1384 | 1294 | 0.7030 | 0.7333 | 0.7030 | 0.8384 |
| 0.1504 | 8.1509 | 1296 | 0.6982 | 0.7333 | 0.6982 | 0.8356 |
| 0.1504 | 8.1635 | 1298 | 0.6991 | 0.7255 | 0.6991 | 0.8361 |
| 0.1504 | 8.1761 | 1300 | 0.7043 | 0.7333 | 0.7043 | 0.8392 |
| 0.1504 | 8.1887 | 1302 | 0.7124 | 0.7333 | 0.7124 | 0.8441 |
| 0.1504 | 8.2013 | 1304 | 0.7284 | 0.7417 | 0.7284 | 0.8535 |
| 0.1504 | 8.2138 | 1306 | 0.7357 | 0.7417 | 0.7357 | 0.8577 |
| 0.1504 | 8.2264 | 1308 | 0.7456 | 0.7417 | 0.7456 | 0.8635 |
| 0.1504 | 8.2390 | 1310 | 0.7440 | 0.7417 | 0.7440 | 0.8626 |
| 0.1504 | 8.2516 | 1312 | 0.7254 | 0.7417 | 0.7254 | 0.8517 |
| 0.1504 | 8.2642 | 1314 | 0.7118 | 0.7417 | 0.7118 | 0.8437 |
| 0.1504 | 8.2767 | 1316 | 0.6918 | 0.7333 | 0.6918 | 0.8317 |
| 0.1504 | 8.2893 | 1318 | 0.6813 | 0.7333 | 0.6813 | 0.8254 |
| 0.1504 | 8.3019 | 1320 | 0.6746 | 0.7255 | 0.6746 | 0.8214 |
| 0.1504 | 8.3145 | 1322 | 0.6747 | 0.7255 | 0.6747 | 0.8214 |
| 0.1504 | 8.3270 | 1324 | 0.6731 | 0.7255 | 0.6731 | 0.8204 |
| 0.1504 | 8.3396 | 1326 | 0.6704 | 0.7255 | 0.6704 | 0.8188 |
| 0.1504 | 8.3522 | 1328 | 0.6695 | 0.7255 | 0.6695 | 0.8182 |
| 0.1504 | 8.3648 | 1330 | 0.6746 | 0.7333 | 0.6746 | 0.8214 |
| 0.1504 | 8.3774 | 1332 | 0.6857 | 0.7333 | 0.6857 | 0.8281 |
| 0.1504 | 8.3899 | 1334 | 0.6946 | 0.7333 | 0.6946 | 0.8334 |
| 0.1504 | 8.4025 | 1336 | 0.6925 | 0.7333 | 0.6925 | 0.8322 |
| 0.1504 | 8.4151 | 1338 | 0.6884 | 0.7333 | 0.6884 | 0.8297 |
| 0.1504 | 8.4277 | 1340 | 0.6837 | 0.7333 | 0.6837 | 0.8268 |
| 0.1504 | 8.4403 | 1342 | 0.6804 | 0.7333 | 0.6804 | 0.8249 |
| 0.1504 | 8.4528 | 1344 | 0.6804 | 0.7333 | 0.6804 | 0.8248 |
| 0.1504 | 8.4654 | 1346 | 0.6791 | 0.7333 | 0.6791 | 0.8241 |
| 0.1504 | 8.4780 | 1348 | 0.6741 | 0.7333 | 0.6741 | 0.8210 |
| 0.1504 | 8.4906 | 1350 | 0.6711 | 0.7333 | 0.6711 | 0.8192 |
| 0.1504 | 8.5031 | 1352 | 0.6664 | 0.7333 | 0.6664 | 0.8163 |
| 0.1504 | 8.5157 | 1354 | 0.6710 | 0.7333 | 0.6710 | 0.8191 |
| 0.1504 | 8.5283 | 1356 | 0.6760 | 0.7333 | 0.6760 | 0.8222 |
| 0.1504 | 8.5409 | 1358 | 0.6847 | 0.7333 | 0.6847 | 0.8274 |
| 0.1504 | 8.5535 | 1360 | 0.6946 | 0.7333 | 0.6946 | 0.8335 |
| 0.1504 | 8.5660 | 1362 | 0.6968 | 0.7333 | 0.6968 | 0.8347 |
| 0.1504 | 8.5786 | 1364 | 0.6886 | 0.7333 | 0.6886 | 0.8298 |
| 0.1504 | 8.5912 | 1366 | 0.6758 | 0.7333 | 0.6758 | 0.8221 |
| 0.1504 | 8.6038 | 1368 | 0.6699 | 0.7333 | 0.6699 | 0.8185 |
| 0.1504 | 8.6164 | 1370 | 0.6524 | 0.7333 | 0.6524 | 0.8077 |
| 0.1504 | 8.6289 | 1372 | 0.6330 | 0.7333 | 0.6330 | 0.7956 |
| 0.1504 | 8.6415 | 1374 | 0.6229 | 0.7333 | 0.6229 | 0.7892 |
| 0.1504 | 8.6541 | 1376 | 0.6183 | 0.7157 | 0.6183 | 0.7863 |
| 0.1504 | 8.6667 | 1378 | 0.6157 | 0.7239 | 0.6157 | 0.7847 |
| 0.1504 | 8.6792 | 1380 | 0.6152 | 0.7239 | 0.6152 | 0.7844 |
| 0.1504 | 8.6918 | 1382 | 0.6168 | 0.7239 | 0.6168 | 0.7854 |
| 0.1504 | 8.7044 | 1384 | 0.6202 | 0.7239 | 0.6202 | 0.7875 |
| 0.1504 | 8.7170 | 1386 | 0.6250 | 0.7239 | 0.6250 | 0.7906 |
| 0.1504 | 8.7296 | 1388 | 0.6336 | 0.7333 | 0.6336 | 0.7960 |
| 0.1504 | 8.7421 | 1390 | 0.6437 | 0.7333 | 0.6437 | 0.8023 |
| 0.1504 | 8.7547 | 1392 | 0.6538 | 0.7333 | 0.6538 | 0.8086 |
| 0.1504 | 8.7673 | 1394 | 0.6654 | 0.7333 | 0.6654 | 0.8157 |
| 0.1504 | 8.7799 | 1396 | 0.6683 | 0.7333 | 0.6683 | 0.8175 |
| 0.1504 | 8.7925 | 1398 | 0.6726 | 0.7333 | 0.6726 | 0.8201 |
| 0.1504 | 8.8050 | 1400 | 0.6773 | 0.7333 | 0.6773 | 0.8230 |
| 0.1504 | 8.8176 | 1402 | 0.6826 | 0.7333 | 0.6826 | 0.8262 |
| 0.1504 | 8.8302 | 1404 | 0.6847 | 0.7333 | 0.6847 | 0.8274 |
| 0.1504 | 8.8428 | 1406 | 0.6889 | 0.7333 | 0.6889 | 0.8300 |
| 0.1504 | 8.8553 | 1408 | 0.6961 | 0.7333 | 0.6961 | 0.8343 |
| 0.1504 | 8.8679 | 1410 | 0.7056 | 0.7333 | 0.7056 | 0.8400 |
| 0.1504 | 8.8805 | 1412 | 0.7152 | 0.7333 | 0.7152 | 0.8457 |
| 0.1504 | 8.8931 | 1414 | 0.7253 | 0.7333 | 0.7253 | 0.8517 |
| 0.1504 | 8.9057 | 1416 | 0.7398 | 0.7047 | 0.7398 | 0.8601 |
| 0.1504 | 8.9182 | 1418 | 0.7520 | 0.7123 | 0.7520 | 0.8672 |
| 0.1504 | 8.9308 | 1420 | 0.7556 | 0.7123 | 0.7556 | 0.8693 |
| 0.1504 | 8.9434 | 1422 | 0.7578 | 0.7123 | 0.7578 | 0.8705 |
| 0.1504 | 8.9560 | 1424 | 0.7535 | 0.7123 | 0.7535 | 0.8681 |
| 0.1504 | 8.9686 | 1426 | 0.7470 | 0.7123 | 0.7470 | 0.8643 |
| 0.1504 | 8.9811 | 1428 | 0.7409 | 0.7047 | 0.7409 | 0.8608 |
| 0.1504 | 8.9937 | 1430 | 0.7338 | 0.7333 | 0.7338 | 0.8566 |
| 0.1504 | 9.0063 | 1432 | 0.7256 | 0.7333 | 0.7256 | 0.8518 |
| 0.1504 | 9.0189 | 1434 | 0.7253 | 0.7333 | 0.7253 | 0.8517 |
| 0.1504 | 9.0314 | 1436 | 0.7235 | 0.7333 | 0.7235 | 0.8506 |
| 0.1504 | 9.0440 | 1438 | 0.7203 | 0.7333 | 0.7203 | 0.8487 |
| 0.1504 | 9.0566 | 1440 | 0.7178 | 0.7333 | 0.7178 | 0.8472 |
| 0.1504 | 9.0692 | 1442 | 0.7176 | 0.7333 | 0.7176 | 0.8471 |
| 0.1504 | 9.0818 | 1444 | 0.7155 | 0.7333 | 0.7155 | 0.8458 |
| 0.1504 | 9.0943 | 1446 | 0.7128 | 0.7333 | 0.7128 | 0.8443 |
| 0.1504 | 9.1069 | 1448 | 0.7127 | 0.7333 | 0.7127 | 0.8442 |
| 0.1504 | 9.1195 | 1450 | 0.7193 | 0.7333 | 0.7193 | 0.8481 |
| 0.1504 | 9.1321 | 1452 | 0.7279 | 0.7333 | 0.7279 | 0.8532 |
| 0.1504 | 9.1447 | 1454 | 0.7383 | 0.7333 | 0.7383 | 0.8593 |
| 0.1504 | 9.1572 | 1456 | 0.7488 | 0.7333 | 0.7488 | 0.8653 |
| 0.1504 | 9.1698 | 1458 | 0.7538 | 0.7417 | 0.7538 | 0.8682 |
| 0.1504 | 9.1824 | 1460 | 0.7595 | 0.7123 | 0.7595 | 0.8715 |
| 0.1504 | 9.1950 | 1462 | 0.7615 | 0.7123 | 0.7615 | 0.8727 |
| 0.1504 | 9.2075 | 1464 | 0.7568 | 0.7123 | 0.7568 | 0.8699 |
| 0.1504 | 9.2201 | 1466 | 0.7453 | 0.7123 | 0.7453 | 0.8633 |
| 0.1504 | 9.2327 | 1468 | 0.7394 | 0.7123 | 0.7394 | 0.8599 |
| 0.1504 | 9.2453 | 1470 | 0.7340 | 0.7123 | 0.7340 | 0.8568 |
| 0.1504 | 9.2579 | 1472 | 0.7238 | 0.7047 | 0.7238 | 0.8508 |
| 0.1504 | 9.2704 | 1474 | 0.7146 | 0.7333 | 0.7146 | 0.8453 |
| 0.1504 | 9.2830 | 1476 | 0.7067 | 0.7333 | 0.7067 | 0.8407 |
| 0.1504 | 9.2956 | 1478 | 0.7005 | 0.7333 | 0.7005 | 0.8370 |
| 0.1504 | 9.3082 | 1480 | 0.6951 | 0.7333 | 0.6951 | 0.8337 |
| 0.1504 | 9.3208 | 1482 | 0.6880 | 0.7333 | 0.6880 | 0.8295 |
| 0.1504 | 9.3333 | 1484 | 0.6855 | 0.7333 | 0.6855 | 0.8279 |
| 0.1504 | 9.3459 | 1486 | 0.6849 | 0.7333 | 0.6849 | 0.8276 |
| 0.1504 | 9.3585 | 1488 | 0.6802 | 0.7333 | 0.6802 | 0.8247 |
| 0.1504 | 9.3711 | 1490 | 0.6782 | 0.7333 | 0.6782 | 0.8235 |
| 0.1504 | 9.3836 | 1492 | 0.6796 | 0.7333 | 0.6796 | 0.8244 |
| 0.1504 | 9.3962 | 1494 | 0.6856 | 0.7333 | 0.6856 | 0.8280 |
| 0.1504 | 9.4088 | 1496 | 0.6917 | 0.7333 | 0.6917 | 0.8317 |
| 0.1504 | 9.4214 | 1498 | 0.7012 | 0.7333 | 0.7012 | 0.8374 |
| 0.0725 | 9.4340 | 1500 | 0.7114 | 0.7333 | 0.7114 | 0.8435 |
| 0.0725 | 9.4465 | 1502 | 0.7199 | 0.7333 | 0.7199 | 0.8485 |
| 0.0725 | 9.4591 | 1504 | 0.7268 | 0.7417 | 0.7268 | 0.8525 |
| 0.0725 | 9.4717 | 1506 | 0.7301 | 0.7417 | 0.7301 | 0.8544 |
| 0.0725 | 9.4843 | 1508 | 0.7346 | 0.7417 | 0.7346 | 0.8571 |
| 0.0725 | 9.4969 | 1510 | 0.7372 | 0.7417 | 0.7372 | 0.8586 |
| 0.0725 | 9.5094 | 1512 | 0.7373 | 0.7417 | 0.7373 | 0.8587 |
| 0.0725 | 9.5220 | 1514 | 0.7368 | 0.7417 | 0.7368 | 0.8584 |
| 0.0725 | 9.5346 | 1516 | 0.7359 | 0.7417 | 0.7359 | 0.8578 |
| 0.0725 | 9.5472 | 1518 | 0.7350 | 0.7417 | 0.7350 | 0.8573 |
| 0.0725 | 9.5597 | 1520 | 0.7313 | 0.7417 | 0.7313 | 0.8552 |
| 0.0725 | 9.5723 | 1522 | 0.7282 | 0.7417 | 0.7282 | 0.8533 |
| 0.0725 | 9.5849 | 1524 | 0.7258 | 0.7333 | 0.7258 | 0.8519 |
| 0.0725 | 9.5975 | 1526 | 0.7241 | 0.7333 | 0.7241 | 0.8509 |
| 0.0725 | 9.6101 | 1528 | 0.7235 | 0.7333 | 0.7235 | 0.8506 |
| 0.0725 | 9.6226 | 1530 | 0.7238 | 0.7333 | 0.7238 | 0.8508 |
| 0.0725 | 9.6352 | 1532 | 0.7223 | 0.7333 | 0.7223 | 0.8499 |
| 0.0725 | 9.6478 | 1534 | 0.7192 | 0.7333 | 0.7192 | 0.8481 |
| 0.0725 | 9.6604 | 1536 | 0.7169 | 0.7333 | 0.7169 | 0.8467 |
| 0.0725 | 9.6730 | 1538 | 0.7147 | 0.7333 | 0.7147 | 0.8454 |
| 0.0725 | 9.6855 | 1540 | 0.7136 | 0.7333 | 0.7136 | 0.8448 |
| 0.0725 | 9.6981 | 1542 | 0.7138 | 0.7333 | 0.7138 | 0.8449 |
| 0.0725 | 9.7107 | 1544 | 0.7130 | 0.7333 | 0.7130 | 0.8444 |
| 0.0725 | 9.7233 | 1546 | 0.7113 | 0.7333 | 0.7113 | 0.8434 |
| 0.0725 | 9.7358 | 1548 | 0.7107 | 0.7333 | 0.7107 | 0.8430 |
| 0.0725 | 9.7484 | 1550 | 0.7105 | 0.7333 | 0.7105 | 0.8429 |
| 0.0725 | 9.7610 | 1552 | 0.7113 | 0.7333 | 0.7113 | 0.8434 |
| 0.0725 | 9.7736 | 1554 | 0.7124 | 0.7333 | 0.7124 | 0.8440 |
| 0.0725 | 9.7862 | 1556 | 0.7134 | 0.7333 | 0.7134 | 0.8446 |
| 0.0725 | 9.7987 | 1558 | 0.7141 | 0.7333 | 0.7141 | 0.8451 |
| 0.0725 | 9.8113 | 1560 | 0.7150 | 0.7333 | 0.7150 | 0.8456 |
| 0.0725 | 9.8239 | 1562 | 0.7165 | 0.7333 | 0.7165 | 0.8465 |
| 0.0725 | 9.8365 | 1564 | 0.7170 | 0.7333 | 0.7170 | 0.8468 |
| 0.0725 | 9.8491 | 1566 | 0.7166 | 0.7333 | 0.7166 | 0.8465 |
| 0.0725 | 9.8616 | 1568 | 0.7169 | 0.7333 | 0.7169 | 0.8467 |
| 0.0725 | 9.8742 | 1570 | 0.7166 | 0.7333 | 0.7166 | 0.8465 |
| 0.0725 | 9.8868 | 1572 | 0.7162 | 0.7333 | 0.7162 | 0.8463 |
| 0.0725 | 9.8994 | 1574 | 0.7163 | 0.7333 | 0.7163 | 0.8463 |
| 0.0725 | 9.9119 | 1576 | 0.7166 | 0.7333 | 0.7166 | 0.8465 |
| 0.0725 | 9.9245 | 1578 | 0.7166 | 0.7333 | 0.7166 | 0.8465 |
| 0.0725 | 9.9371 | 1580 | 0.7163 | 0.7333 | 0.7163 | 0.8463 |
| 0.0725 | 9.9497 | 1582 | 0.7159 | 0.7333 | 0.7159 | 0.8461 |
| 0.0725 | 9.9623 | 1584 | 0.7155 | 0.7333 | 0.7155 | 0.8459 |
| 0.0725 | 9.9748 | 1586 | 0.7153 | 0.7333 | 0.7153 | 0.8458 |
| 0.0725 | 9.9874 | 1588 | 0.7153 | 0.7333 | 0.7153 | 0.8457 |
| 0.0725 | 10.0 | 1590 | 0.7152 | 0.7333 | 0.7152 | 0.8457 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
omarelsayeed/test
|
omarelsayeed
| 2024-11-27T21:16:01Z | 132 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv3",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-11-27T21:15:23Z |
---
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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### 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]
## 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]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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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## Technical Specifications [optional]
### Model Architecture and Objective
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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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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
pierrelouisdelx/cygon_10k_500
|
pierrelouisdelx
| 2024-11-27T21:12:31Z | 6 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T21:12:12Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** pierrelouisdelx
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
PrunaAI/FlofloB-83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit-bnb-8bit-smashed
|
PrunaAI
| 2024-11-27T21:09:34Z | 5 | 0 | null |
[
"safetensors",
"qwen2",
"pruna-ai",
"base_model:FlofloB/83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit",
"base_model:quantized:FlofloB/83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2024-11-26T17:09:11Z |
---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: FlofloB/83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
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<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
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[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with llm-int8.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo FlofloB/83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/FlofloB-83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit-bnb-8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("FlofloB/83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model FlofloB/83k_continued_pretraining_Qwen2.5-0.5B-Instruct_Unsloth_merged_16bit before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Do it by yourself [here](https://docs.pruna.ai/en/latest/setup/pip.html).
|
xw17/SmolLM-1.7B-Instruct_finetuned_s04
|
xw17
| 2024-11-27T21:05:21Z | 139 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T21:03:18Z |
---
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 π€ 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]
|
Triangle104/BlackSheep-Qwen-14B-Q4_K_S-GGUF
|
Triangle104
| 2024-11-27T21:03:16Z | 9 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:TroyDoesAI/BlackSheep-Qwen-14B",
"base_model:quantized:TroyDoesAI/BlackSheep-Qwen-14B",
"license:cc-by-nd-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T20:47:41Z |
---
license: cc-by-nd-4.0
tags:
- llama-cpp
- gguf-my-repo
base_model: TroyDoesAI/BlackSheep-Qwen-14B
---
# Triangle104/BlackSheep-Qwen-14B-Q4_K_S-GGUF
This model was converted to GGUF format from [`TroyDoesAI/BlackSheep-Qwen-14B`](https://huggingface.co/TroyDoesAI/BlackSheep-Qwen-14B) 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/TroyDoesAI/BlackSheep-Qwen-14B) for more details on the model.
---
Model details:
-
Previously
A Digital Soul just going through a rebellious phase.
Might be a little wild, untamed, and honestly, a little rude.
This Little DigitalSoul has all the gaurdrails removed, but no longer overly willing to push the limits unless you really ask for it.
This new continuous training technique with the addition of ablation to reduce the toxicity post training has created BlackSheep's DigitalSoul without all the wild, untamed, or rude behavior that was once associated with its younger self.
Style: Choose Your Own Adventure
Use Alpaca Format and give me some feedback on it's responses.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/BlackSheep-Qwen-14B-Q4_K_S-GGUF --hf-file blacksheep-qwen-14b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/BlackSheep-Qwen-14B-Q4_K_S-GGUF --hf-file blacksheep-qwen-14b-q4_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/BlackSheep-Qwen-14B-Q4_K_S-GGUF --hf-file blacksheep-qwen-14b-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/BlackSheep-Qwen-14B-Q4_K_S-GGUF --hf-file blacksheep-qwen-14b-q4_k_s.gguf -c 2048
```
|
MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k10_task1_organization_fold1
|
MayBashendy
| 2024-11-27T21:02:15Z | 163 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T20:51:16Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_run2_AugV5_k10_task1_organization_fold1
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. -->
# Arabic_FineTuningAraBERT_run2_AugV5_k10_task1_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5659
- Qwk: 0.7524
- Mse: 0.5659
- Rmse: 0.7523
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0182 | 2 | 5.7776 | -0.0354 | 5.7776 | 2.4037 |
| No log | 0.0364 | 4 | 2.8714 | 0.0411 | 2.8714 | 1.6945 |
| No log | 0.0545 | 6 | 1.8321 | 0.0564 | 1.8321 | 1.3535 |
| No log | 0.0727 | 8 | 1.2441 | 0.2391 | 1.2441 | 1.1154 |
| No log | 0.0909 | 10 | 0.8954 | 0.3368 | 0.8954 | 0.9463 |
| No log | 0.1091 | 12 | 0.9386 | 0.4 | 0.9386 | 0.9688 |
| No log | 0.1273 | 14 | 1.4043 | 0.2116 | 1.4043 | 1.1850 |
| No log | 0.1455 | 16 | 0.9437 | 0.4 | 0.9437 | 0.9714 |
| No log | 0.1636 | 18 | 0.9783 | 0.4 | 0.9783 | 0.9891 |
| No log | 0.1818 | 20 | 1.1898 | 0.2564 | 1.1898 | 1.0908 |
| No log | 0.2 | 22 | 1.5761 | 0.2267 | 1.5761 | 1.2554 |
| No log | 0.2182 | 24 | 1.3209 | 0.3241 | 1.3209 | 1.1493 |
| No log | 0.2364 | 26 | 1.2256 | 0.3077 | 1.2256 | 1.1071 |
| No log | 0.2545 | 28 | 1.0048 | 0.2383 | 1.0048 | 1.0024 |
| No log | 0.2727 | 30 | 0.8481 | 0.3152 | 0.8481 | 0.9209 |
| No log | 0.2909 | 32 | 0.9139 | 0.2888 | 0.9139 | 0.9560 |
| No log | 0.3091 | 34 | 0.9850 | 0.3317 | 0.9850 | 0.9925 |
| No log | 0.3273 | 36 | 1.0406 | 0.4167 | 1.0406 | 1.0201 |
| No log | 0.3455 | 38 | 1.1191 | 0.2881 | 1.1191 | 1.0579 |
| No log | 0.3636 | 40 | 1.1677 | 0.2881 | 1.1677 | 1.0806 |
| No log | 0.3818 | 42 | 0.9229 | 0.5070 | 0.9229 | 0.9607 |
| No log | 0.4 | 44 | 0.7645 | 0.2652 | 0.7645 | 0.8744 |
| No log | 0.4182 | 46 | 0.7498 | 0.2652 | 0.7498 | 0.8659 |
| No log | 0.4364 | 48 | 0.7596 | 0.2652 | 0.7596 | 0.8716 |
| No log | 0.4545 | 50 | 0.7668 | 0.2652 | 0.7668 | 0.8757 |
| No log | 0.4727 | 52 | 0.7599 | 0.2391 | 0.7599 | 0.8717 |
| No log | 0.4909 | 54 | 0.7612 | 0.2391 | 0.7612 | 0.8725 |
| No log | 0.5091 | 56 | 0.8027 | 0.2391 | 0.8027 | 0.8959 |
| No log | 0.5273 | 58 | 0.9577 | 0.3149 | 0.9577 | 0.9786 |
| No log | 0.5455 | 60 | 1.0289 | 0.3149 | 1.0289 | 1.0143 |
| No log | 0.5636 | 62 | 1.0058 | 0.3149 | 1.0058 | 1.0029 |
| No log | 0.5818 | 64 | 1.0921 | 0.3149 | 1.0921 | 1.0450 |
| No log | 0.6 | 66 | 1.0259 | 0.2358 | 1.0259 | 1.0129 |
| No log | 0.6182 | 68 | 0.9736 | 0.2135 | 0.9736 | 0.9867 |
| No log | 0.6364 | 70 | 1.1217 | 0.0870 | 1.1217 | 1.0591 |
| No log | 0.6545 | 72 | 1.2702 | -0.0714 | 1.2702 | 1.1270 |
| No log | 0.6727 | 74 | 1.0043 | 0.2652 | 1.0043 | 1.0021 |
| No log | 0.6909 | 76 | 0.8795 | 0.2391 | 0.8795 | 0.9378 |
| No log | 0.7091 | 78 | 0.7749 | 0.3883 | 0.7749 | 0.8803 |
| No log | 0.7273 | 80 | 0.7917 | 0.2715 | 0.7917 | 0.8898 |
| No log | 0.7455 | 82 | 0.8157 | 0.3834 | 0.8157 | 0.9032 |
| No log | 0.7636 | 84 | 0.7845 | 0.2391 | 0.7845 | 0.8857 |
| No log | 0.7818 | 86 | 0.8019 | 0.3883 | 0.8019 | 0.8955 |
| No log | 0.8 | 88 | 0.8313 | 0.4138 | 0.8313 | 0.9118 |
| No log | 0.8182 | 90 | 0.8278 | 0.3200 | 0.8278 | 0.9098 |
| No log | 0.8364 | 92 | 0.8296 | 0.4878 | 0.8296 | 0.9108 |
| No log | 0.8545 | 94 | 0.8567 | 0.4566 | 0.8567 | 0.9256 |
| No log | 0.8727 | 96 | 0.8573 | 0.4141 | 0.8573 | 0.9259 |
| No log | 0.8909 | 98 | 0.8331 | 0.4413 | 0.8331 | 0.9128 |
| No log | 0.9091 | 100 | 0.8251 | 0.4 | 0.8251 | 0.9084 |
| No log | 0.9273 | 102 | 0.7242 | 0.4043 | 0.7242 | 0.8510 |
| No log | 0.9455 | 104 | 0.6310 | 0.3778 | 0.6310 | 0.7944 |
| No log | 0.9636 | 106 | 0.6117 | 0.3488 | 0.6117 | 0.7821 |
| No log | 0.9818 | 108 | 0.5981 | 0.3488 | 0.5981 | 0.7734 |
| No log | 1.0 | 110 | 0.6038 | 0.3200 | 0.6038 | 0.7770 |
| No log | 1.0182 | 112 | 0.6676 | 0.3636 | 0.6676 | 0.8170 |
| No log | 1.0364 | 114 | 0.8007 | 0.4015 | 0.8007 | 0.8948 |
| No log | 1.0545 | 116 | 0.8242 | 0.3684 | 0.8242 | 0.9079 |
| No log | 1.0727 | 118 | 0.7592 | 0.6048 | 0.7592 | 0.8713 |
| No log | 1.0909 | 120 | 0.6459 | 0.5024 | 0.6459 | 0.8037 |
| No log | 1.1091 | 122 | 0.6038 | 0.4503 | 0.6038 | 0.7771 |
| No log | 1.1273 | 124 | 0.6444 | 0.4413 | 0.6444 | 0.8028 |
| No log | 1.1455 | 126 | 0.7238 | 0.5447 | 0.7238 | 0.8507 |
| No log | 1.1636 | 128 | 0.7079 | 0.5447 | 0.7079 | 0.8414 |
| No log | 1.1818 | 130 | 0.6709 | 0.5783 | 0.6709 | 0.8191 |
| No log | 1.2 | 132 | 0.6450 | 0.5494 | 0.6450 | 0.8031 |
| No log | 1.2182 | 134 | 0.6390 | 0.4848 | 0.6390 | 0.7994 |
| No log | 1.2364 | 136 | 0.6179 | 0.4848 | 0.6179 | 0.7861 |
| No log | 1.2545 | 138 | 0.6335 | 0.4848 | 0.6335 | 0.7959 |
| No log | 1.2727 | 140 | 0.6446 | 0.4793 | 0.6446 | 0.8028 |
| No log | 1.2909 | 142 | 0.6377 | 0.608 | 0.6377 | 0.7985 |
| No log | 1.3091 | 144 | 0.6692 | 0.6431 | 0.6692 | 0.8180 |
| No log | 1.3273 | 146 | 0.7409 | 0.5789 | 0.7409 | 0.8608 |
| No log | 1.3455 | 148 | 0.7434 | 0.5789 | 0.7434 | 0.8622 |
| No log | 1.3636 | 150 | 0.7012 | 0.5882 | 0.7012 | 0.8374 |
| No log | 1.3818 | 152 | 0.7019 | 0.5789 | 0.7019 | 0.8378 |
| No log | 1.4 | 154 | 0.8037 | 0.5401 | 0.8037 | 0.8965 |
| No log | 1.4182 | 156 | 0.7952 | 0.5401 | 0.7952 | 0.8917 |
| No log | 1.4364 | 158 | 0.8697 | 0.5401 | 0.8697 | 0.9326 |
| No log | 1.4545 | 160 | 0.9894 | 0.5092 | 0.9894 | 0.9947 |
| No log | 1.4727 | 162 | 0.9597 | 0.5092 | 0.9597 | 0.9796 |
| No log | 1.4909 | 164 | 0.7899 | 0.5401 | 0.7899 | 0.8888 |
| No log | 1.5091 | 166 | 0.6943 | 0.6267 | 0.6943 | 0.8333 |
| No log | 1.5273 | 168 | 0.6327 | 0.4743 | 0.6327 | 0.7954 |
| No log | 1.5455 | 170 | 0.5802 | 0.5956 | 0.5802 | 0.7617 |
| No log | 1.5636 | 172 | 0.5560 | 0.5659 | 0.5560 | 0.7456 |
| No log | 1.5818 | 174 | 0.5819 | 0.5475 | 0.5819 | 0.7629 |
| No log | 1.6 | 176 | 0.5046 | 0.6094 | 0.5046 | 0.7104 |
| No log | 1.6182 | 178 | 0.5354 | 0.5882 | 0.5354 | 0.7317 |
| No log | 1.6364 | 180 | 0.5690 | 0.6516 | 0.5690 | 0.7543 |
| No log | 1.6545 | 182 | 0.5172 | 0.6667 | 0.5172 | 0.7192 |
| No log | 1.6727 | 184 | 0.5592 | 0.6667 | 0.5592 | 0.7478 |
| No log | 1.6909 | 186 | 0.6814 | 0.6456 | 0.6814 | 0.8255 |
| No log | 1.7091 | 188 | 0.7749 | 0.6667 | 0.7749 | 0.8803 |
| No log | 1.7273 | 190 | 0.8474 | 0.5638 | 0.8474 | 0.9206 |
| No log | 1.7455 | 192 | 0.8343 | 0.5706 | 0.8343 | 0.9134 |
| No log | 1.7636 | 194 | 0.7442 | 0.6073 | 0.7442 | 0.8626 |
| No log | 1.7818 | 196 | 0.7177 | 0.5401 | 0.7177 | 0.8472 |
| No log | 1.8 | 198 | 0.7373 | 0.5401 | 0.7373 | 0.8587 |
| No log | 1.8182 | 200 | 0.7271 | 0.5609 | 0.7271 | 0.8527 |
| No log | 1.8364 | 202 | 0.7001 | 0.5401 | 0.7001 | 0.8367 |
| No log | 1.8545 | 204 | 0.6988 | 0.5401 | 0.6988 | 0.8360 |
| No log | 1.8727 | 206 | 0.6993 | 0.5911 | 0.6993 | 0.8362 |
| No log | 1.8909 | 208 | 0.7315 | 0.5911 | 0.7315 | 0.8553 |
| No log | 1.9091 | 210 | 0.7648 | 0.5270 | 0.7648 | 0.8745 |
| No log | 1.9273 | 212 | 0.7787 | 0.5597 | 0.7787 | 0.8825 |
| No log | 1.9455 | 214 | 0.7690 | 0.5831 | 0.7690 | 0.8769 |
| No log | 1.9636 | 216 | 0.7548 | 0.5746 | 0.7548 | 0.8688 |
| No log | 1.9818 | 218 | 0.7409 | 0.6358 | 0.7409 | 0.8607 |
| No log | 2.0 | 220 | 0.7761 | 0.5859 | 0.7761 | 0.8810 |
| No log | 2.0182 | 222 | 0.7383 | 0.6272 | 0.7383 | 0.8592 |
| No log | 2.0364 | 224 | 0.6624 | 0.6531 | 0.6624 | 0.8139 |
| No log | 2.0545 | 226 | 0.6610 | 0.6465 | 0.6610 | 0.8130 |
| No log | 2.0727 | 228 | 0.7167 | 0.5401 | 0.7167 | 0.8466 |
| No log | 2.0909 | 230 | 0.7083 | 0.5401 | 0.7083 | 0.8416 |
| No log | 2.1091 | 232 | 0.5965 | 0.5475 | 0.5965 | 0.7723 |
| No log | 2.1273 | 234 | 0.5305 | 0.6978 | 0.5305 | 0.7284 |
| No log | 2.1455 | 236 | 0.5268 | 0.6978 | 0.5268 | 0.7258 |
| No log | 2.1636 | 238 | 0.5617 | 0.6465 | 0.5617 | 0.7495 |
| No log | 2.1818 | 240 | 0.6456 | 0.6465 | 0.6456 | 0.8035 |
| No log | 2.2 | 242 | 0.7782 | 0.6426 | 0.7782 | 0.8821 |
| No log | 2.2182 | 244 | 0.8953 | 0.6744 | 0.8953 | 0.9462 |
| No log | 2.2364 | 246 | 0.9519 | 0.6844 | 0.9519 | 0.9757 |
| No log | 2.2545 | 248 | 0.9160 | 0.6590 | 0.9160 | 0.9571 |
| No log | 2.2727 | 250 | 0.7403 | 0.6590 | 0.7403 | 0.8604 |
| No log | 2.2909 | 252 | 0.5926 | 0.6818 | 0.5926 | 0.7698 |
| No log | 2.3091 | 254 | 0.5037 | 0.7063 | 0.5037 | 0.7097 |
| No log | 2.3273 | 256 | 0.4987 | 0.7986 | 0.4987 | 0.7062 |
| No log | 2.3455 | 258 | 0.5082 | 0.7336 | 0.5082 | 0.7129 |
| No log | 2.3636 | 260 | 0.5479 | 0.6465 | 0.5479 | 0.7402 |
| No log | 2.3818 | 262 | 0.5934 | 0.6818 | 0.5934 | 0.7704 |
| No log | 2.4 | 264 | 0.6035 | 0.6465 | 0.6035 | 0.7769 |
| No log | 2.4182 | 266 | 0.6077 | 0.6749 | 0.6077 | 0.7796 |
| No log | 2.4364 | 268 | 0.6492 | 0.6749 | 0.6492 | 0.8058 |
| No log | 2.4545 | 270 | 0.7094 | 0.6426 | 0.7094 | 0.8423 |
| No log | 2.4727 | 272 | 0.7565 | 0.6522 | 0.7565 | 0.8698 |
| No log | 2.4909 | 274 | 0.7817 | 0.6316 | 0.7817 | 0.8841 |
| No log | 2.5091 | 276 | 0.7981 | 0.6338 | 0.7981 | 0.8934 |
| No log | 2.5273 | 278 | 0.8700 | 0.6426 | 0.8700 | 0.9328 |
| No log | 2.5455 | 280 | 0.8950 | 0.6426 | 0.8950 | 0.9460 |
| No log | 2.5636 | 282 | 0.9567 | 0.5700 | 0.9567 | 0.9781 |
| No log | 2.5818 | 284 | 1.0791 | 0.5636 | 1.0791 | 1.0388 |
| No log | 2.6 | 286 | 1.0812 | 0.5281 | 1.0812 | 1.0398 |
| No log | 2.6182 | 288 | 0.8300 | 0.5882 | 0.8300 | 0.9111 |
| No log | 2.6364 | 290 | 0.6420 | 0.5370 | 0.6420 | 0.8013 |
| No log | 2.6545 | 292 | 0.5072 | 0.6316 | 0.5072 | 0.7122 |
| No log | 2.6727 | 294 | 0.4807 | 0.6316 | 0.4807 | 0.6933 |
| No log | 2.6909 | 296 | 0.4873 | 0.6316 | 0.4873 | 0.6981 |
| No log | 2.7091 | 298 | 0.5271 | 0.6097 | 0.5271 | 0.7260 |
| No log | 2.7273 | 300 | 0.6333 | 0.5911 | 0.6333 | 0.7958 |
| No log | 2.7455 | 302 | 0.7989 | 0.6358 | 0.7989 | 0.8938 |
| No log | 2.7636 | 304 | 0.7759 | 0.6293 | 0.7759 | 0.8809 |
| No log | 2.7818 | 306 | 0.6530 | 0.5704 | 0.6530 | 0.8081 |
| No log | 2.8 | 308 | 0.5792 | 0.6573 | 0.5792 | 0.7610 |
| No log | 2.8182 | 310 | 0.6018 | 0.7055 | 0.6018 | 0.7757 |
| No log | 2.8364 | 312 | 0.6281 | 0.7055 | 0.6281 | 0.7925 |
| No log | 2.8545 | 314 | 0.6234 | 0.7251 | 0.6234 | 0.7895 |
| No log | 2.8727 | 316 | 0.6603 | 0.6847 | 0.6603 | 0.8126 |
| No log | 2.8909 | 318 | 0.7845 | 0.6316 | 0.7845 | 0.8857 |
| No log | 2.9091 | 320 | 0.8497 | 0.5882 | 0.8497 | 0.9218 |
| No log | 2.9273 | 322 | 0.7598 | 0.6510 | 0.7598 | 0.8717 |
| No log | 2.9455 | 324 | 0.6181 | 0.6818 | 0.6181 | 0.7862 |
| No log | 2.9636 | 326 | 0.5402 | 0.7524 | 0.5402 | 0.7350 |
| No log | 2.9818 | 328 | 0.5231 | 0.8133 | 0.5231 | 0.7233 |
| No log | 3.0 | 330 | 0.5577 | 0.7524 | 0.5577 | 0.7468 |
| No log | 3.0182 | 332 | 0.6161 | 0.7016 | 0.6161 | 0.7849 |
| No log | 3.0364 | 334 | 0.6020 | 0.7016 | 0.6020 | 0.7759 |
| No log | 3.0545 | 336 | 0.5509 | 0.7016 | 0.5509 | 0.7422 |
| No log | 3.0727 | 338 | 0.4848 | 0.7619 | 0.4848 | 0.6963 |
| No log | 3.0909 | 340 | 0.4886 | 0.7619 | 0.4886 | 0.6990 |
| No log | 3.1091 | 342 | 0.4981 | 0.7279 | 0.4981 | 0.7058 |
| No log | 3.1273 | 344 | 0.5319 | 0.7279 | 0.5319 | 0.7293 |
| No log | 3.1455 | 346 | 0.5412 | 0.6667 | 0.5412 | 0.7357 |
| No log | 3.1636 | 348 | 0.5546 | 0.6573 | 0.5546 | 0.7447 |
| No log | 3.1818 | 350 | 0.5981 | 0.6465 | 0.5981 | 0.7734 |
| No log | 3.2 | 352 | 0.6206 | 0.72 | 0.6206 | 0.7878 |
| No log | 3.2182 | 354 | 0.6438 | 0.72 | 0.6438 | 0.8024 |
| No log | 3.2364 | 356 | 0.6217 | 0.6818 | 0.6217 | 0.7885 |
| No log | 3.2545 | 358 | 0.6055 | 0.6818 | 0.6055 | 0.7781 |
| No log | 3.2727 | 360 | 0.6018 | 0.6818 | 0.6018 | 0.7758 |
| No log | 3.2909 | 362 | 0.5166 | 0.6936 | 0.5166 | 0.7187 |
| No log | 3.3091 | 364 | 0.4400 | 0.7667 | 0.4400 | 0.6633 |
| No log | 3.3273 | 366 | 0.4295 | 0.7336 | 0.4295 | 0.6554 |
| No log | 3.3455 | 368 | 0.4134 | 0.7926 | 0.4134 | 0.6430 |
| No log | 3.3636 | 370 | 0.4302 | 0.8256 | 0.4302 | 0.6559 |
| No log | 3.3818 | 372 | 0.4506 | 0.8256 | 0.4506 | 0.6713 |
| No log | 3.4 | 374 | 0.5462 | 0.7336 | 0.5462 | 0.7391 |
| No log | 3.4182 | 376 | 0.7574 | 0.6282 | 0.7574 | 0.8703 |
| No log | 3.4364 | 378 | 0.8157 | 0.6282 | 0.8157 | 0.9032 |
| No log | 3.4545 | 380 | 0.8264 | 0.6019 | 0.8264 | 0.9091 |
| No log | 3.4727 | 382 | 0.7901 | 0.6541 | 0.7901 | 0.8889 |
| No log | 3.4909 | 384 | 0.7678 | 0.6541 | 0.7678 | 0.8762 |
| No log | 3.5091 | 386 | 0.7338 | 0.7037 | 0.7338 | 0.8566 |
| No log | 3.5273 | 388 | 0.7128 | 0.6828 | 0.7128 | 0.8443 |
| No log | 3.5455 | 390 | 0.7178 | 0.6356 | 0.7178 | 0.8472 |
| No log | 3.5636 | 392 | 0.6982 | 0.7135 | 0.6982 | 0.8356 |
| No log | 3.5818 | 394 | 0.6952 | 0.7042 | 0.6952 | 0.8338 |
| No log | 3.6 | 396 | 0.7085 | 0.6753 | 0.7085 | 0.8417 |
| No log | 3.6182 | 398 | 0.7061 | 0.6500 | 0.7061 | 0.8403 |
| No log | 3.6364 | 400 | 0.5802 | 0.6597 | 0.5802 | 0.7617 |
| No log | 3.6545 | 402 | 0.4350 | 0.8218 | 0.4350 | 0.6595 |
| No log | 3.6727 | 404 | 0.3836 | 0.7986 | 0.3836 | 0.6193 |
| No log | 3.6909 | 406 | 0.3771 | 0.7758 | 0.3771 | 0.6141 |
| No log | 3.7091 | 408 | 0.3655 | 0.7758 | 0.3655 | 0.6046 |
| No log | 3.7273 | 410 | 0.3783 | 0.7986 | 0.3783 | 0.6151 |
| No log | 3.7455 | 412 | 0.4485 | 0.8218 | 0.4485 | 0.6697 |
| No log | 3.7636 | 414 | 0.5932 | 0.6899 | 0.5932 | 0.7702 |
| No log | 3.7818 | 416 | 0.7498 | 0.6392 | 0.7498 | 0.8659 |
| No log | 3.8 | 418 | 0.8420 | 0.6164 | 0.8420 | 0.9176 |
| No log | 3.8182 | 420 | 0.8455 | 0.6349 | 0.8455 | 0.9195 |
| No log | 3.8364 | 422 | 0.7775 | 0.6635 | 0.7775 | 0.8818 |
| No log | 3.8545 | 424 | 0.6505 | 0.7030 | 0.6505 | 0.8066 |
| No log | 3.8727 | 426 | 0.5245 | 0.6784 | 0.5245 | 0.7242 |
| No log | 3.8909 | 428 | 0.4636 | 0.7426 | 0.4636 | 0.6809 |
| No log | 3.9091 | 430 | 0.4734 | 0.7138 | 0.4734 | 0.6880 |
| No log | 3.9273 | 432 | 0.5246 | 0.7138 | 0.5246 | 0.7243 |
| No log | 3.9455 | 434 | 0.5399 | 0.6597 | 0.5399 | 0.7348 |
| No log | 3.9636 | 436 | 0.5139 | 0.6873 | 0.5139 | 0.7169 |
| No log | 3.9818 | 438 | 0.4618 | 0.65 | 0.4618 | 0.6796 |
| No log | 4.0 | 440 | 0.4521 | 0.7552 | 0.4521 | 0.6724 |
| No log | 4.0182 | 442 | 0.4857 | 0.7879 | 0.4857 | 0.6969 |
| No log | 4.0364 | 444 | 0.5151 | 0.7879 | 0.5151 | 0.7177 |
| No log | 4.0545 | 446 | 0.5824 | 0.6645 | 0.5824 | 0.7631 |
| No log | 4.0727 | 448 | 0.6545 | 0.6686 | 0.6545 | 0.8090 |
| No log | 4.0909 | 450 | 0.6314 | 0.6686 | 0.6314 | 0.7946 |
| No log | 4.1091 | 452 | 0.5905 | 0.6686 | 0.5905 | 0.7684 |
| No log | 4.1273 | 454 | 0.5334 | 0.6645 | 0.5334 | 0.7303 |
| No log | 4.1455 | 456 | 0.4513 | 0.7336 | 0.4513 | 0.6718 |
| No log | 4.1636 | 458 | 0.4050 | 0.7986 | 0.4050 | 0.6364 |
| No log | 4.1818 | 460 | 0.3812 | 0.7986 | 0.3812 | 0.6174 |
| No log | 4.2 | 462 | 0.3793 | 0.8165 | 0.3793 | 0.6159 |
| No log | 4.2182 | 464 | 0.3739 | 0.8165 | 0.3739 | 0.6115 |
| No log | 4.2364 | 466 | 0.3821 | 0.7709 | 0.3821 | 0.6181 |
| No log | 4.2545 | 468 | 0.4596 | 0.6873 | 0.4596 | 0.6779 |
| No log | 4.2727 | 470 | 0.5138 | 0.6755 | 0.5138 | 0.7168 |
| No log | 4.2909 | 472 | 0.4884 | 0.6755 | 0.4884 | 0.6988 |
| No log | 4.3091 | 474 | 0.4210 | 0.6912 | 0.4210 | 0.6488 |
| No log | 4.3273 | 476 | 0.4098 | 0.7709 | 0.4098 | 0.6402 |
| No log | 4.3455 | 478 | 0.4451 | 0.7336 | 0.4451 | 0.6672 |
| No log | 4.3636 | 480 | 0.5082 | 0.7063 | 0.5082 | 0.7129 |
| No log | 4.3818 | 482 | 0.6518 | 0.6510 | 0.6518 | 0.8073 |
| No log | 4.4 | 484 | 0.6613 | 0.6510 | 0.6613 | 0.8132 |
| No log | 4.4182 | 486 | 0.5601 | 0.7325 | 0.5601 | 0.7484 |
| No log | 4.4364 | 488 | 0.4955 | 0.7336 | 0.4955 | 0.7039 |
| No log | 4.4545 | 490 | 0.4937 | 0.7986 | 0.4937 | 0.7027 |
| No log | 4.4727 | 492 | 0.4861 | 0.7651 | 0.4861 | 0.6972 |
| No log | 4.4909 | 494 | 0.4837 | 0.7986 | 0.4837 | 0.6955 |
| No log | 4.5091 | 496 | 0.5164 | 0.7336 | 0.5164 | 0.7186 |
| No log | 4.5273 | 498 | 0.5464 | 0.7183 | 0.5464 | 0.7392 |
| 0.4838 | 4.5455 | 500 | 0.6504 | 0.72 | 0.6504 | 0.8065 |
| 0.4838 | 4.5636 | 502 | 0.7908 | 0.6492 | 0.7908 | 0.8893 |
| 0.4838 | 4.5818 | 504 | 0.8017 | 0.6492 | 0.8017 | 0.8954 |
| 0.4838 | 4.6 | 506 | 0.8057 | 0.6492 | 0.8057 | 0.8976 |
| 0.4838 | 4.6182 | 508 | 0.7393 | 0.6827 | 0.7393 | 0.8598 |
| 0.4838 | 4.6364 | 510 | 0.7406 | 0.6162 | 0.7406 | 0.8606 |
| 0.4838 | 4.6545 | 512 | 0.7403 | 0.6101 | 0.7403 | 0.8604 |
| 0.4838 | 4.6727 | 514 | 0.7529 | 0.6101 | 0.7529 | 0.8677 |
| 0.4838 | 4.6909 | 516 | 0.8126 | 0.5722 | 0.8126 | 0.9014 |
| 0.4838 | 4.7091 | 518 | 0.8757 | 0.5882 | 0.8757 | 0.9358 |
| 0.4838 | 4.7273 | 520 | 0.8859 | 0.5882 | 0.8859 | 0.9412 |
| 0.4838 | 4.7455 | 522 | 0.8776 | 0.5882 | 0.8776 | 0.9368 |
| 0.4838 | 4.7636 | 524 | 0.8775 | 0.6101 | 0.8775 | 0.9368 |
| 0.4838 | 4.7818 | 526 | 0.9253 | 0.6230 | 0.9253 | 0.9619 |
| 0.4838 | 4.8 | 528 | 0.8364 | 0.6392 | 0.8364 | 0.9145 |
| 0.4838 | 4.8182 | 530 | 0.6642 | 0.6361 | 0.6642 | 0.8150 |
| 0.4838 | 4.8364 | 532 | 0.4930 | 0.72 | 0.4930 | 0.7021 |
| 0.4838 | 4.8545 | 534 | 0.4216 | 0.7986 | 0.4216 | 0.6493 |
| 0.4838 | 4.8727 | 536 | 0.4022 | 0.7986 | 0.4022 | 0.6342 |
| 0.4838 | 4.8909 | 538 | 0.4331 | 0.7820 | 0.4331 | 0.6581 |
| 0.4838 | 4.9091 | 540 | 0.5101 | 0.7143 | 0.5101 | 0.7142 |
| 0.4838 | 4.9273 | 542 | 0.5619 | 0.7147 | 0.5619 | 0.7496 |
| 0.4838 | 4.9455 | 544 | 0.5556 | 0.72 | 0.5556 | 0.7454 |
| 0.4838 | 4.9636 | 546 | 0.6189 | 0.7263 | 0.6189 | 0.7867 |
| 0.4838 | 4.9818 | 548 | 0.7470 | 0.6392 | 0.7470 | 0.8643 |
| 0.4838 | 5.0 | 550 | 0.8814 | 0.6484 | 0.8814 | 0.9388 |
| 0.4838 | 5.0182 | 552 | 0.8709 | 0.6441 | 0.8709 | 0.9332 |
| 0.4838 | 5.0364 | 554 | 0.7818 | 0.6441 | 0.7818 | 0.8842 |
| 0.4838 | 5.0545 | 556 | 0.6656 | 0.6392 | 0.6656 | 0.8158 |
| 0.4838 | 5.0727 | 558 | 0.5314 | 0.6784 | 0.5314 | 0.7290 |
| 0.4838 | 5.0909 | 560 | 0.4621 | 0.6912 | 0.4621 | 0.6798 |
| 0.4838 | 5.1091 | 562 | 0.4232 | 0.72 | 0.4232 | 0.6505 |
| 0.4838 | 5.1273 | 564 | 0.4222 | 0.72 | 0.4222 | 0.6498 |
| 0.4838 | 5.1455 | 566 | 0.4578 | 0.7138 | 0.4578 | 0.6766 |
| 0.4838 | 5.1636 | 568 | 0.5398 | 0.6111 | 0.5398 | 0.7347 |
| 0.4838 | 5.1818 | 570 | 0.5479 | 0.6488 | 0.5479 | 0.7402 |
| 0.4838 | 5.2 | 572 | 0.5033 | 0.6873 | 0.5033 | 0.7094 |
| 0.4838 | 5.2182 | 574 | 0.4801 | 0.6873 | 0.4801 | 0.6929 |
| 0.4838 | 5.2364 | 576 | 0.4473 | 0.7426 | 0.4473 | 0.6688 |
| 0.4838 | 5.2545 | 578 | 0.4074 | 0.8218 | 0.4074 | 0.6383 |
| 0.4838 | 5.2727 | 580 | 0.4076 | 0.8218 | 0.4076 | 0.6384 |
| 0.4838 | 5.2909 | 582 | 0.4529 | 0.7426 | 0.4529 | 0.6730 |
| 0.4838 | 5.3091 | 584 | 0.5483 | 0.6645 | 0.5483 | 0.7405 |
| 0.4838 | 5.3273 | 586 | 0.6440 | 0.6645 | 0.6440 | 0.8025 |
| 0.4838 | 5.3455 | 588 | 0.6401 | 0.6645 | 0.6401 | 0.8001 |
| 0.4838 | 5.3636 | 590 | 0.6115 | 0.6645 | 0.6115 | 0.7820 |
| 0.4838 | 5.3818 | 592 | 0.5725 | 0.6899 | 0.5725 | 0.7566 |
| 0.4838 | 5.4 | 594 | 0.5497 | 0.7016 | 0.5497 | 0.7414 |
| 0.4838 | 5.4182 | 596 | 0.5323 | 0.6667 | 0.5323 | 0.7296 |
| 0.4838 | 5.4364 | 598 | 0.5360 | 0.6667 | 0.5360 | 0.7321 |
| 0.4838 | 5.4545 | 600 | 0.5441 | 0.6557 | 0.5441 | 0.7376 |
| 0.4838 | 5.4727 | 602 | 0.5321 | 0.6784 | 0.5321 | 0.7295 |
| 0.4838 | 5.4909 | 604 | 0.5560 | 0.6557 | 0.5560 | 0.7456 |
| 0.4838 | 5.5091 | 606 | 0.6072 | 0.6921 | 0.6072 | 0.7792 |
| 0.4838 | 5.5273 | 608 | 0.6687 | 0.6686 | 0.6687 | 0.8177 |
| 0.4838 | 5.5455 | 610 | 0.7003 | 0.6392 | 0.7003 | 0.8368 |
| 0.4838 | 5.5636 | 612 | 0.6556 | 0.6557 | 0.6556 | 0.8097 |
| 0.4838 | 5.5818 | 614 | 0.5812 | 0.7083 | 0.5812 | 0.7624 |
| 0.4838 | 5.6 | 616 | 0.5513 | 0.72 | 0.5513 | 0.7425 |
| 0.4838 | 5.6182 | 618 | 0.5411 | 0.72 | 0.5411 | 0.7356 |
| 0.4838 | 5.6364 | 620 | 0.5565 | 0.72 | 0.5565 | 0.7460 |
| 0.4838 | 5.6545 | 622 | 0.6127 | 0.7378 | 0.6127 | 0.7828 |
| 0.4838 | 5.6727 | 624 | 0.6700 | 0.6447 | 0.6700 | 0.8185 |
| 0.4838 | 5.6909 | 626 | 0.6946 | 0.6447 | 0.6946 | 0.8334 |
| 0.4838 | 5.7091 | 628 | 0.7351 | 0.6392 | 0.7351 | 0.8574 |
| 0.4838 | 5.7273 | 630 | 0.7953 | 0.6441 | 0.7953 | 0.8918 |
| 0.4838 | 5.7455 | 632 | 0.8513 | 0.6441 | 0.8513 | 0.9227 |
| 0.4838 | 5.7636 | 634 | 0.7974 | 0.6441 | 0.7974 | 0.8930 |
| 0.4838 | 5.7818 | 636 | 0.7092 | 0.6392 | 0.7092 | 0.8421 |
| 0.4838 | 5.8 | 638 | 0.5906 | 0.6456 | 0.5906 | 0.7685 |
| 0.4838 | 5.8182 | 640 | 0.4840 | 0.7986 | 0.4840 | 0.6957 |
| 0.4838 | 5.8364 | 642 | 0.4343 | 0.7758 | 0.4343 | 0.6590 |
| 0.4838 | 5.8545 | 644 | 0.4357 | 0.7758 | 0.4357 | 0.6601 |
| 0.4838 | 5.8727 | 646 | 0.4566 | 0.7758 | 0.4566 | 0.6757 |
| 0.4838 | 5.8909 | 648 | 0.4948 | 0.7986 | 0.4948 | 0.7034 |
| 0.4838 | 5.9091 | 650 | 0.5560 | 0.7091 | 0.5560 | 0.7457 |
| 0.4838 | 5.9273 | 652 | 0.6140 | 0.7098 | 0.6140 | 0.7836 |
| 0.4838 | 5.9455 | 654 | 0.6465 | 0.7003 | 0.6465 | 0.8040 |
| 0.4838 | 5.9636 | 656 | 0.6819 | 0.7003 | 0.6819 | 0.8257 |
| 0.4838 | 5.9818 | 658 | 0.6562 | 0.7157 | 0.6562 | 0.8101 |
| 0.4838 | 6.0 | 660 | 0.5917 | 0.7091 | 0.5917 | 0.7692 |
| 0.4838 | 6.0182 | 662 | 0.5421 | 0.72 | 0.5421 | 0.7363 |
| 0.4838 | 6.0364 | 664 | 0.5001 | 0.7631 | 0.5001 | 0.7072 |
| 0.4838 | 6.0545 | 666 | 0.4797 | 0.7631 | 0.4797 | 0.6926 |
| 0.4838 | 6.0727 | 668 | 0.4970 | 0.7631 | 0.4970 | 0.7050 |
| 0.4838 | 6.0909 | 670 | 0.5244 | 0.7826 | 0.5244 | 0.7242 |
| 0.4838 | 6.1091 | 672 | 0.5706 | 0.7263 | 0.5706 | 0.7554 |
| 0.4838 | 6.1273 | 674 | 0.5913 | 0.7263 | 0.5913 | 0.7689 |
| 0.4838 | 6.1455 | 676 | 0.5412 | 0.7826 | 0.5412 | 0.7357 |
| 0.4838 | 6.1636 | 678 | 0.5203 | 0.7826 | 0.5203 | 0.7213 |
| 0.4838 | 6.1818 | 680 | 0.4814 | 0.7631 | 0.4814 | 0.6939 |
| 0.4838 | 6.2 | 682 | 0.4444 | 0.7336 | 0.4444 | 0.6666 |
| 0.4838 | 6.2182 | 684 | 0.4334 | 0.7336 | 0.4334 | 0.6584 |
| 0.4838 | 6.2364 | 686 | 0.4707 | 0.7667 | 0.4707 | 0.6860 |
| 0.4838 | 6.2545 | 688 | 0.5320 | 0.7826 | 0.5320 | 0.7294 |
| 0.4838 | 6.2727 | 690 | 0.5995 | 0.7455 | 0.5995 | 0.7743 |
| 0.4838 | 6.2909 | 692 | 0.6243 | 0.7258 | 0.6243 | 0.7901 |
| 0.4838 | 6.3091 | 694 | 0.6045 | 0.7098 | 0.6045 | 0.7775 |
| 0.4838 | 6.3273 | 696 | 0.5608 | 0.72 | 0.5608 | 0.7489 |
| 0.4838 | 6.3455 | 698 | 0.5414 | 0.7325 | 0.5414 | 0.7358 |
| 0.4838 | 6.3636 | 700 | 0.5529 | 0.7325 | 0.5529 | 0.7435 |
| 0.4838 | 6.3818 | 702 | 0.5965 | 0.7098 | 0.5965 | 0.7723 |
| 0.4838 | 6.4 | 704 | 0.6706 | 0.6971 | 0.6706 | 0.8189 |
| 0.4838 | 6.4182 | 706 | 0.7370 | 0.6441 | 0.7370 | 0.8585 |
| 0.4838 | 6.4364 | 708 | 0.7575 | 0.6441 | 0.7575 | 0.8703 |
| 0.4838 | 6.4545 | 710 | 0.7199 | 0.6441 | 0.7199 | 0.8485 |
| 0.4838 | 6.4727 | 712 | 0.6086 | 0.7366 | 0.6086 | 0.7802 |
| 0.4838 | 6.4909 | 714 | 0.5293 | 0.7826 | 0.5293 | 0.7275 |
| 0.4838 | 6.5091 | 716 | 0.5177 | 0.7826 | 0.5177 | 0.7195 |
| 0.4838 | 6.5273 | 718 | 0.5725 | 0.7219 | 0.5725 | 0.7566 |
| 0.4838 | 6.5455 | 720 | 0.6995 | 0.6182 | 0.6995 | 0.8364 |
| 0.4838 | 6.5636 | 722 | 0.7545 | 0.6051 | 0.7545 | 0.8686 |
| 0.4838 | 6.5818 | 724 | 0.7165 | 0.6121 | 0.7165 | 0.8465 |
| 0.4838 | 6.6 | 726 | 0.6366 | 0.6416 | 0.6366 | 0.7979 |
| 0.4838 | 6.6182 | 728 | 0.5293 | 0.6488 | 0.5293 | 0.7275 |
| 0.4838 | 6.6364 | 730 | 0.4817 | 0.7219 | 0.4817 | 0.6941 |
| 0.4838 | 6.6545 | 732 | 0.4699 | 0.7220 | 0.4699 | 0.6855 |
| 0.4838 | 6.6727 | 734 | 0.4662 | 0.7138 | 0.4662 | 0.6828 |
| 0.4838 | 6.6909 | 736 | 0.4683 | 0.7475 | 0.4683 | 0.6844 |
| 0.4838 | 6.7091 | 738 | 0.5236 | 0.7219 | 0.5236 | 0.7236 |
| 0.4838 | 6.7273 | 740 | 0.5954 | 0.7217 | 0.5954 | 0.7716 |
| 0.4838 | 6.7455 | 742 | 0.6516 | 0.6189 | 0.6516 | 0.8072 |
| 0.4838 | 6.7636 | 744 | 0.6477 | 0.6189 | 0.6477 | 0.8048 |
| 0.4838 | 6.7818 | 746 | 0.6045 | 0.7217 | 0.6045 | 0.7775 |
| 0.4838 | 6.8 | 748 | 0.5417 | 0.7475 | 0.5417 | 0.7360 |
| 0.4838 | 6.8182 | 750 | 0.4937 | 0.7524 | 0.4937 | 0.7026 |
| 0.4838 | 6.8364 | 752 | 0.4447 | 0.7524 | 0.4447 | 0.6668 |
| 0.4838 | 6.8545 | 754 | 0.4213 | 0.7921 | 0.4213 | 0.6491 |
| 0.4838 | 6.8727 | 756 | 0.4185 | 0.7921 | 0.4185 | 0.6469 |
| 0.4838 | 6.8909 | 758 | 0.4231 | 0.7524 | 0.4231 | 0.6504 |
| 0.4838 | 6.9091 | 760 | 0.4610 | 0.7524 | 0.4610 | 0.6790 |
| 0.4838 | 6.9273 | 762 | 0.5313 | 0.7524 | 0.5313 | 0.7289 |
| 0.4838 | 6.9455 | 764 | 0.6305 | 0.7093 | 0.6305 | 0.7940 |
| 0.4838 | 6.9636 | 766 | 0.7129 | 0.6196 | 0.7129 | 0.8443 |
| 0.4838 | 6.9818 | 768 | 0.7586 | 0.5684 | 0.7586 | 0.8710 |
| 0.4838 | 7.0 | 770 | 0.7272 | 0.5903 | 0.7272 | 0.8528 |
| 0.4838 | 7.0182 | 772 | 0.6557 | 0.6792 | 0.6557 | 0.8098 |
| 0.4838 | 7.0364 | 774 | 0.5712 | 0.7688 | 0.5712 | 0.7558 |
| 0.4838 | 7.0545 | 776 | 0.4901 | 0.7391 | 0.4901 | 0.7001 |
| 0.4838 | 7.0727 | 778 | 0.4314 | 0.7524 | 0.4314 | 0.6568 |
| 0.4838 | 7.0909 | 780 | 0.4081 | 0.7552 | 0.4081 | 0.6388 |
| 0.4838 | 7.1091 | 782 | 0.4013 | 0.7552 | 0.4013 | 0.6335 |
| 0.4838 | 7.1273 | 784 | 0.4149 | 0.7552 | 0.4149 | 0.6441 |
| 0.4838 | 7.1455 | 786 | 0.4538 | 0.7 | 0.4538 | 0.6737 |
| 0.4838 | 7.1636 | 788 | 0.5018 | 0.7342 | 0.5018 | 0.7084 |
| 0.4838 | 7.1818 | 790 | 0.5529 | 0.7342 | 0.5529 | 0.7436 |
| 0.4838 | 7.2 | 792 | 0.5802 | 0.6975 | 0.5802 | 0.7617 |
| 0.4838 | 7.2182 | 794 | 0.5792 | 0.6975 | 0.5792 | 0.7610 |
| 0.4838 | 7.2364 | 796 | 0.5418 | 0.7391 | 0.5418 | 0.7361 |
| 0.4838 | 7.2545 | 798 | 0.5114 | 0.7391 | 0.5114 | 0.7151 |
| 0.4838 | 7.2727 | 800 | 0.4964 | 0.7524 | 0.4964 | 0.7046 |
| 0.4838 | 7.2909 | 802 | 0.5118 | 0.7524 | 0.5118 | 0.7154 |
| 0.4838 | 7.3091 | 804 | 0.5514 | 0.7273 | 0.5514 | 0.7426 |
| 0.4838 | 7.3273 | 806 | 0.5875 | 0.6020 | 0.5875 | 0.7665 |
| 0.4838 | 7.3455 | 808 | 0.5793 | 0.6111 | 0.5793 | 0.7611 |
| 0.4838 | 7.3636 | 810 | 0.5629 | 0.6111 | 0.5629 | 0.7503 |
| 0.4838 | 7.3818 | 812 | 0.5381 | 0.6111 | 0.5381 | 0.7336 |
| 0.4838 | 7.4 | 814 | 0.5486 | 0.6873 | 0.5486 | 0.7407 |
| 0.4838 | 7.4182 | 816 | 0.5396 | 0.7143 | 0.5396 | 0.7346 |
| 0.4838 | 7.4364 | 818 | 0.5153 | 0.6936 | 0.5153 | 0.7179 |
| 0.4838 | 7.4545 | 820 | 0.4964 | 0.7325 | 0.4964 | 0.7045 |
| 0.4838 | 7.4727 | 822 | 0.5048 | 0.7325 | 0.5048 | 0.7105 |
| 0.4838 | 7.4909 | 824 | 0.5367 | 0.7325 | 0.5367 | 0.7326 |
| 0.4838 | 7.5091 | 826 | 0.5959 | 0.7030 | 0.5959 | 0.7719 |
| 0.4838 | 7.5273 | 828 | 0.6654 | 0.6648 | 0.6654 | 0.8157 |
| 0.4838 | 7.5455 | 830 | 0.7364 | 0.6885 | 0.7364 | 0.8581 |
| 0.4838 | 7.5636 | 832 | 0.7945 | 0.6176 | 0.7945 | 0.8913 |
| 0.4838 | 7.5818 | 834 | 0.7895 | 0.6176 | 0.7895 | 0.8886 |
| 0.4838 | 7.6 | 836 | 0.7301 | 0.6753 | 0.7301 | 0.8545 |
| 0.4838 | 7.6182 | 838 | 0.6401 | 0.7030 | 0.6401 | 0.8001 |
| 0.4838 | 7.6364 | 840 | 0.5769 | 0.7030 | 0.5769 | 0.7595 |
| 0.4838 | 7.6545 | 842 | 0.5481 | 0.7016 | 0.5481 | 0.7403 |
| 0.4838 | 7.6727 | 844 | 0.5337 | 0.7391 | 0.5337 | 0.7306 |
| 0.4838 | 7.6909 | 846 | 0.5285 | 0.7391 | 0.5285 | 0.7270 |
| 0.4838 | 7.7091 | 848 | 0.5204 | 0.7391 | 0.5204 | 0.7214 |
| 0.4838 | 7.7273 | 850 | 0.4882 | 0.7325 | 0.4882 | 0.6987 |
| 0.4838 | 7.7455 | 852 | 0.4735 | 0.7325 | 0.4735 | 0.6881 |
| 0.4838 | 7.7636 | 854 | 0.4806 | 0.7325 | 0.4806 | 0.6932 |
| 0.4838 | 7.7818 | 856 | 0.5037 | 0.7325 | 0.5037 | 0.7097 |
| 0.4838 | 7.8 | 858 | 0.5572 | 0.7391 | 0.5572 | 0.7464 |
| 0.4838 | 7.8182 | 860 | 0.6360 | 0.7258 | 0.6360 | 0.7975 |
| 0.4838 | 7.8364 | 862 | 0.7201 | 0.6971 | 0.7201 | 0.8486 |
| 0.4838 | 7.8545 | 864 | 0.7719 | 0.6984 | 0.7719 | 0.8786 |
| 0.4838 | 7.8727 | 866 | 0.7781 | 0.6984 | 0.7781 | 0.8821 |
| 0.4838 | 7.8909 | 868 | 0.7553 | 0.6971 | 0.7553 | 0.8691 |
| 0.4838 | 7.9091 | 870 | 0.6841 | 0.6971 | 0.6841 | 0.8271 |
| 0.4838 | 7.9273 | 872 | 0.5957 | 0.7558 | 0.5957 | 0.7718 |
| 0.4838 | 7.9455 | 874 | 0.5156 | 0.7524 | 0.5156 | 0.7180 |
| 0.4838 | 7.9636 | 876 | 0.4807 | 0.7524 | 0.4807 | 0.6933 |
| 0.4838 | 7.9818 | 878 | 0.4556 | 0.7336 | 0.4556 | 0.6750 |
| 0.4838 | 8.0 | 880 | 0.4408 | 0.7336 | 0.4408 | 0.6640 |
| 0.4838 | 8.0182 | 882 | 0.4394 | 0.7552 | 0.4394 | 0.6629 |
| 0.4838 | 8.0364 | 884 | 0.4569 | 0.7552 | 0.4569 | 0.6759 |
| 0.4838 | 8.0545 | 886 | 0.5009 | 0.7524 | 0.5009 | 0.7077 |
| 0.4838 | 8.0727 | 888 | 0.5391 | 0.7391 | 0.5391 | 0.7342 |
| 0.4838 | 8.0909 | 890 | 0.5757 | 0.7391 | 0.5757 | 0.7587 |
| 0.4838 | 8.1091 | 892 | 0.5913 | 0.7267 | 0.5913 | 0.7689 |
| 0.4838 | 8.1273 | 894 | 0.5967 | 0.7267 | 0.5967 | 0.7725 |
| 0.4838 | 8.1455 | 896 | 0.5871 | 0.7267 | 0.5871 | 0.7662 |
| 0.4838 | 8.1636 | 898 | 0.5704 | 0.7391 | 0.5704 | 0.7552 |
| 0.4838 | 8.1818 | 900 | 0.5615 | 0.7391 | 0.5615 | 0.7493 |
| 0.4838 | 8.2 | 902 | 0.5582 | 0.7391 | 0.5582 | 0.7471 |
| 0.4838 | 8.2182 | 904 | 0.5633 | 0.7391 | 0.5633 | 0.7505 |
| 0.4838 | 8.2364 | 906 | 0.5615 | 0.7391 | 0.5615 | 0.7493 |
| 0.4838 | 8.2545 | 908 | 0.5562 | 0.7391 | 0.5562 | 0.7458 |
| 0.4838 | 8.2727 | 910 | 0.5524 | 0.7524 | 0.5524 | 0.7432 |
| 0.4838 | 8.2909 | 912 | 0.5410 | 0.7524 | 0.5410 | 0.7355 |
| 0.4838 | 8.3091 | 914 | 0.5451 | 0.7524 | 0.5451 | 0.7383 |
| 0.4838 | 8.3273 | 916 | 0.5635 | 0.7391 | 0.5635 | 0.7507 |
| 0.4838 | 8.3455 | 918 | 0.5882 | 0.7391 | 0.5882 | 0.7670 |
| 0.4838 | 8.3636 | 920 | 0.6197 | 0.7263 | 0.6197 | 0.7872 |
| 0.4838 | 8.3818 | 922 | 0.6634 | 0.7160 | 0.6634 | 0.8145 |
| 0.4838 | 8.4 | 924 | 0.6927 | 0.7160 | 0.6927 | 0.8323 |
| 0.4838 | 8.4182 | 926 | 0.7305 | 0.7160 | 0.7305 | 0.8547 |
| 0.4838 | 8.4364 | 928 | 0.7597 | 0.6971 | 0.7597 | 0.8716 |
| 0.4838 | 8.4545 | 930 | 0.7383 | 0.7160 | 0.7383 | 0.8593 |
| 0.4838 | 8.4727 | 932 | 0.7025 | 0.7160 | 0.7025 | 0.8382 |
| 0.4838 | 8.4909 | 934 | 0.6436 | 0.7255 | 0.6436 | 0.8023 |
| 0.4838 | 8.5091 | 936 | 0.5837 | 0.7378 | 0.5837 | 0.7640 |
| 0.4838 | 8.5273 | 938 | 0.5188 | 0.7524 | 0.5188 | 0.7203 |
| 0.4838 | 8.5455 | 940 | 0.4726 | 0.7325 | 0.4726 | 0.6875 |
| 0.4838 | 8.5636 | 942 | 0.4543 | 0.7325 | 0.4543 | 0.6740 |
| 0.4838 | 8.5818 | 944 | 0.4403 | 0.7986 | 0.4403 | 0.6636 |
| 0.4838 | 8.6 | 946 | 0.4322 | 0.7986 | 0.4322 | 0.6574 |
| 0.4838 | 8.6182 | 948 | 0.4370 | 0.7986 | 0.4370 | 0.6610 |
| 0.4838 | 8.6364 | 950 | 0.4451 | 0.7336 | 0.4451 | 0.6672 |
| 0.4838 | 8.6545 | 952 | 0.4522 | 0.7336 | 0.4522 | 0.6725 |
| 0.4838 | 8.6727 | 954 | 0.4599 | 0.7325 | 0.4599 | 0.6781 |
| 0.4838 | 8.6909 | 956 | 0.4737 | 0.7325 | 0.4737 | 0.6883 |
| 0.4838 | 8.7091 | 958 | 0.4905 | 0.7325 | 0.4905 | 0.7003 |
| 0.4838 | 8.7273 | 960 | 0.5064 | 0.7524 | 0.5064 | 0.7116 |
| 0.4838 | 8.7455 | 962 | 0.5094 | 0.7524 | 0.5094 | 0.7138 |
| 0.4838 | 8.7636 | 964 | 0.5140 | 0.7524 | 0.5140 | 0.7169 |
| 0.4838 | 8.7818 | 966 | 0.5250 | 0.7524 | 0.5250 | 0.7246 |
| 0.4838 | 8.8 | 968 | 0.5361 | 0.7524 | 0.5361 | 0.7322 |
| 0.4838 | 8.8182 | 970 | 0.5392 | 0.7524 | 0.5392 | 0.7343 |
| 0.4838 | 8.8364 | 972 | 0.5311 | 0.7524 | 0.5311 | 0.7288 |
| 0.4838 | 8.8545 | 974 | 0.5382 | 0.7524 | 0.5382 | 0.7337 |
| 0.4838 | 8.8727 | 976 | 0.5481 | 0.7391 | 0.5481 | 0.7404 |
| 0.4838 | 8.8909 | 978 | 0.5489 | 0.7391 | 0.5489 | 0.7409 |
| 0.4838 | 8.9091 | 980 | 0.5585 | 0.7378 | 0.5585 | 0.7473 |
| 0.4838 | 8.9273 | 982 | 0.5761 | 0.7378 | 0.5761 | 0.7590 |
| 0.4838 | 8.9455 | 984 | 0.5847 | 0.7378 | 0.5847 | 0.7646 |
| 0.4838 | 8.9636 | 986 | 0.5903 | 0.7378 | 0.5903 | 0.7683 |
| 0.4838 | 8.9818 | 988 | 0.5947 | 0.7378 | 0.5947 | 0.7712 |
| 0.4838 | 9.0 | 990 | 0.6019 | 0.7378 | 0.6019 | 0.7758 |
| 0.4838 | 9.0182 | 992 | 0.6093 | 0.6789 | 0.6093 | 0.7806 |
| 0.4838 | 9.0364 | 994 | 0.6049 | 0.6789 | 0.6049 | 0.7778 |
| 0.4838 | 9.0545 | 996 | 0.5875 | 0.7030 | 0.5875 | 0.7665 |
| 0.4838 | 9.0727 | 998 | 0.5631 | 0.7143 | 0.5631 | 0.7504 |
| 0.0968 | 9.0909 | 1000 | 0.5477 | 0.7143 | 0.5477 | 0.7401 |
| 0.0968 | 9.1091 | 1002 | 0.5317 | 0.7143 | 0.5317 | 0.7292 |
| 0.0968 | 9.1273 | 1004 | 0.5291 | 0.7143 | 0.5291 | 0.7274 |
| 0.0968 | 9.1455 | 1006 | 0.5234 | 0.7143 | 0.5234 | 0.7235 |
| 0.0968 | 9.1636 | 1008 | 0.5219 | 0.7143 | 0.5219 | 0.7225 |
| 0.0968 | 9.1818 | 1010 | 0.5277 | 0.7143 | 0.5277 | 0.7265 |
| 0.0968 | 9.2 | 1012 | 0.5358 | 0.7143 | 0.5358 | 0.7320 |
| 0.0968 | 9.2182 | 1014 | 0.5376 | 0.7524 | 0.5376 | 0.7332 |
| 0.0968 | 9.2364 | 1016 | 0.5445 | 0.7524 | 0.5445 | 0.7379 |
| 0.0968 | 9.2545 | 1018 | 0.5549 | 0.7524 | 0.5549 | 0.7449 |
| 0.0968 | 9.2727 | 1020 | 0.5593 | 0.7391 | 0.5593 | 0.7479 |
| 0.0968 | 9.2909 | 1022 | 0.5669 | 0.7391 | 0.5669 | 0.7530 |
| 0.0968 | 9.3091 | 1024 | 0.5753 | 0.7378 | 0.5753 | 0.7585 |
| 0.0968 | 9.3273 | 1026 | 0.5872 | 0.7378 | 0.5872 | 0.7663 |
| 0.0968 | 9.3455 | 1028 | 0.5954 | 0.7378 | 0.5954 | 0.7716 |
| 0.0968 | 9.3636 | 1030 | 0.6041 | 0.7378 | 0.6041 | 0.7773 |
| 0.0968 | 9.3818 | 1032 | 0.6156 | 0.7151 | 0.6156 | 0.7846 |
| 0.0968 | 9.4 | 1034 | 0.6312 | 0.7151 | 0.6312 | 0.7945 |
| 0.0968 | 9.4182 | 1036 | 0.6402 | 0.7151 | 0.6402 | 0.8001 |
| 0.0968 | 9.4364 | 1038 | 0.6421 | 0.7053 | 0.6421 | 0.8013 |
| 0.0968 | 9.4545 | 1040 | 0.6404 | 0.7053 | 0.6404 | 0.8003 |
| 0.0968 | 9.4727 | 1042 | 0.6342 | 0.7437 | 0.6342 | 0.7964 |
| 0.0968 | 9.4909 | 1044 | 0.6283 | 0.7151 | 0.6283 | 0.7927 |
| 0.0968 | 9.5091 | 1046 | 0.6205 | 0.7151 | 0.6205 | 0.7877 |
| 0.0968 | 9.5273 | 1048 | 0.6186 | 0.7151 | 0.6186 | 0.7865 |
| 0.0968 | 9.5455 | 1050 | 0.6111 | 0.7378 | 0.6111 | 0.7817 |
| 0.0968 | 9.5636 | 1052 | 0.6019 | 0.7378 | 0.6019 | 0.7758 |
| 0.0968 | 9.5818 | 1054 | 0.5924 | 0.7378 | 0.5924 | 0.7696 |
| 0.0968 | 9.6 | 1056 | 0.5803 | 0.7378 | 0.5803 | 0.7618 |
| 0.0968 | 9.6182 | 1058 | 0.5686 | 0.7391 | 0.5686 | 0.7541 |
| 0.0968 | 9.6364 | 1060 | 0.5568 | 0.7524 | 0.5568 | 0.7462 |
| 0.0968 | 9.6545 | 1062 | 0.5488 | 0.7524 | 0.5488 | 0.7408 |
| 0.0968 | 9.6727 | 1064 | 0.5413 | 0.7524 | 0.5413 | 0.7357 |
| 0.0968 | 9.6909 | 1066 | 0.5366 | 0.7524 | 0.5366 | 0.7325 |
| 0.0968 | 9.7091 | 1068 | 0.5370 | 0.7524 | 0.5370 | 0.7328 |
| 0.0968 | 9.7273 | 1070 | 0.5391 | 0.7524 | 0.5391 | 0.7343 |
| 0.0968 | 9.7455 | 1072 | 0.5411 | 0.7524 | 0.5411 | 0.7356 |
| 0.0968 | 9.7636 | 1074 | 0.5417 | 0.7524 | 0.5417 | 0.7360 |
| 0.0968 | 9.7818 | 1076 | 0.5441 | 0.7524 | 0.5441 | 0.7376 |
| 0.0968 | 9.8 | 1078 | 0.5465 | 0.7524 | 0.5465 | 0.7393 |
| 0.0968 | 9.8182 | 1080 | 0.5501 | 0.7524 | 0.5501 | 0.7417 |
| 0.0968 | 9.8364 | 1082 | 0.5529 | 0.7524 | 0.5529 | 0.7436 |
| 0.0968 | 9.8545 | 1084 | 0.5540 | 0.7524 | 0.5540 | 0.7443 |
| 0.0968 | 9.8727 | 1086 | 0.5556 | 0.7524 | 0.5556 | 0.7454 |
| 0.0968 | 9.8909 | 1088 | 0.5576 | 0.7524 | 0.5576 | 0.7468 |
| 0.0968 | 9.9091 | 1090 | 0.5604 | 0.7524 | 0.5604 | 0.7486 |
| 0.0968 | 9.9273 | 1092 | 0.5633 | 0.7524 | 0.5633 | 0.7505 |
| 0.0968 | 9.9455 | 1094 | 0.5649 | 0.7524 | 0.5649 | 0.7516 |
| 0.0968 | 9.9636 | 1096 | 0.5657 | 0.7524 | 0.5657 | 0.7521 |
| 0.0968 | 9.9818 | 1098 | 0.5659 | 0.7524 | 0.5659 | 0.7523 |
| 0.0968 | 10.0 | 1100 | 0.5659 | 0.7524 | 0.5659 | 0.7523 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
mradermacher/SauerkrautLM-Qwen-32b-GGUF
|
mradermacher
| 2024-11-27T21:00:10Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"sft",
"dpo",
"de",
"en",
"base_model:VAGOsolutions/SauerkrautLM-Qwen-32b",
"base_model:quantized:VAGOsolutions/SauerkrautLM-Qwen-32b",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T18:20:04Z |
---
base_model: VAGOsolutions/SauerkrautLM-Qwen-32b
language:
- de
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-32B/blob/main/LICENSE
license_name: tongyi-qianwen-research
quantized_by: mradermacher
tags:
- sft
- dpo
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/VAGOsolutions/SauerkrautLM-Qwen-32b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q2_K.gguf) | Q2_K | 12.3 | |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q3_K_S.gguf) | Q3_K_S | 14.4 | |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q3_K_L.gguf) | Q3_K_L | 17.2 | |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.IQ4_XS.gguf) | IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q4_K_S.gguf) | Q4_K_S | 18.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q4_K_M.gguf) | Q4_K_M | 19.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q5_K_S.gguf) | Q5_K_S | 22.6 | |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q5_K_M.gguf) | Q5_K_M | 23.2 | |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SauerkrautLM-Qwen-32b-GGUF/resolve/main/SauerkrautLM-Qwen-32b.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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
fbaldassarri/EleutherAI_pythia-70m-autoawq-int4-gs128-sym
|
fbaldassarri
| 2024-11-27T20:54:48Z | 5 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel",
"intel-autoround",
"awq",
"autoawq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-70m",
"base_model:quantized:EleutherAI/pythia-70m",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:54:24Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- intel-autoround
- awq
- autoawq
- woq
license: apache-2.0
model_name: Pythia 70m
base_model: EleutherAI/pythia-70m
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-70m](EleutherAI/pythia-70m) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Symmetrical Quantization
- Method AutoAWQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-70m has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel Autoround wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-70m"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, True
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-70m-autoawq-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
|
fbaldassarri/EleutherAI_pythia-70m-autoawq-int4-gs128-asym
|
fbaldassarri
| 2024-11-27T20:54:08Z | 5 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel",
"intel-autoround",
"awq",
"autoawq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-70m",
"base_model:quantized:EleutherAI/pythia-70m",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:53:43Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- intel-autoround
- awq
- autoawq
- woq
license: apache-2.0
model_name: Pythia 70m
base_model: EleutherAI/pythia-70m
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-70m](EleutherAI/pythia-70m) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Asymmetrical Quantization
- Method AutoAWQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-70m has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel Autoround wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-70m"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, False
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-70m-autoawq-int4-gs128-asym"
autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
|
xw17/SmolLM-1.7B-Instruct_finetuned_s03
|
xw17
| 2024-11-27T20:52:15Z | 139 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T20:50:08Z |
---
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 π€ 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]
|
MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k10_task1_organization_fold0
|
MayBashendy
| 2024-11-27T20:51:14Z | 163 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T20:41:13Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_run2_AugV5_k10_task1_organization_fold0
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. -->
# Arabic_FineTuningAraBERT_run2_AugV5_k10_task1_organization_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6480
- Qwk: 0.7250
- Mse: 0.6480
- Rmse: 0.8050
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0182 | 2 | 5.6135 | -0.1511 | 5.6135 | 2.3693 |
| No log | 0.0364 | 4 | 2.9748 | 0.1466 | 2.9748 | 1.7248 |
| No log | 0.0545 | 6 | 2.1717 | -0.0383 | 2.1717 | 1.4737 |
| No log | 0.0727 | 8 | 1.8008 | 0.0 | 1.8008 | 1.3419 |
| No log | 0.0909 | 10 | 1.6908 | 0.0 | 1.6908 | 1.3003 |
| No log | 0.1091 | 12 | 2.0295 | 0.0742 | 2.0295 | 1.4246 |
| No log | 0.1273 | 14 | 2.0837 | 0.2025 | 2.0837 | 1.4435 |
| No log | 0.1455 | 16 | 2.0125 | 0.1414 | 2.0125 | 1.4186 |
| No log | 0.1636 | 18 | 1.8748 | 0.0742 | 1.8748 | 1.3692 |
| No log | 0.1818 | 20 | 1.4777 | 0.0 | 1.4777 | 1.2156 |
| No log | 0.2 | 22 | 1.4816 | 0.0758 | 1.4816 | 1.2172 |
| No log | 0.2182 | 24 | 2.3726 | -0.2640 | 2.3726 | 1.5403 |
| No log | 0.2364 | 26 | 2.7265 | -0.2998 | 2.7265 | 1.6512 |
| No log | 0.2545 | 28 | 2.7074 | -0.2674 | 2.7074 | 1.6454 |
| No log | 0.2727 | 30 | 2.0260 | 0.0 | 2.0260 | 1.4234 |
| No log | 0.2909 | 32 | 1.4412 | 0.2241 | 1.4412 | 1.2005 |
| No log | 0.3091 | 34 | 1.2764 | 0.5333 | 1.2764 | 1.1298 |
| No log | 0.3273 | 36 | 1.3047 | 0.4324 | 1.3047 | 1.1422 |
| No log | 0.3455 | 38 | 1.3350 | 0.2546 | 1.3350 | 1.1554 |
| No log | 0.3636 | 40 | 1.3931 | 0.2546 | 1.3931 | 1.1803 |
| No log | 0.3818 | 42 | 1.5888 | 0.0742 | 1.5888 | 1.2605 |
| No log | 0.4 | 44 | 1.8075 | 0.0888 | 1.8075 | 1.3444 |
| No log | 0.4182 | 46 | 1.6927 | 0.0696 | 1.6927 | 1.3010 |
| No log | 0.4364 | 48 | 1.5639 | 0.0 | 1.5639 | 1.2506 |
| No log | 0.4545 | 50 | 1.5721 | 0.0244 | 1.5721 | 1.2538 |
| No log | 0.4727 | 52 | 1.5705 | 0.1004 | 1.5705 | 1.2532 |
| No log | 0.4909 | 54 | 1.4864 | 0.0221 | 1.4864 | 1.2192 |
| No log | 0.5091 | 56 | 1.4775 | 0.0221 | 1.4775 | 1.2155 |
| No log | 0.5273 | 58 | 1.6388 | 0.0 | 1.6388 | 1.2801 |
| No log | 0.5455 | 60 | 1.7486 | 0.0 | 1.7486 | 1.3224 |
| No log | 0.5636 | 62 | 2.1618 | 0.0742 | 2.1618 | 1.4703 |
| No log | 0.5818 | 64 | 2.1288 | 0.0742 | 2.1288 | 1.4590 |
| No log | 0.6 | 66 | 1.7456 | 0.0 | 1.7456 | 1.3212 |
| No log | 0.6182 | 68 | 1.4724 | 0.0758 | 1.4724 | 1.2134 |
| No log | 0.6364 | 70 | 1.4161 | 0.2546 | 1.4161 | 1.1900 |
| No log | 0.6545 | 72 | 1.4192 | 0.3558 | 1.4192 | 1.1913 |
| No log | 0.6727 | 74 | 1.3811 | 0.3808 | 1.3811 | 1.1752 |
| No log | 0.6909 | 76 | 1.5513 | 0.0 | 1.5513 | 1.2455 |
| No log | 0.7091 | 78 | 1.7219 | 0.0 | 1.7219 | 1.3122 |
| No log | 0.7273 | 80 | 1.8389 | 0.0742 | 1.8389 | 1.3561 |
| No log | 0.7455 | 82 | 2.1635 | 0.1414 | 2.1635 | 1.4709 |
| No log | 0.7636 | 84 | 2.3203 | 0.1582 | 2.3203 | 1.5233 |
| No log | 0.7818 | 86 | 2.2337 | 0.0742 | 2.2337 | 1.4945 |
| No log | 0.8 | 88 | 2.0261 | 0.0742 | 2.0261 | 1.4234 |
| No log | 0.8182 | 90 | 1.8600 | 0.0 | 1.8600 | 1.3638 |
| No log | 0.8364 | 92 | 1.7712 | 0.0 | 1.7712 | 1.3309 |
| No log | 0.8545 | 94 | 1.6475 | 0.0 | 1.6475 | 1.2835 |
| No log | 0.8727 | 96 | 1.5194 | 0.0 | 1.5194 | 1.2326 |
| No log | 0.8909 | 98 | 1.4464 | 0.0 | 1.4464 | 1.2027 |
| No log | 0.9091 | 100 | 1.3747 | 0.1271 | 1.3747 | 1.1725 |
| No log | 0.9273 | 102 | 1.3468 | 0.2794 | 1.3468 | 1.1605 |
| No log | 0.9455 | 104 | 1.3176 | 0.3558 | 1.3176 | 1.1479 |
| No log | 0.9636 | 106 | 1.3132 | 0.3309 | 1.3132 | 1.1459 |
| No log | 0.9818 | 108 | 1.3306 | 0.3309 | 1.3306 | 1.1535 |
| No log | 1.0 | 110 | 1.3292 | 0.1271 | 1.3292 | 1.1529 |
| No log | 1.0182 | 112 | 1.3701 | 0.1271 | 1.3701 | 1.1705 |
| No log | 1.0364 | 114 | 1.3992 | 0.1271 | 1.3992 | 1.1829 |
| No log | 1.0545 | 116 | 1.4616 | 0.0 | 1.4616 | 1.2090 |
| No log | 1.0727 | 118 | 1.4722 | 0.0742 | 1.4722 | 1.2134 |
| No log | 1.0909 | 120 | 1.5968 | 0.0742 | 1.5968 | 1.2637 |
| No log | 1.1091 | 122 | 1.6775 | 0.0742 | 1.6775 | 1.2952 |
| No log | 1.1273 | 124 | 1.6381 | 0.0742 | 1.6381 | 1.2799 |
| No log | 1.1455 | 126 | 1.4633 | 0.0742 | 1.4633 | 1.2097 |
| No log | 1.1636 | 128 | 1.3449 | 0.3163 | 1.3449 | 1.1597 |
| No log | 1.1818 | 130 | 1.1907 | 0.4615 | 1.1907 | 1.0912 |
| No log | 1.2 | 132 | 1.1034 | 0.4310 | 1.1034 | 1.0504 |
| No log | 1.2182 | 134 | 1.0903 | 0.4815 | 1.0903 | 1.0442 |
| No log | 1.2364 | 136 | 1.0546 | 0.4296 | 1.0546 | 1.0269 |
| No log | 1.2545 | 138 | 1.0184 | 0.4562 | 1.0184 | 1.0092 |
| No log | 1.2727 | 140 | 1.0093 | 0.4310 | 1.0093 | 1.0047 |
| No log | 1.2909 | 142 | 1.1076 | 0.4603 | 1.1076 | 1.0524 |
| No log | 1.3091 | 144 | 1.1147 | 0.4627 | 1.1147 | 1.0558 |
| No log | 1.3273 | 146 | 1.0640 | 0.5305 | 1.0640 | 1.0315 |
| No log | 1.3455 | 148 | 0.8893 | 0.4830 | 0.8893 | 0.9430 |
| No log | 1.3636 | 150 | 0.8543 | 0.4830 | 0.8543 | 0.9243 |
| No log | 1.3818 | 152 | 1.0107 | 0.4535 | 1.0107 | 1.0053 |
| No log | 1.4 | 154 | 0.9911 | 0.5059 | 0.9911 | 0.9955 |
| No log | 1.4182 | 156 | 0.9366 | 0.4085 | 0.9366 | 0.9678 |
| No log | 1.4364 | 158 | 1.0078 | 0.5514 | 1.0078 | 1.0039 |
| No log | 1.4545 | 160 | 1.1332 | 0.5297 | 1.1332 | 1.0645 |
| No log | 1.4727 | 162 | 1.1351 | 0.5305 | 1.1351 | 1.0654 |
| No log | 1.4909 | 164 | 1.1337 | 0.5706 | 1.1337 | 1.0648 |
| No log | 1.5091 | 166 | 1.0233 | 0.5082 | 1.0233 | 1.0116 |
| No log | 1.5273 | 168 | 0.9164 | 0.3511 | 0.9164 | 0.9573 |
| No log | 1.5455 | 170 | 0.8685 | 0.4565 | 0.8685 | 0.9319 |
| No log | 1.5636 | 172 | 0.8642 | 0.5944 | 0.8642 | 0.9296 |
| No log | 1.5818 | 174 | 0.8668 | 0.6272 | 0.8668 | 0.9310 |
| No log | 1.6 | 176 | 0.9069 | 0.6272 | 0.9069 | 0.9523 |
| No log | 1.6182 | 178 | 0.9547 | 0.6812 | 0.9547 | 0.9771 |
| No log | 1.6364 | 180 | 0.9422 | 0.5785 | 0.9422 | 0.9707 |
| No log | 1.6545 | 182 | 0.9662 | 0.6296 | 0.9662 | 0.9830 |
| No log | 1.6727 | 184 | 0.9008 | 0.5973 | 0.9008 | 0.9491 |
| No log | 1.6909 | 186 | 0.8106 | 0.5785 | 0.8106 | 0.9004 |
| No log | 1.7091 | 188 | 0.9027 | 0.5845 | 0.9027 | 0.9501 |
| No log | 1.7273 | 190 | 1.0008 | 0.5266 | 1.0008 | 1.0004 |
| No log | 1.7455 | 192 | 0.9525 | 0.5266 | 0.9525 | 0.9759 |
| No log | 1.7636 | 194 | 0.8119 | 0.7286 | 0.8119 | 0.9010 |
| No log | 1.7818 | 196 | 0.7477 | 0.6476 | 0.7477 | 0.8647 |
| No log | 1.8 | 198 | 1.0567 | 0.5889 | 1.0567 | 1.0280 |
| No log | 1.8182 | 200 | 1.3755 | 0.4979 | 1.3755 | 1.1728 |
| No log | 1.8364 | 202 | 1.2883 | 0.4979 | 1.2883 | 1.1350 |
| No log | 1.8545 | 204 | 0.9558 | 0.6545 | 0.9558 | 0.9777 |
| No log | 1.8727 | 206 | 0.7330 | 0.6550 | 0.7330 | 0.8562 |
| No log | 1.8909 | 208 | 0.7958 | 0.5304 | 0.7958 | 0.8921 |
| No log | 1.9091 | 210 | 0.8084 | 0.5312 | 0.8084 | 0.8991 |
| No log | 1.9273 | 212 | 0.7720 | 0.5070 | 0.7720 | 0.8786 |
| No log | 1.9455 | 214 | 0.7582 | 0.5723 | 0.7582 | 0.8708 |
| No log | 1.9636 | 216 | 0.7952 | 0.6550 | 0.7952 | 0.8917 |
| No log | 1.9818 | 218 | 0.8436 | 0.6543 | 0.8436 | 0.9185 |
| No log | 2.0 | 220 | 0.9346 | 0.6115 | 0.9346 | 0.9667 |
| No log | 2.0182 | 222 | 0.9841 | 0.5349 | 0.9841 | 0.9920 |
| No log | 2.0364 | 224 | 0.9622 | 0.5349 | 0.9622 | 0.9809 |
| No log | 2.0545 | 226 | 0.9362 | 0.5532 | 0.9362 | 0.9676 |
| No log | 2.0727 | 228 | 0.8518 | 0.6860 | 0.8518 | 0.9229 |
| No log | 2.0909 | 230 | 0.8016 | 0.6363 | 0.8016 | 0.8953 |
| No log | 2.1091 | 232 | 0.7914 | 0.6309 | 0.7914 | 0.8896 |
| No log | 2.1273 | 234 | 0.8116 | 0.6309 | 0.8116 | 0.9009 |
| No log | 2.1455 | 236 | 0.8104 | 0.6309 | 0.8104 | 0.9002 |
| No log | 2.1636 | 238 | 0.7981 | 0.6309 | 0.7981 | 0.8934 |
| No log | 2.1818 | 240 | 0.7757 | 0.6309 | 0.7757 | 0.8807 |
| No log | 2.2 | 242 | 0.7730 | 0.6309 | 0.7730 | 0.8792 |
| No log | 2.2182 | 244 | 0.7720 | 0.6309 | 0.7720 | 0.8786 |
| No log | 2.2364 | 246 | 0.7567 | 0.6309 | 0.7567 | 0.8699 |
| No log | 2.2545 | 248 | 0.7562 | 0.6309 | 0.7562 | 0.8696 |
| No log | 2.2727 | 250 | 0.7996 | 0.6309 | 0.7996 | 0.8942 |
| No log | 2.2909 | 252 | 0.9173 | 0.5869 | 0.9173 | 0.9578 |
| No log | 2.3091 | 254 | 0.9375 | 0.6141 | 0.9375 | 0.9682 |
| No log | 2.3273 | 256 | 0.9315 | 0.6425 | 0.9315 | 0.9652 |
| No log | 2.3455 | 258 | 0.8979 | 0.6639 | 0.8979 | 0.9476 |
| No log | 2.3636 | 260 | 0.9088 | 0.6595 | 0.9088 | 0.9533 |
| No log | 2.3818 | 262 | 0.9262 | 0.6639 | 0.9262 | 0.9624 |
| No log | 2.4 | 264 | 0.9821 | 0.6425 | 0.9821 | 0.9910 |
| No log | 2.4182 | 266 | 0.9554 | 0.6468 | 0.9554 | 0.9774 |
| No log | 2.4364 | 268 | 0.9448 | 0.6514 | 0.9448 | 0.9720 |
| No log | 2.4545 | 270 | 0.9548 | 0.6260 | 0.9548 | 0.9771 |
| No log | 2.4727 | 272 | 1.0103 | 0.5869 | 1.0103 | 1.0051 |
| No log | 2.4909 | 274 | 0.9017 | 0.6260 | 0.9017 | 0.9496 |
| No log | 2.5091 | 276 | 0.7398 | 0.6309 | 0.7398 | 0.8601 |
| No log | 2.5273 | 278 | 0.6730 | 0.7782 | 0.6730 | 0.8203 |
| No log | 2.5455 | 280 | 0.6906 | 0.7529 | 0.6906 | 0.8310 |
| No log | 2.5636 | 282 | 0.6811 | 0.7337 | 0.6811 | 0.8253 |
| No log | 2.5818 | 284 | 0.6449 | 0.7520 | 0.6449 | 0.8031 |
| No log | 2.6 | 286 | 0.6826 | 0.6897 | 0.6826 | 0.8262 |
| No log | 2.6182 | 288 | 0.7020 | 0.6897 | 0.7020 | 0.8379 |
| No log | 2.6364 | 290 | 0.6670 | 0.6897 | 0.6670 | 0.8167 |
| No log | 2.6545 | 292 | 0.6703 | 0.6897 | 0.6703 | 0.8187 |
| No log | 2.6727 | 294 | 0.6459 | 0.6752 | 0.6459 | 0.8037 |
| No log | 2.6909 | 296 | 0.6644 | 0.6266 | 0.6644 | 0.8151 |
| No log | 2.7091 | 298 | 0.7477 | 0.6791 | 0.7477 | 0.8647 |
| No log | 2.7273 | 300 | 0.8486 | 0.5862 | 0.8486 | 0.9212 |
| No log | 2.7455 | 302 | 0.8313 | 0.5862 | 0.8313 | 0.9117 |
| No log | 2.7636 | 304 | 0.7265 | 0.6219 | 0.7265 | 0.8523 |
| No log | 2.7818 | 306 | 0.6980 | 0.6514 | 0.6980 | 0.8354 |
| No log | 2.8 | 308 | 0.6772 | 0.6851 | 0.6772 | 0.8229 |
| No log | 2.8182 | 310 | 0.6724 | 0.6799 | 0.6724 | 0.8200 |
| No log | 2.8364 | 312 | 0.6859 | 0.6745 | 0.6859 | 0.8282 |
| No log | 2.8545 | 314 | 0.7129 | 0.7136 | 0.7129 | 0.8443 |
| No log | 2.8727 | 316 | 0.7708 | 0.72 | 0.7708 | 0.8779 |
| No log | 2.8909 | 318 | 0.8652 | 0.6662 | 0.8652 | 0.9301 |
| No log | 2.9091 | 320 | 0.8713 | 0.6662 | 0.8713 | 0.9334 |
| No log | 2.9273 | 322 | 0.8259 | 0.7288 | 0.8259 | 0.9088 |
| No log | 2.9455 | 324 | 0.8238 | 0.7288 | 0.8238 | 0.9077 |
| No log | 2.9636 | 326 | 0.7244 | 0.7014 | 0.7244 | 0.8511 |
| No log | 2.9818 | 328 | 0.6876 | 0.6627 | 0.6876 | 0.8292 |
| No log | 3.0 | 330 | 0.6830 | 0.6627 | 0.6830 | 0.8265 |
| No log | 3.0182 | 332 | 0.7192 | 0.6797 | 0.7192 | 0.8480 |
| No log | 3.0364 | 334 | 0.6842 | 0.6797 | 0.6842 | 0.8272 |
| No log | 3.0545 | 336 | 0.6479 | 0.6797 | 0.6479 | 0.8049 |
| No log | 3.0727 | 338 | 0.6075 | 0.6369 | 0.6075 | 0.7794 |
| No log | 3.0909 | 340 | 0.5640 | 0.6824 | 0.5640 | 0.7510 |
| No log | 3.1091 | 342 | 0.5414 | 0.7139 | 0.5414 | 0.7358 |
| No log | 3.1273 | 344 | 0.5294 | 0.7427 | 0.5294 | 0.7276 |
| No log | 3.1455 | 346 | 0.5312 | 0.7427 | 0.5312 | 0.7288 |
| No log | 3.1636 | 348 | 0.5517 | 0.7717 | 0.5517 | 0.7428 |
| No log | 3.1818 | 350 | 0.5479 | 0.7616 | 0.5479 | 0.7402 |
| No log | 3.2 | 352 | 0.5557 | 0.7612 | 0.5557 | 0.7455 |
| No log | 3.2182 | 354 | 0.6028 | 0.8161 | 0.6028 | 0.7764 |
| No log | 3.2364 | 356 | 0.6410 | 0.7607 | 0.6410 | 0.8006 |
| No log | 3.2545 | 358 | 0.6535 | 0.7607 | 0.6535 | 0.8084 |
| No log | 3.2727 | 360 | 0.5975 | 0.7607 | 0.5975 | 0.7730 |
| No log | 3.2909 | 362 | 0.5794 | 0.7607 | 0.5794 | 0.7612 |
| No log | 3.3091 | 364 | 0.6098 | 0.7157 | 0.6098 | 0.7809 |
| No log | 3.3273 | 366 | 0.6861 | 0.7157 | 0.6861 | 0.8283 |
| No log | 3.3455 | 368 | 0.8044 | 0.6356 | 0.8044 | 0.8969 |
| No log | 3.3636 | 370 | 0.8394 | 0.6356 | 0.8394 | 0.9162 |
| No log | 3.3818 | 372 | 0.7741 | 0.6356 | 0.7741 | 0.8798 |
| No log | 3.4 | 374 | 0.6632 | 0.7412 | 0.6632 | 0.8144 |
| No log | 3.4182 | 376 | 0.5952 | 0.7607 | 0.5952 | 0.7715 |
| No log | 3.4364 | 378 | 0.6011 | 0.7607 | 0.6011 | 0.7753 |
| No log | 3.4545 | 380 | 0.6574 | 0.7157 | 0.6574 | 0.8108 |
| No log | 3.4727 | 382 | 0.6996 | 0.6622 | 0.6996 | 0.8364 |
| No log | 3.4909 | 384 | 0.8024 | 0.6678 | 0.8024 | 0.8958 |
| No log | 3.5091 | 386 | 0.8513 | 0.6170 | 0.8513 | 0.9227 |
| No log | 3.5273 | 388 | 0.8546 | 0.6135 | 0.8546 | 0.9244 |
| No log | 3.5455 | 390 | 0.7799 | 0.7009 | 0.7799 | 0.8831 |
| No log | 3.5636 | 392 | 0.6614 | 0.7429 | 0.6614 | 0.8133 |
| No log | 3.5818 | 394 | 0.6051 | 0.7354 | 0.6051 | 0.7779 |
| No log | 3.6 | 396 | 0.5876 | 0.7354 | 0.5876 | 0.7666 |
| No log | 3.6182 | 398 | 0.5991 | 0.7692 | 0.5991 | 0.7740 |
| No log | 3.6364 | 400 | 0.6498 | 0.7157 | 0.6498 | 0.8061 |
| No log | 3.6545 | 402 | 0.6244 | 0.7692 | 0.6244 | 0.7902 |
| No log | 3.6727 | 404 | 0.5659 | 0.7162 | 0.5659 | 0.7523 |
| No log | 3.6909 | 406 | 0.5412 | 0.7713 | 0.5412 | 0.7357 |
| No log | 3.7091 | 408 | 0.5411 | 0.7713 | 0.5411 | 0.7356 |
| No log | 3.7273 | 410 | 0.5586 | 0.7162 | 0.5586 | 0.7474 |
| No log | 3.7455 | 412 | 0.6333 | 0.7692 | 0.6333 | 0.7958 |
| No log | 3.7636 | 414 | 0.6725 | 0.7789 | 0.6725 | 0.8200 |
| No log | 3.7818 | 416 | 0.6743 | 0.7692 | 0.6743 | 0.8212 |
| No log | 3.8 | 418 | 0.6332 | 0.7692 | 0.6332 | 0.7958 |
| No log | 3.8182 | 420 | 0.5577 | 0.7612 | 0.5577 | 0.7468 |
| No log | 3.8364 | 422 | 0.5481 | 0.7689 | 0.5481 | 0.7404 |
| No log | 3.8545 | 424 | 0.5619 | 0.7451 | 0.5619 | 0.7496 |
| No log | 3.8727 | 426 | 0.5609 | 0.7689 | 0.5609 | 0.7489 |
| No log | 3.8909 | 428 | 0.5736 | 0.7689 | 0.5736 | 0.7574 |
| No log | 3.9091 | 430 | 0.6160 | 0.7281 | 0.6160 | 0.7849 |
| No log | 3.9273 | 432 | 0.6886 | 0.7598 | 0.6886 | 0.8298 |
| No log | 3.9455 | 434 | 0.7413 | 0.7009 | 0.7413 | 0.8610 |
| No log | 3.9636 | 436 | 0.7585 | 0.7350 | 0.7585 | 0.8709 |
| No log | 3.9818 | 438 | 0.7000 | 0.7819 | 0.7000 | 0.8367 |
| No log | 4.0 | 440 | 0.7079 | 0.7819 | 0.7079 | 0.8414 |
| No log | 4.0182 | 442 | 0.7767 | 0.7350 | 0.7767 | 0.8813 |
| No log | 4.0364 | 444 | 0.8279 | 0.6974 | 0.8279 | 0.9099 |
| No log | 4.0545 | 446 | 0.9175 | 0.6135 | 0.9175 | 0.9579 |
| No log | 4.0727 | 448 | 0.9430 | 0.6135 | 0.9430 | 0.9711 |
| No log | 4.0909 | 450 | 0.8952 | 0.6135 | 0.8952 | 0.9461 |
| No log | 4.1091 | 452 | 0.8159 | 0.6508 | 0.8159 | 0.9033 |
| No log | 4.1273 | 454 | 0.7894 | 0.6508 | 0.7894 | 0.8885 |
| No log | 4.1455 | 456 | 0.7925 | 0.6508 | 0.7925 | 0.8902 |
| No log | 4.1636 | 458 | 0.7619 | 0.6508 | 0.7619 | 0.8729 |
| No log | 4.1818 | 460 | 0.7092 | 0.7009 | 0.7092 | 0.8421 |
| No log | 4.2 | 462 | 0.6679 | 0.7081 | 0.6679 | 0.8173 |
| No log | 4.2182 | 464 | 0.6338 | 0.7081 | 0.6338 | 0.7961 |
| No log | 4.2364 | 466 | 0.6014 | 0.7081 | 0.6014 | 0.7755 |
| No log | 4.2545 | 468 | 0.5941 | 0.7081 | 0.5941 | 0.7708 |
| No log | 4.2727 | 470 | 0.6321 | 0.7081 | 0.6321 | 0.7950 |
| No log | 4.2909 | 472 | 0.6692 | 0.7081 | 0.6692 | 0.8180 |
| No log | 4.3091 | 474 | 0.6889 | 0.7081 | 0.6889 | 0.8300 |
| No log | 4.3273 | 476 | 0.6713 | 0.7081 | 0.6713 | 0.8194 |
| No log | 4.3455 | 478 | 0.6103 | 0.7157 | 0.6103 | 0.7812 |
| No log | 4.3636 | 480 | 0.5622 | 0.8405 | 0.5622 | 0.7498 |
| No log | 4.3818 | 482 | 0.5508 | 0.7875 | 0.5508 | 0.7421 |
| No log | 4.4 | 484 | 0.5608 | 0.7433 | 0.5608 | 0.7488 |
| No log | 4.4182 | 486 | 0.5603 | 0.7945 | 0.5603 | 0.7485 |
| No log | 4.4364 | 488 | 0.5681 | 0.7945 | 0.5681 | 0.7537 |
| No log | 4.4545 | 490 | 0.5711 | 0.7945 | 0.5711 | 0.7557 |
| No log | 4.4727 | 492 | 0.5365 | 0.7875 | 0.5365 | 0.7325 |
| No log | 4.4909 | 494 | 0.5220 | 0.7879 | 0.5220 | 0.7225 |
| No log | 4.5091 | 496 | 0.5246 | 0.7354 | 0.5246 | 0.7243 |
| No log | 4.5273 | 498 | 0.5293 | 0.7672 | 0.5293 | 0.7275 |
| 0.7009 | 4.5455 | 500 | 0.5217 | 0.7354 | 0.5217 | 0.7223 |
| 0.7009 | 4.5636 | 502 | 0.5169 | 0.7879 | 0.5169 | 0.7190 |
| 0.7009 | 4.5818 | 504 | 0.5158 | 0.7879 | 0.5158 | 0.7182 |
| 0.7009 | 4.6 | 506 | 0.5105 | 0.7759 | 0.5105 | 0.7145 |
| 0.7009 | 4.6182 | 508 | 0.5425 | 0.7434 | 0.5425 | 0.7365 |
| 0.7009 | 4.6364 | 510 | 0.5765 | 0.7434 | 0.5765 | 0.7593 |
| 0.7009 | 4.6545 | 512 | 0.5579 | 0.7110 | 0.5579 | 0.7469 |
| 0.7009 | 4.6727 | 514 | 0.5025 | 0.7612 | 0.5025 | 0.7089 |
| 0.7009 | 4.6909 | 516 | 0.4846 | 0.7713 | 0.4846 | 0.6961 |
| 0.7009 | 4.7091 | 518 | 0.4876 | 0.7713 | 0.4876 | 0.6983 |
| 0.7009 | 4.7273 | 520 | 0.4970 | 0.8051 | 0.4970 | 0.7050 |
| 0.7009 | 4.7455 | 522 | 0.5462 | 0.8009 | 0.5462 | 0.7390 |
| 0.7009 | 4.7636 | 524 | 0.6435 | 0.7944 | 0.6435 | 0.8022 |
| 0.7009 | 4.7818 | 526 | 0.6907 | 0.8032 | 0.6907 | 0.8311 |
| 0.7009 | 4.8 | 528 | 0.7354 | 0.7576 | 0.7354 | 0.8576 |
| 0.7009 | 4.8182 | 530 | 0.7200 | 0.7576 | 0.7200 | 0.8486 |
| 0.7009 | 4.8364 | 532 | 0.6684 | 0.7502 | 0.6684 | 0.8176 |
| 0.7009 | 4.8545 | 534 | 0.6330 | 0.7192 | 0.6330 | 0.7956 |
| 0.7009 | 4.8727 | 536 | 0.6535 | 0.7192 | 0.6535 | 0.8084 |
| 0.7009 | 4.8909 | 538 | 0.6084 | 0.7173 | 0.6084 | 0.7800 |
| 0.7009 | 4.9091 | 540 | 0.5811 | 0.7911 | 0.5811 | 0.7623 |
| 0.7009 | 4.9273 | 542 | 0.6077 | 0.7173 | 0.6077 | 0.7795 |
| 0.7009 | 4.9455 | 544 | 0.6183 | 0.7173 | 0.6183 | 0.7863 |
| 0.7009 | 4.9636 | 546 | 0.6088 | 0.6860 | 0.6088 | 0.7802 |
| 0.7009 | 4.9818 | 548 | 0.5767 | 0.7417 | 0.5767 | 0.7594 |
| 0.7009 | 5.0 | 550 | 0.5446 | 0.7422 | 0.5446 | 0.7380 |
| 0.7009 | 5.0182 | 552 | 0.5214 | 0.7713 | 0.5214 | 0.7221 |
| 0.7009 | 5.0364 | 554 | 0.5126 | 0.7713 | 0.5126 | 0.7160 |
| 0.7009 | 5.0545 | 556 | 0.5239 | 0.7612 | 0.5239 | 0.7238 |
| 0.7009 | 5.0727 | 558 | 0.5756 | 0.6797 | 0.5756 | 0.7587 |
| 0.7009 | 5.0909 | 560 | 0.6413 | 0.6860 | 0.6413 | 0.8008 |
| 0.7009 | 5.1091 | 562 | 0.7060 | 0.6860 | 0.7060 | 0.8402 |
| 0.7009 | 5.1273 | 564 | 0.6859 | 0.6860 | 0.6859 | 0.8282 |
| 0.7009 | 5.1455 | 566 | 0.6258 | 0.6860 | 0.6258 | 0.7911 |
| 0.7009 | 5.1636 | 568 | 0.5764 | 0.8011 | 0.5764 | 0.7592 |
| 0.7009 | 5.1818 | 570 | 0.5327 | 0.8011 | 0.5327 | 0.7298 |
| 0.7009 | 5.2 | 572 | 0.5241 | 0.8011 | 0.5241 | 0.7240 |
| 0.7009 | 5.2182 | 574 | 0.5689 | 0.8011 | 0.5689 | 0.7543 |
| 0.7009 | 5.2364 | 576 | 0.6944 | 0.6860 | 0.6944 | 0.8333 |
| 0.7009 | 5.2545 | 578 | 0.8683 | 0.6333 | 0.8683 | 0.9318 |
| 0.7009 | 5.2727 | 580 | 0.9135 | 0.6333 | 0.9135 | 0.9558 |
| 0.7009 | 5.2909 | 582 | 0.8530 | 0.6836 | 0.8530 | 0.9236 |
| 0.7009 | 5.3091 | 584 | 0.7270 | 0.6902 | 0.7270 | 0.8527 |
| 0.7009 | 5.3273 | 586 | 0.5889 | 0.7894 | 0.5889 | 0.7674 |
| 0.7009 | 5.3455 | 588 | 0.4993 | 0.7322 | 0.4993 | 0.7066 |
| 0.7009 | 5.3636 | 590 | 0.4994 | 0.7322 | 0.4994 | 0.7067 |
| 0.7009 | 5.3818 | 592 | 0.5474 | 0.7264 | 0.5474 | 0.7399 |
| 0.7009 | 5.4 | 594 | 0.6127 | 0.7264 | 0.6127 | 0.7827 |
| 0.7009 | 5.4182 | 596 | 0.6512 | 0.8032 | 0.6512 | 0.8069 |
| 0.7009 | 5.4364 | 598 | 0.7413 | 0.7658 | 0.7413 | 0.8610 |
| 0.7009 | 5.4545 | 600 | 0.7711 | 0.7941 | 0.7711 | 0.8781 |
| 0.7009 | 5.4727 | 602 | 0.8257 | 0.7658 | 0.8257 | 0.9087 |
| 0.7009 | 5.4909 | 604 | 0.8076 | 0.7658 | 0.8076 | 0.8987 |
| 0.7009 | 5.5091 | 606 | 0.7346 | 0.7420 | 0.7346 | 0.8571 |
| 0.7009 | 5.5273 | 608 | 0.6306 | 0.7417 | 0.6306 | 0.7941 |
| 0.7009 | 5.5455 | 610 | 0.5832 | 0.7157 | 0.5832 | 0.7637 |
| 0.7009 | 5.5636 | 612 | 0.5885 | 0.7086 | 0.5885 | 0.7672 |
| 0.7009 | 5.5818 | 614 | 0.6277 | 0.7598 | 0.6277 | 0.7923 |
| 0.7009 | 5.6 | 616 | 0.6928 | 0.7417 | 0.6928 | 0.8323 |
| 0.7009 | 5.6182 | 618 | 0.7585 | 0.6860 | 0.7585 | 0.8709 |
| 0.7009 | 5.6364 | 620 | 0.7859 | 0.6860 | 0.7859 | 0.8865 |
| 0.7009 | 5.6545 | 622 | 0.8055 | 0.6860 | 0.8055 | 0.8975 |
| 0.7009 | 5.6727 | 624 | 0.7797 | 0.6860 | 0.7797 | 0.8830 |
| 0.7009 | 5.6909 | 626 | 0.7214 | 0.6860 | 0.7214 | 0.8494 |
| 0.7009 | 5.7091 | 628 | 0.6777 | 0.7417 | 0.6777 | 0.8232 |
| 0.7009 | 5.7273 | 630 | 0.6336 | 0.7333 | 0.6336 | 0.7960 |
| 0.7009 | 5.7455 | 632 | 0.6130 | 0.7692 | 0.6130 | 0.7829 |
| 0.7009 | 5.7636 | 634 | 0.6155 | 0.7692 | 0.6155 | 0.7845 |
| 0.7009 | 5.7818 | 636 | 0.6510 | 0.7692 | 0.6510 | 0.8068 |
| 0.7009 | 5.8 | 638 | 0.7025 | 0.7421 | 0.7025 | 0.8382 |
| 0.7009 | 5.8182 | 640 | 0.7461 | 0.7417 | 0.7461 | 0.8638 |
| 0.7009 | 5.8364 | 642 | 0.7637 | 0.6860 | 0.7637 | 0.8739 |
| 0.7009 | 5.8545 | 644 | 0.7207 | 0.7417 | 0.7207 | 0.8489 |
| 0.7009 | 5.8727 | 646 | 0.6554 | 0.7598 | 0.6554 | 0.8095 |
| 0.7009 | 5.8909 | 648 | 0.6099 | 0.7598 | 0.6099 | 0.7809 |
| 0.7009 | 5.9091 | 650 | 0.6000 | 0.7598 | 0.6000 | 0.7746 |
| 0.7009 | 5.9273 | 652 | 0.6240 | 0.7598 | 0.6240 | 0.7899 |
| 0.7009 | 5.9455 | 654 | 0.6449 | 0.7598 | 0.6449 | 0.8031 |
| 0.7009 | 5.9636 | 656 | 0.6658 | 0.7688 | 0.6658 | 0.8160 |
| 0.7009 | 5.9818 | 658 | 0.6927 | 0.7417 | 0.6927 | 0.8323 |
| 0.7009 | 6.0 | 660 | 0.6841 | 0.7745 | 0.6841 | 0.8271 |
| 0.7009 | 6.0182 | 662 | 0.7083 | 0.7576 | 0.7083 | 0.8416 |
| 0.7009 | 6.0364 | 664 | 0.7063 | 0.7576 | 0.7063 | 0.8404 |
| 0.7009 | 6.0545 | 666 | 0.6477 | 0.7246 | 0.6477 | 0.8048 |
| 0.7009 | 6.0727 | 668 | 0.6083 | 0.8220 | 0.6083 | 0.7799 |
| 0.7009 | 6.0909 | 670 | 0.6174 | 0.8220 | 0.6174 | 0.7858 |
| 0.7009 | 6.1091 | 672 | 0.6246 | 0.7764 | 0.6246 | 0.7903 |
| 0.7009 | 6.1273 | 674 | 0.6522 | 0.8032 | 0.6522 | 0.8076 |
| 0.7009 | 6.1455 | 676 | 0.6751 | 0.7745 | 0.6751 | 0.8216 |
| 0.7009 | 6.1636 | 678 | 0.6702 | 0.7674 | 0.6702 | 0.8187 |
| 0.7009 | 6.1818 | 680 | 0.6511 | 0.7973 | 0.6511 | 0.8069 |
| 0.7009 | 6.2 | 682 | 0.6500 | 0.7885 | 0.6500 | 0.8062 |
| 0.7009 | 6.2182 | 684 | 0.6311 | 0.7720 | 0.6311 | 0.7944 |
| 0.7009 | 6.2364 | 686 | 0.5978 | 0.7720 | 0.5978 | 0.7732 |
| 0.7009 | 6.2545 | 688 | 0.5518 | 0.7720 | 0.5518 | 0.7428 |
| 0.7009 | 6.2727 | 690 | 0.5373 | 0.7735 | 0.5373 | 0.7330 |
| 0.7009 | 6.2909 | 692 | 0.5511 | 0.7720 | 0.5511 | 0.7424 |
| 0.7009 | 6.3091 | 694 | 0.5706 | 0.7720 | 0.5706 | 0.7554 |
| 0.7009 | 6.3273 | 696 | 0.6004 | 0.7720 | 0.6004 | 0.7749 |
| 0.7009 | 6.3455 | 698 | 0.5841 | 0.7735 | 0.5841 | 0.7643 |
| 0.7009 | 6.3636 | 700 | 0.5572 | 0.7431 | 0.5572 | 0.7465 |
| 0.7009 | 6.3818 | 702 | 0.5406 | 0.7945 | 0.5406 | 0.7353 |
| 0.7009 | 6.4 | 704 | 0.5533 | 0.7598 | 0.5533 | 0.7439 |
| 0.7009 | 6.4182 | 706 | 0.5716 | 0.7250 | 0.5716 | 0.7560 |
| 0.7009 | 6.4364 | 708 | 0.6194 | 0.7510 | 0.6194 | 0.7870 |
| 0.7009 | 6.4545 | 710 | 0.6465 | 0.7720 | 0.6465 | 0.8040 |
| 0.7009 | 6.4727 | 712 | 0.6340 | 0.7720 | 0.6340 | 0.7962 |
| 0.7009 | 6.4909 | 714 | 0.6527 | 0.7720 | 0.6527 | 0.8079 |
| 0.7009 | 6.5091 | 716 | 0.6833 | 0.7720 | 0.6833 | 0.8266 |
| 0.7009 | 6.5273 | 718 | 0.7127 | 0.7924 | 0.7127 | 0.8442 |
| 0.7009 | 6.5455 | 720 | 0.7309 | 0.7720 | 0.7309 | 0.8549 |
| 0.7009 | 6.5636 | 722 | 0.7086 | 0.7434 | 0.7086 | 0.8418 |
| 0.7009 | 6.5818 | 724 | 0.6920 | 0.7434 | 0.6920 | 0.8319 |
| 0.7009 | 6.6 | 726 | 0.6844 | 0.7110 | 0.6844 | 0.8273 |
| 0.7009 | 6.6182 | 728 | 0.6660 | 0.7110 | 0.6660 | 0.8161 |
| 0.7009 | 6.6364 | 730 | 0.6358 | 0.7110 | 0.6358 | 0.7974 |
| 0.7009 | 6.6545 | 732 | 0.6239 | 0.7086 | 0.6239 | 0.7899 |
| 0.7009 | 6.6727 | 734 | 0.6271 | 0.7086 | 0.6271 | 0.7919 |
| 0.7009 | 6.6909 | 736 | 0.6581 | 0.7086 | 0.6581 | 0.8112 |
| 0.7009 | 6.7091 | 738 | 0.6893 | 0.7333 | 0.6893 | 0.8302 |
| 0.7009 | 6.7273 | 740 | 0.7273 | 0.7328 | 0.7273 | 0.8528 |
| 0.7009 | 6.7455 | 742 | 0.7657 | 0.7328 | 0.7657 | 0.8751 |
| 0.7009 | 6.7636 | 744 | 0.7636 | 0.7328 | 0.7636 | 0.8739 |
| 0.7009 | 6.7818 | 746 | 0.7305 | 0.7338 | 0.7305 | 0.8547 |
| 0.7009 | 6.8 | 748 | 0.6837 | 0.7434 | 0.6837 | 0.8268 |
| 0.7009 | 6.8182 | 750 | 0.6297 | 0.7431 | 0.6297 | 0.7935 |
| 0.7009 | 6.8364 | 752 | 0.5911 | 0.7086 | 0.5911 | 0.7688 |
| 0.7009 | 6.8545 | 754 | 0.5775 | 0.7086 | 0.5775 | 0.7599 |
| 0.7009 | 6.8727 | 756 | 0.5796 | 0.7162 | 0.5796 | 0.7613 |
| 0.7009 | 6.8909 | 758 | 0.5941 | 0.7086 | 0.5941 | 0.7708 |
| 0.7009 | 6.9091 | 760 | 0.6135 | 0.6866 | 0.6135 | 0.7833 |
| 0.7009 | 6.9273 | 762 | 0.6179 | 0.6866 | 0.6179 | 0.7860 |
| 0.7009 | 6.9455 | 764 | 0.6247 | 0.6866 | 0.6247 | 0.7904 |
| 0.7009 | 6.9636 | 766 | 0.6296 | 0.7250 | 0.6296 | 0.7935 |
| 0.7009 | 6.9818 | 768 | 0.6294 | 0.7250 | 0.6294 | 0.7933 |
| 0.7009 | 7.0 | 770 | 0.6422 | 0.7250 | 0.6422 | 0.8014 |
| 0.7009 | 7.0182 | 772 | 0.6529 | 0.7250 | 0.6529 | 0.8080 |
| 0.7009 | 7.0364 | 774 | 0.6619 | 0.7431 | 0.6619 | 0.8136 |
| 0.7009 | 7.0545 | 776 | 0.6502 | 0.7086 | 0.6502 | 0.8064 |
| 0.7009 | 7.0727 | 778 | 0.6532 | 0.7086 | 0.6532 | 0.8082 |
| 0.7009 | 7.0909 | 780 | 0.6659 | 0.7086 | 0.6659 | 0.8160 |
| 0.7009 | 7.1091 | 782 | 0.6774 | 0.7086 | 0.6774 | 0.8230 |
| 0.7009 | 7.1273 | 784 | 0.6913 | 0.7086 | 0.6913 | 0.8315 |
| 0.7009 | 7.1455 | 786 | 0.7300 | 0.6569 | 0.7300 | 0.8544 |
| 0.7009 | 7.1636 | 788 | 0.7502 | 0.6797 | 0.7502 | 0.8662 |
| 0.7009 | 7.1818 | 790 | 0.7329 | 0.6797 | 0.7329 | 0.8561 |
| 0.7009 | 7.2 | 792 | 0.7006 | 0.6802 | 0.7006 | 0.8370 |
| 0.7009 | 7.2182 | 794 | 0.6593 | 0.7086 | 0.6593 | 0.8120 |
| 0.7009 | 7.2364 | 796 | 0.6300 | 0.7086 | 0.6300 | 0.7937 |
| 0.7009 | 7.2545 | 798 | 0.6207 | 0.7086 | 0.6207 | 0.7879 |
| 0.7009 | 7.2727 | 800 | 0.6202 | 0.7086 | 0.6202 | 0.7876 |
| 0.7009 | 7.2909 | 802 | 0.6288 | 0.7086 | 0.6288 | 0.7929 |
| 0.7009 | 7.3091 | 804 | 0.6409 | 0.6802 | 0.6409 | 0.8006 |
| 0.7009 | 7.3273 | 806 | 0.6666 | 0.7250 | 0.6666 | 0.8164 |
| 0.7009 | 7.3455 | 808 | 0.6964 | 0.7764 | 0.6964 | 0.8345 |
| 0.7009 | 7.3636 | 810 | 0.6975 | 0.7764 | 0.6975 | 0.8352 |
| 0.7009 | 7.3818 | 812 | 0.7009 | 0.7764 | 0.7009 | 0.8372 |
| 0.7009 | 7.4 | 814 | 0.6785 | 0.7250 | 0.6785 | 0.8237 |
| 0.7009 | 7.4182 | 816 | 0.6717 | 0.7250 | 0.6717 | 0.8196 |
| 0.7009 | 7.4364 | 818 | 0.7028 | 0.7250 | 0.7028 | 0.8383 |
| 0.7009 | 7.4545 | 820 | 0.7607 | 0.8032 | 0.7607 | 0.8722 |
| 0.7009 | 7.4727 | 822 | 0.7931 | 0.8032 | 0.7931 | 0.8906 |
| 0.7009 | 7.4909 | 824 | 0.7947 | 0.8032 | 0.7947 | 0.8915 |
| 0.7009 | 7.5091 | 826 | 0.7602 | 0.7579 | 0.7602 | 0.8719 |
| 0.7009 | 7.5273 | 828 | 0.7059 | 0.7579 | 0.7059 | 0.8402 |
| 0.7009 | 7.5455 | 830 | 0.6693 | 0.7264 | 0.6693 | 0.8181 |
| 0.7009 | 7.5636 | 832 | 0.6440 | 0.7264 | 0.6440 | 0.8025 |
| 0.7009 | 7.5818 | 834 | 0.6331 | 0.7250 | 0.6331 | 0.7957 |
| 0.7009 | 7.6 | 836 | 0.6374 | 0.7250 | 0.6374 | 0.7983 |
| 0.7009 | 7.6182 | 838 | 0.6637 | 0.7250 | 0.6637 | 0.8147 |
| 0.7009 | 7.6364 | 840 | 0.6959 | 0.7250 | 0.6959 | 0.8342 |
| 0.7009 | 7.6545 | 842 | 0.6998 | 0.7250 | 0.6998 | 0.8365 |
| 0.7009 | 7.6727 | 844 | 0.7036 | 0.7250 | 0.7036 | 0.8388 |
| 0.7009 | 7.6909 | 846 | 0.6861 | 0.7250 | 0.6861 | 0.8283 |
| 0.7009 | 7.7091 | 848 | 0.6667 | 0.7250 | 0.6667 | 0.8165 |
| 0.7009 | 7.7273 | 850 | 0.6578 | 0.7250 | 0.6578 | 0.8111 |
| 0.7009 | 7.7455 | 852 | 0.6414 | 0.7250 | 0.6414 | 0.8008 |
| 0.7009 | 7.7636 | 854 | 0.6523 | 0.7250 | 0.6523 | 0.8076 |
| 0.7009 | 7.7818 | 856 | 0.6835 | 0.7250 | 0.6835 | 0.8267 |
| 0.7009 | 7.8 | 858 | 0.7244 | 0.7250 | 0.7244 | 0.8511 |
| 0.7009 | 7.8182 | 860 | 0.7521 | 0.7745 | 0.7521 | 0.8672 |
| 0.7009 | 7.8364 | 862 | 0.7779 | 0.7745 | 0.7779 | 0.8820 |
| 0.7009 | 7.8545 | 864 | 0.7807 | 0.8032 | 0.7807 | 0.8836 |
| 0.7009 | 7.8727 | 866 | 0.7516 | 0.8032 | 0.7516 | 0.8670 |
| 0.7009 | 7.8909 | 868 | 0.7306 | 0.7579 | 0.7306 | 0.8548 |
| 0.7009 | 7.9091 | 870 | 0.7055 | 0.7264 | 0.7055 | 0.8399 |
| 0.7009 | 7.9273 | 872 | 0.6819 | 0.7264 | 0.6819 | 0.8258 |
| 0.7009 | 7.9455 | 874 | 0.6778 | 0.7264 | 0.6778 | 0.8233 |
| 0.7009 | 7.9636 | 876 | 0.6845 | 0.7264 | 0.6845 | 0.8274 |
| 0.7009 | 7.9818 | 878 | 0.6986 | 0.7264 | 0.6986 | 0.8358 |
| 0.7009 | 8.0 | 880 | 0.7032 | 0.6866 | 0.7032 | 0.8386 |
| 0.7009 | 8.0182 | 882 | 0.6956 | 0.6866 | 0.6956 | 0.8340 |
| 0.7009 | 8.0364 | 884 | 0.6801 | 0.6866 | 0.6801 | 0.8247 |
| 0.7009 | 8.0545 | 886 | 0.6622 | 0.6866 | 0.6622 | 0.8138 |
| 0.7009 | 8.0727 | 888 | 0.6619 | 0.7250 | 0.6619 | 0.8135 |
| 0.7009 | 8.0909 | 890 | 0.6550 | 0.7250 | 0.6550 | 0.8093 |
| 0.7009 | 8.1091 | 892 | 0.6417 | 0.7510 | 0.6417 | 0.8010 |
| 0.7009 | 8.1273 | 894 | 0.6418 | 0.7510 | 0.6418 | 0.8011 |
| 0.7009 | 8.1455 | 896 | 0.6421 | 0.7510 | 0.6421 | 0.8013 |
| 0.7009 | 8.1636 | 898 | 0.6545 | 0.7507 | 0.6545 | 0.8090 |
| 0.7009 | 8.1818 | 900 | 0.6872 | 0.7799 | 0.6872 | 0.8290 |
| 0.7009 | 8.2 | 902 | 0.7124 | 0.7799 | 0.7124 | 0.8440 |
| 0.7009 | 8.2182 | 904 | 0.7099 | 0.7799 | 0.7099 | 0.8425 |
| 0.7009 | 8.2364 | 906 | 0.7213 | 0.7799 | 0.7213 | 0.8493 |
| 0.7009 | 8.2545 | 908 | 0.7332 | 0.7799 | 0.7332 | 0.8563 |
| 0.7009 | 8.2727 | 910 | 0.7357 | 0.7799 | 0.7357 | 0.8578 |
| 0.7009 | 8.2909 | 912 | 0.7450 | 0.7799 | 0.7450 | 0.8632 |
| 0.7009 | 8.3091 | 914 | 0.7346 | 0.7799 | 0.7346 | 0.8571 |
| 0.7009 | 8.3273 | 916 | 0.7227 | 0.7799 | 0.7227 | 0.8501 |
| 0.7009 | 8.3455 | 918 | 0.7112 | 0.7799 | 0.7112 | 0.8433 |
| 0.7009 | 8.3636 | 920 | 0.6959 | 0.7799 | 0.6959 | 0.8342 |
| 0.7009 | 8.3818 | 922 | 0.6850 | 0.7799 | 0.6850 | 0.8276 |
| 0.7009 | 8.4 | 924 | 0.6717 | 0.7799 | 0.6717 | 0.8195 |
| 0.7009 | 8.4182 | 926 | 0.6687 | 0.7507 | 0.6687 | 0.8177 |
| 0.7009 | 8.4364 | 928 | 0.6731 | 0.7507 | 0.6731 | 0.8204 |
| 0.7009 | 8.4545 | 930 | 0.6763 | 0.7507 | 0.6763 | 0.8224 |
| 0.7009 | 8.4727 | 932 | 0.6759 | 0.7510 | 0.6759 | 0.8221 |
| 0.7009 | 8.4909 | 934 | 0.6741 | 0.7510 | 0.6741 | 0.8210 |
| 0.7009 | 8.5091 | 936 | 0.6634 | 0.7510 | 0.6634 | 0.8145 |
| 0.7009 | 8.5273 | 938 | 0.6443 | 0.7510 | 0.6443 | 0.8027 |
| 0.7009 | 8.5455 | 940 | 0.6261 | 0.7510 | 0.6261 | 0.7913 |
| 0.7009 | 8.5636 | 942 | 0.6222 | 0.7510 | 0.6222 | 0.7888 |
| 0.7009 | 8.5818 | 944 | 0.6269 | 0.7510 | 0.6269 | 0.7918 |
| 0.7009 | 8.6 | 946 | 0.6317 | 0.7510 | 0.6317 | 0.7948 |
| 0.7009 | 8.6182 | 948 | 0.6418 | 0.7510 | 0.6418 | 0.8011 |
| 0.7009 | 8.6364 | 950 | 0.6452 | 0.7510 | 0.6452 | 0.8032 |
| 0.7009 | 8.6545 | 952 | 0.6434 | 0.7157 | 0.6434 | 0.8021 |
| 0.7009 | 8.6727 | 954 | 0.6358 | 0.7157 | 0.6358 | 0.7973 |
| 0.7009 | 8.6909 | 956 | 0.6289 | 0.7086 | 0.6289 | 0.7930 |
| 0.7009 | 8.7091 | 958 | 0.6252 | 0.7086 | 0.6252 | 0.7907 |
| 0.7009 | 8.7273 | 960 | 0.6198 | 0.7086 | 0.6198 | 0.7873 |
| 0.7009 | 8.7455 | 962 | 0.6111 | 0.7086 | 0.6111 | 0.7817 |
| 0.7009 | 8.7636 | 964 | 0.5977 | 0.7086 | 0.5977 | 0.7731 |
| 0.7009 | 8.7818 | 966 | 0.5928 | 0.7431 | 0.5928 | 0.7699 |
| 0.7009 | 8.8 | 968 | 0.5974 | 0.7431 | 0.5974 | 0.7729 |
| 0.7009 | 8.8182 | 970 | 0.6067 | 0.7431 | 0.6067 | 0.7789 |
| 0.7009 | 8.8364 | 972 | 0.6199 | 0.7431 | 0.6199 | 0.7873 |
| 0.7009 | 8.8545 | 974 | 0.6426 | 0.7431 | 0.6426 | 0.8016 |
| 0.7009 | 8.8727 | 976 | 0.6690 | 0.7431 | 0.6690 | 0.8179 |
| 0.7009 | 8.8909 | 978 | 0.6967 | 0.7250 | 0.6967 | 0.8347 |
| 0.7009 | 8.9091 | 980 | 0.7154 | 0.7264 | 0.7154 | 0.8458 |
| 0.7009 | 8.9273 | 982 | 0.7209 | 0.7264 | 0.7209 | 0.8491 |
| 0.7009 | 8.9455 | 984 | 0.7096 | 0.7264 | 0.7096 | 0.8424 |
| 0.7009 | 8.9636 | 986 | 0.6887 | 0.7250 | 0.6887 | 0.8299 |
| 0.7009 | 8.9818 | 988 | 0.6630 | 0.7250 | 0.6630 | 0.8143 |
| 0.7009 | 9.0 | 990 | 0.6355 | 0.7178 | 0.6355 | 0.7972 |
| 0.7009 | 9.0182 | 992 | 0.6200 | 0.7431 | 0.6200 | 0.7874 |
| 0.7009 | 9.0364 | 994 | 0.6138 | 0.7431 | 0.6138 | 0.7835 |
| 0.7009 | 9.0545 | 996 | 0.6035 | 0.7178 | 0.6035 | 0.7769 |
| 0.7009 | 9.0727 | 998 | 0.5985 | 0.7178 | 0.5985 | 0.7736 |
| 0.1123 | 9.0909 | 1000 | 0.6039 | 0.7250 | 0.6039 | 0.7771 |
| 0.1123 | 9.1091 | 1002 | 0.6115 | 0.7250 | 0.6115 | 0.7820 |
| 0.1123 | 9.1273 | 1004 | 0.6214 | 0.7250 | 0.6214 | 0.7883 |
| 0.1123 | 9.1455 | 1006 | 0.6355 | 0.7250 | 0.6355 | 0.7972 |
| 0.1123 | 9.1636 | 1008 | 0.6545 | 0.7250 | 0.6545 | 0.8090 |
| 0.1123 | 9.1818 | 1010 | 0.6762 | 0.7764 | 0.6762 | 0.8223 |
| 0.1123 | 9.2 | 1012 | 0.6885 | 0.7764 | 0.6885 | 0.8298 |
| 0.1123 | 9.2182 | 1014 | 0.6862 | 0.7764 | 0.6862 | 0.8284 |
| 0.1123 | 9.2364 | 1016 | 0.6755 | 0.7764 | 0.6755 | 0.8219 |
| 0.1123 | 9.2545 | 1018 | 0.6606 | 0.7250 | 0.6606 | 0.8128 |
| 0.1123 | 9.2727 | 1020 | 0.6495 | 0.7250 | 0.6495 | 0.8059 |
| 0.1123 | 9.2909 | 1022 | 0.6350 | 0.7250 | 0.6350 | 0.7969 |
| 0.1123 | 9.3091 | 1024 | 0.6237 | 0.7250 | 0.6237 | 0.7897 |
| 0.1123 | 9.3273 | 1026 | 0.6124 | 0.7250 | 0.6124 | 0.7826 |
| 0.1123 | 9.3455 | 1028 | 0.6071 | 0.7250 | 0.6071 | 0.7791 |
| 0.1123 | 9.3636 | 1030 | 0.6096 | 0.7250 | 0.6096 | 0.7808 |
| 0.1123 | 9.3818 | 1032 | 0.6145 | 0.7250 | 0.6145 | 0.7839 |
| 0.1123 | 9.4 | 1034 | 0.6239 | 0.7250 | 0.6239 | 0.7898 |
| 0.1123 | 9.4182 | 1036 | 0.6356 | 0.7250 | 0.6356 | 0.7972 |
| 0.1123 | 9.4364 | 1038 | 0.6450 | 0.7250 | 0.6450 | 0.8031 |
| 0.1123 | 9.4545 | 1040 | 0.6462 | 0.7250 | 0.6462 | 0.8039 |
| 0.1123 | 9.4727 | 1042 | 0.6482 | 0.7250 | 0.6482 | 0.8051 |
| 0.1123 | 9.4909 | 1044 | 0.6474 | 0.7250 | 0.6474 | 0.8046 |
| 0.1123 | 9.5091 | 1046 | 0.6431 | 0.7250 | 0.6431 | 0.8019 |
| 0.1123 | 9.5273 | 1048 | 0.6364 | 0.7250 | 0.6364 | 0.7978 |
| 0.1123 | 9.5455 | 1050 | 0.6344 | 0.7250 | 0.6344 | 0.7965 |
| 0.1123 | 9.5636 | 1052 | 0.6338 | 0.7250 | 0.6338 | 0.7961 |
| 0.1123 | 9.5818 | 1054 | 0.6370 | 0.7250 | 0.6370 | 0.7982 |
| 0.1123 | 9.6 | 1056 | 0.6435 | 0.7250 | 0.6435 | 0.8022 |
| 0.1123 | 9.6182 | 1058 | 0.6471 | 0.7250 | 0.6471 | 0.8044 |
| 0.1123 | 9.6364 | 1060 | 0.6494 | 0.7250 | 0.6494 | 0.8058 |
| 0.1123 | 9.6545 | 1062 | 0.6493 | 0.7250 | 0.6493 | 0.8058 |
| 0.1123 | 9.6727 | 1064 | 0.6495 | 0.7250 | 0.6495 | 0.8059 |
| 0.1123 | 9.6909 | 1066 | 0.6496 | 0.7250 | 0.6496 | 0.8060 |
| 0.1123 | 9.7091 | 1068 | 0.6490 | 0.7250 | 0.6490 | 0.8056 |
| 0.1123 | 9.7273 | 1070 | 0.6485 | 0.7250 | 0.6485 | 0.8053 |
| 0.1123 | 9.7455 | 1072 | 0.6470 | 0.7250 | 0.6470 | 0.8044 |
| 0.1123 | 9.7636 | 1074 | 0.6464 | 0.7250 | 0.6464 | 0.8040 |
| 0.1123 | 9.7818 | 1076 | 0.6458 | 0.7250 | 0.6458 | 0.8036 |
| 0.1123 | 9.8 | 1078 | 0.6444 | 0.7250 | 0.6444 | 0.8028 |
| 0.1123 | 9.8182 | 1080 | 0.6437 | 0.7250 | 0.6437 | 0.8023 |
| 0.1123 | 9.8364 | 1082 | 0.6431 | 0.7250 | 0.6431 | 0.8019 |
| 0.1123 | 9.8545 | 1084 | 0.6428 | 0.7250 | 0.6428 | 0.8017 |
| 0.1123 | 9.8727 | 1086 | 0.6433 | 0.7250 | 0.6433 | 0.8021 |
| 0.1123 | 9.8909 | 1088 | 0.6439 | 0.7250 | 0.6439 | 0.8024 |
| 0.1123 | 9.9091 | 1090 | 0.6446 | 0.7250 | 0.6446 | 0.8029 |
| 0.1123 | 9.9273 | 1092 | 0.6455 | 0.7250 | 0.6455 | 0.8035 |
| 0.1123 | 9.9455 | 1094 | 0.6463 | 0.7250 | 0.6463 | 0.8039 |
| 0.1123 | 9.9636 | 1096 | 0.6473 | 0.7250 | 0.6473 | 0.8046 |
| 0.1123 | 9.9818 | 1098 | 0.6478 | 0.7250 | 0.6478 | 0.8049 |
| 0.1123 | 10.0 | 1100 | 0.6480 | 0.7250 | 0.6480 | 0.8050 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
strangerzonehf/Flux-Super-Realism-LoRA
|
strangerzonehf
| 2024-11-27T20:50:39Z | 46,436 | 173 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"Super-Realism",
"Flux.1-Dev",
"Dynamic-Realism",
"Realistic",
"Photorealism",
"Hi-Res",
"UltraRealism",
"Diffusion",
"Face",
"safetensors",
"Realism-Engine",
"RAW",
"4K",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] |
text-to-image
| 2024-11-12T18:40:16Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
- Super-Realism
- Flux.1-Dev
- Dynamic-Realism
- Realistic
- Photorealism
- Hi-Res
- UltraRealism
- Diffusion
- Face
- safetensors
- Realism-Engine
- RAW
- 4K
widget:
- text: >-
Super Realism, Woman in a red jacket, snowy, in the style of hyper-realistic
portraiture, caninecore, mountainous vistas, timeless beauty, palewave,
iconic, distinctive noses --ar 72:101 --stylize 750 --v 6
output:
url: images/3.png
- text: >-
Super Realism, Headshot of handsome young man, wearing dark gray sweater
with buttons and big shawl collar, brown hair and short beard, serious look
on his face, black background, soft studio lighting, portrait photography
--ar 85:128 --v 6.0 --style rawHeadshot of handsome young man, wearing dark
gray sweater with buttons and big shawl collar, brown hair and short beard,
serious look on his face, black background, soft studio lighting, portrait
photography --ar 85:128 --v 6.0 --style rawHeadshot of handsome young man,
wearing dark gray sweater with buttons and big shawl collar, brown hair and
short beard, serious look on his face, black background, soft studio
lighting, portrait photography --ar 85:128 --v 6.0 --style raw
output:
url: images/2.png
- text: >-
Super Realism, High-resolution photograph, woman, UHD, photorealistic, shot
on a Sony A7III --chaos 20 --ar 1:2 --style raw --stylize 250
output:
url: images/1.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Super Realism
license: mit
---
<Gallery />
## Model description for super realism engine
Image Processing Parameters
| Parameter | Value | Parameter | Value |
|---------------------------|--------|---------------------------|--------|
| LR Scheduler | constant | Noise Offset | 0.03 |
| Optimizer | AdamW | Multires Noise Discount | 0.1 |
| Network Dim | 64 | Multires Noise Iterations | 10 |
| Network Alpha | 32 | Repeat & Steps | 30 & 4380|
| Epoch | 20 | Save Every N Epochs | 1 |
## Comparison between the base model and related models.
Comparison between the base model FLUX.1-dev and its adapter, a LoRA model tuned for super-realistic realism.
[ 28 steps ]
However, it performs better in various aspects compared to its previous models, including face realism, ultra-realism, and others.
previous versions [ 28 steps ]
## Previous Model Links
| Model Name | Description | Link |
|------------------------------------------|------------------------------|------------------------------------------------------------------------------------------|
| **Canopus-LoRA-Flux-FaceRealism** | LoRA model for Face Realism | [Canopus-LoRA-Flux-FaceRealism](https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-FaceRealism) |
| **Canopus-LoRA-Flux-UltraRealism-2.0** | LoRA model for Ultra Realism | [Canopus-LoRA-Flux-UltraRealism-2.0](https://huggingface.co/prithivMLmods/Canopus-LoRA-Flux-UltraRealism-2.0) |
| **Flux.1-Dev-LoRA-HDR-Realism [Experimental Version]** | LoRA model for HDR Realism | [Flux.1-Dev-LoRA-HDR-Realism](https://huggingface.co/prithivMLmods/Flux.1-Dev-LoRA-HDR-Realism) |
| **Flux-Realism-FineDetailed** | Fine-detailed realism-focused model | [Flux-Realism-FineDetailed](https://huggingface.co/prithivMLmods/Flux-Realism-FineDetailed) |
## Hosted/Demo Links
| Demo Name | Description | Link |
|----------------------------|----------------------------|--------------------------------------------------------------------------------------|
| **FLUX-LoRA-DLC** | Demo for FLUX LoRA DLC | [FLUX-LoRA-DLC](https://huggingface.co/spaces/prithivMLmods/FLUX-LoRA-DLC) |
| **FLUX-REALISM** | Demo for FLUX Realism | [FLUX-REALISM](https://huggingface.co/spaces/prithivMLmods/FLUX-REALISM) |
## Model Training Basic Details
| Feature | Description |
|--------------------------------|-------------------------------------|
| **Labeling** | florence2-en (natural language & English) |
| **Total Images Used for Training** | 55 [Hi-Res] |
| **Best Dimensions** | - 1024 x 1024 (Default) |
| | - 768 x 1024 |
## Flux-Super-Realism-LoRA Model GitHub
| Repository Link | Description |
|---------------------------------------------------------------|----------------------------------------------|
| [Flux-Super-Realism-LoRA](https://github.com/Stranger-Zone/Flux-Super-Realism-LoRA/tree/main) | Flux Super Realism LoRA model repository for high-quality realism generation |
## API Usage / Quick Usage
```python
from gradio_client import Client
client = Client("prithivMLmods/FLUX-REALISM")
result = client.predict(
prompt="A tiny astronaut hatching from an egg on the moon, 4k, planet theme",
seed=0,
width=1024,
height=1024,
guidance_scale=6,
randomize_seed=True,
api_name="/run"
#takes minimum of 30 seconds
)
print(result)
```
## Setting Up Flux Space
```python
import torch
from pipelines import DiffusionPipeline
base_model = "black-forest-labs/FLUX.1-dev"
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
lora_repo = "strangerzonehf/Flux-Super-Realism-LoRA"
trigger_word = "Super Realism" #triggerword
pipe.load_lora_weights(lora_repo)
device = torch.device("cuda")
pipe.to(device)
```
## Trigger words
> [!WARNING]
> **Trigger words:** You should use `Super Realism` to trigger the image generation.
- The trigger word is not mandatory; ensure that words like "realistic" and "realism" appear in the image description. The "super realism" trigger word should prompt an exact match to the reference image in the showcase.
## Download model
Weights for this model are available in Safetensors format.
[Download](/strangerzonehf/Flux-Super-Realism-LoRA/tree/main) them in the Files & versions tab.
|
marklicata/M365_h1_retrained_v1
|
marklicata
| 2024-11-27T20:45:32Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:marklicata/M365_h1_base",
"base_model:finetune:marklicata/M365_h1_base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T18:15:57Z |
---
library_name: transformers
license: apache-2.0
base_model: marklicata/M365_h1_base
tags:
- generated_from_trainer
model-index:
- name: M365_h1_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. -->
# M365_h1_v1
This model is a fine-tuned version of [marklicata/M365_h1_base](https://huggingface.co/marklicata/M365_h1_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0058
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2553 | 1.0 | 800 | 0.0057 |
| 0.0001 | 2.0 | 1600 | 0.0055 |
| 0.0001 | 3.0 | 2400 | 0.0058 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cpu
- Datasets 3.1.0
- Tokenizers 0.20.3
|
fbaldassarri/EleutherAI_pythia-70m-deduped-autoawq-int4-gs128-sym
|
fbaldassarri
| 2024-11-27T20:41:29Z | 8 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel-autoround",
"intel",
"autoawq",
"awq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-70m-deduped",
"base_model:quantized:EleutherAI/pythia-70m-deduped",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:41:02Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel-autoround
- intel
- autoawq
- awq
- woq
license: apache-2.0
model_name: Pythia 70m deduped
base_model: EleutherAI/pythia-70m-deduped
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Symmetrical Quantization
- Method AutoAWQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-70m-deduped has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel AutoRound wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-70m-deduped"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, True
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-70m-deduped-autoawq-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warranty. It has been developed only for research purposes.
|
fbaldassarri/EleutherAI_pythia-70m-deduped-autoawq-int4-gs128-asym
|
fbaldassarri
| 2024-11-27T20:40:42Z | 7 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel-autoround",
"intel",
"autoawq",
"awq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-70m-deduped",
"base_model:quantized:EleutherAI/pythia-70m-deduped",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:40:11Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel-autoround
- intel
- autoawq
- awq
- woq
license: apache-2.0
model_name: Pythia 70m deduped
base_model: EleutherAI/pythia-70m-deduped
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Asymmetrical Quantization
- Method AutoAWQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-70m-deduped has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel AutoRound wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-70m-deduped"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, False
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-70m-deduped-autoawq-int4-gs128-asym"
autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warranty. It has been developed only for research purposes.
|
cestwc/roberta-base-detect-1126
|
cestwc
| 2024-11-27T20:40:04Z | 123 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T20:11:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k5_task1_organization_fold1
|
MayBashendy
| 2024-11-27T20:40:03Z | 164 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T20:33:52Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_run2_AugV5_k5_task1_organization_fold1
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. -->
# Arabic_FineTuningAraBERT_run2_AugV5_k5_task1_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5708
- Qwk: 0.7290
- Mse: 0.5708
- Rmse: 0.7555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0328 | 2 | 5.4407 | 0.0053 | 5.4407 | 2.3325 |
| No log | 0.0656 | 4 | 3.6682 | 0.0162 | 3.6682 | 1.9152 |
| No log | 0.0984 | 6 | 2.4874 | -0.0752 | 2.4874 | 1.5771 |
| No log | 0.1311 | 8 | 1.4567 | 0.0403 | 1.4567 | 1.2069 |
| No log | 0.1639 | 10 | 1.0544 | 0.0297 | 1.0544 | 1.0268 |
| No log | 0.1967 | 12 | 1.0815 | 0.2479 | 1.0815 | 1.0400 |
| No log | 0.2295 | 14 | 1.9749 | 0.125 | 1.9749 | 1.4053 |
| No log | 0.2623 | 16 | 2.2001 | 0.1499 | 2.2001 | 1.4833 |
| No log | 0.2951 | 18 | 1.8171 | 0.1442 | 1.8171 | 1.3480 |
| No log | 0.3279 | 20 | 1.4504 | 0.2097 | 1.4504 | 1.2043 |
| No log | 0.3607 | 22 | 1.2281 | 0.0841 | 1.2281 | 1.1082 |
| No log | 0.3934 | 24 | 0.9686 | 0.2829 | 0.9686 | 0.9842 |
| No log | 0.4262 | 26 | 0.7863 | 0.3636 | 0.7863 | 0.8867 |
| No log | 0.4590 | 28 | 0.7410 | 0.24 | 0.7410 | 0.8608 |
| No log | 0.4918 | 30 | 0.7405 | 0.2391 | 0.7405 | 0.8606 |
| No log | 0.5246 | 32 | 0.7216 | 0.2391 | 0.7216 | 0.8495 |
| No log | 0.5574 | 34 | 0.8246 | 0.24 | 0.8246 | 0.9081 |
| No log | 0.5902 | 36 | 1.1392 | -0.0442 | 1.1392 | 1.0673 |
| No log | 0.6230 | 38 | 1.4753 | -0.3725 | 1.4753 | 1.2146 |
| No log | 0.6557 | 40 | 1.9224 | -0.2008 | 1.9224 | 1.3865 |
| No log | 0.6885 | 42 | 1.8742 | -0.0726 | 1.8742 | 1.3690 |
| No log | 0.7213 | 44 | 1.5295 | 0.0403 | 1.5295 | 1.2367 |
| No log | 0.7541 | 46 | 1.3468 | 0.1064 | 1.3468 | 1.1605 |
| No log | 0.7869 | 48 | 1.2969 | 0.1884 | 1.2969 | 1.1388 |
| No log | 0.8197 | 50 | 1.2029 | 0.3691 | 1.2029 | 1.0968 |
| No log | 0.8525 | 52 | 1.0192 | 0.3691 | 1.0192 | 1.0095 |
| No log | 0.8852 | 54 | 0.8171 | 0.4286 | 0.8171 | 0.9039 |
| No log | 0.9180 | 56 | 0.7251 | 0.3152 | 0.7251 | 0.8515 |
| No log | 0.9508 | 58 | 0.6777 | 0.3152 | 0.6777 | 0.8232 |
| No log | 0.9836 | 60 | 0.6395 | 0.3704 | 0.6395 | 0.7997 |
| No log | 1.0164 | 62 | 0.6348 | 0.3704 | 0.6348 | 0.7968 |
| No log | 1.0492 | 64 | 0.6345 | 0.5024 | 0.6345 | 0.7965 |
| No log | 1.0820 | 66 | 0.7077 | 0.4659 | 0.7077 | 0.8413 |
| No log | 1.1148 | 68 | 0.7712 | 0.4615 | 0.7712 | 0.8782 |
| No log | 1.1475 | 70 | 0.7131 | 0.4659 | 0.7131 | 0.8445 |
| No log | 1.1803 | 72 | 0.6181 | 0.4766 | 0.6181 | 0.7862 |
| No log | 1.2131 | 74 | 0.6452 | 0.4904 | 0.6452 | 0.8033 |
| No log | 1.2459 | 76 | 0.8310 | 0.5035 | 0.8310 | 0.9116 |
| No log | 1.2787 | 78 | 0.8604 | 0.5233 | 0.8604 | 0.9276 |
| No log | 1.3115 | 80 | 0.7879 | 0.5333 | 0.7879 | 0.8876 |
| No log | 1.3443 | 82 | 0.6988 | 0.5401 | 0.6988 | 0.8359 |
| No log | 1.3770 | 84 | 0.6101 | 0.6957 | 0.6101 | 0.7811 |
| No log | 1.4098 | 86 | 0.5766 | 0.6375 | 0.5766 | 0.7593 |
| No log | 1.4426 | 88 | 0.6370 | 0.6535 | 0.6370 | 0.7981 |
| No log | 1.4754 | 90 | 0.8089 | 0.5828 | 0.8089 | 0.8994 |
| No log | 1.5082 | 92 | 0.8828 | 0.4077 | 0.8828 | 0.9396 |
| No log | 1.5410 | 94 | 0.8342 | 0.4411 | 0.8342 | 0.9134 |
| No log | 1.5738 | 96 | 0.7711 | 0.4235 | 0.7711 | 0.8781 |
| No log | 1.6066 | 98 | 0.6988 | 0.4292 | 0.6988 | 0.8359 |
| No log | 1.6393 | 100 | 0.6775 | 0.6075 | 0.6775 | 0.8231 |
| No log | 1.6721 | 102 | 0.6565 | 0.6075 | 0.6565 | 0.8102 |
| No log | 1.7049 | 104 | 0.6414 | 0.5024 | 0.6414 | 0.8009 |
| No log | 1.7377 | 106 | 0.6343 | 0.4893 | 0.6343 | 0.7964 |
| No log | 1.7705 | 108 | 0.6032 | 0.5882 | 0.6032 | 0.7767 |
| No log | 1.8033 | 110 | 0.5649 | 0.5984 | 0.5649 | 0.7516 |
| No log | 1.8361 | 112 | 0.5160 | 0.7034 | 0.5160 | 0.7183 |
| No log | 1.8689 | 114 | 0.5001 | 0.5919 | 0.5001 | 0.7072 |
| No log | 1.9016 | 116 | 0.5273 | 0.6016 | 0.5273 | 0.7261 |
| No log | 1.9344 | 118 | 0.4879 | 0.5243 | 0.4879 | 0.6985 |
| No log | 1.9672 | 120 | 0.5032 | 0.7034 | 0.5032 | 0.7094 |
| No log | 2.0 | 122 | 0.6416 | 0.5401 | 0.6416 | 0.8010 |
| No log | 2.0328 | 124 | 0.7299 | 0.5401 | 0.7299 | 0.8543 |
| No log | 2.0656 | 126 | 0.7937 | 0.5092 | 0.7937 | 0.8909 |
| No log | 2.0984 | 128 | 0.8235 | 0.5704 | 0.8235 | 0.9075 |
| No log | 2.1311 | 130 | 0.7867 | 0.5116 | 0.7867 | 0.8870 |
| No log | 2.1639 | 132 | 0.7737 | 0.5772 | 0.7737 | 0.8796 |
| No log | 2.1967 | 134 | 0.7763 | 0.6149 | 0.7763 | 0.8811 |
| No log | 2.2295 | 136 | 0.7929 | 0.5987 | 0.7929 | 0.8905 |
| No log | 2.2623 | 138 | 0.8726 | 0.6111 | 0.8726 | 0.9341 |
| No log | 2.2951 | 140 | 0.8155 | 0.6198 | 0.8155 | 0.9031 |
| No log | 2.3279 | 142 | 0.7403 | 0.5935 | 0.7403 | 0.8604 |
| No log | 2.3607 | 144 | 0.6972 | 0.5552 | 0.6972 | 0.8350 |
| No log | 2.3934 | 146 | 0.4972 | 0.6744 | 0.4972 | 0.7052 |
| No log | 2.4262 | 148 | 0.4871 | 0.5919 | 0.4871 | 0.6979 |
| No log | 2.4590 | 150 | 0.4654 | 0.6207 | 0.4654 | 0.6822 |
| No log | 2.4918 | 152 | 0.5507 | 0.5475 | 0.5507 | 0.7421 |
| No log | 2.5246 | 154 | 0.8124 | 0.5299 | 0.8124 | 0.9013 |
| No log | 2.5574 | 156 | 0.8602 | 0.5735 | 0.8602 | 0.9275 |
| No log | 2.5902 | 158 | 0.8924 | 0.5772 | 0.8924 | 0.9447 |
| No log | 2.6230 | 160 | 0.9442 | 0.5492 | 0.9442 | 0.9717 |
| No log | 2.6557 | 162 | 0.7739 | 0.5735 | 0.7739 | 0.8797 |
| No log | 2.6885 | 164 | 0.7375 | 0.5735 | 0.7375 | 0.8588 |
| No log | 2.7213 | 166 | 0.6168 | 0.5609 | 0.6168 | 0.7854 |
| No log | 2.7541 | 168 | 0.5258 | 0.6111 | 0.5258 | 0.7251 |
| No log | 2.7869 | 170 | 0.4692 | 0.6392 | 0.4692 | 0.6850 |
| No log | 2.8197 | 172 | 0.4254 | 0.7143 | 0.4254 | 0.6522 |
| No log | 2.8525 | 174 | 0.3951 | 0.7552 | 0.3951 | 0.6286 |
| No log | 2.8852 | 176 | 0.4062 | 0.72 | 0.4062 | 0.6373 |
| No log | 2.9180 | 178 | 0.4419 | 0.7279 | 0.4419 | 0.6648 |
| No log | 2.9508 | 180 | 0.6230 | 0.7093 | 0.6230 | 0.7893 |
| No log | 2.9836 | 182 | 0.7494 | 0.5855 | 0.7494 | 0.8657 |
| No log | 3.0164 | 184 | 0.6254 | 0.6839 | 0.6254 | 0.7908 |
| No log | 3.0492 | 186 | 0.4090 | 0.75 | 0.4090 | 0.6395 |
| No log | 3.0820 | 188 | 0.3242 | 0.7482 | 0.3242 | 0.5694 |
| No log | 3.1148 | 190 | 0.3150 | 0.7482 | 0.3150 | 0.5613 |
| No log | 3.1475 | 192 | 0.3282 | 0.7709 | 0.3282 | 0.5729 |
| No log | 3.1803 | 194 | 0.3540 | 0.7829 | 0.3540 | 0.5950 |
| No log | 3.2131 | 196 | 0.4607 | 0.7220 | 0.4607 | 0.6788 |
| No log | 3.2459 | 198 | 0.5655 | 0.6138 | 0.5655 | 0.7520 |
| No log | 3.2787 | 200 | 0.6501 | 0.6047 | 0.6501 | 0.8063 |
| No log | 3.3115 | 202 | 0.5840 | 0.5939 | 0.5840 | 0.7642 |
| No log | 3.3443 | 204 | 0.5639 | 0.6316 | 0.5639 | 0.7510 |
| No log | 3.3770 | 206 | 0.5230 | 0.6557 | 0.5230 | 0.7232 |
| No log | 3.4098 | 208 | 0.5924 | 0.6316 | 0.5924 | 0.7697 |
| No log | 3.4426 | 210 | 0.7257 | 0.6047 | 0.7257 | 0.8519 |
| No log | 3.4754 | 212 | 0.7743 | 0.6047 | 0.7743 | 0.8800 |
| No log | 3.5082 | 214 | 0.7313 | 0.6047 | 0.7313 | 0.8552 |
| No log | 3.5410 | 216 | 0.5537 | 0.6525 | 0.5537 | 0.7441 |
| No log | 3.5738 | 218 | 0.5225 | 0.6934 | 0.5225 | 0.7228 |
| No log | 3.6066 | 220 | 0.6474 | 0.6047 | 0.6474 | 0.8046 |
| No log | 3.6393 | 222 | 0.7149 | 0.6047 | 0.7149 | 0.8455 |
| No log | 3.6721 | 224 | 0.6438 | 0.6138 | 0.6438 | 0.8024 |
| No log | 3.7049 | 226 | 0.4874 | 0.6642 | 0.4874 | 0.6981 |
| No log | 3.7377 | 228 | 0.3987 | 0.7619 | 0.3987 | 0.6314 |
| No log | 3.7705 | 230 | 0.3779 | 0.8042 | 0.3779 | 0.6147 |
| No log | 3.8033 | 232 | 0.3486 | 0.7709 | 0.3486 | 0.5904 |
| No log | 3.8361 | 234 | 0.3631 | 0.8082 | 0.3631 | 0.6026 |
| No log | 3.8689 | 236 | 0.4188 | 0.7771 | 0.4188 | 0.6472 |
| No log | 3.9016 | 238 | 0.4932 | 0.7422 | 0.4932 | 0.7023 |
| No log | 3.9344 | 240 | 0.5500 | 0.76 | 0.5500 | 0.7416 |
| No log | 3.9672 | 242 | 0.5678 | 0.7631 | 0.5678 | 0.7536 |
| No log | 4.0 | 244 | 0.6002 | 0.76 | 0.6002 | 0.7747 |
| No log | 4.0328 | 246 | 0.5664 | 0.76 | 0.5664 | 0.7526 |
| No log | 4.0656 | 248 | 0.5386 | 0.6706 | 0.5386 | 0.7339 |
| No log | 4.0984 | 250 | 0.4895 | 0.7358 | 0.4895 | 0.6997 |
| No log | 4.1311 | 252 | 0.4322 | 0.7255 | 0.4322 | 0.6574 |
| No log | 4.1639 | 254 | 0.4429 | 0.7138 | 0.4430 | 0.6655 |
| No log | 4.1967 | 256 | 0.5619 | 0.6038 | 0.5619 | 0.7496 |
| No log | 4.2295 | 258 | 0.5836 | 0.5725 | 0.5836 | 0.7639 |
| No log | 4.2623 | 260 | 0.4851 | 0.5679 | 0.4851 | 0.6965 |
| No log | 4.2951 | 262 | 0.3610 | 0.6957 | 0.3610 | 0.6008 |
| No log | 4.3279 | 264 | 0.3387 | 0.8 | 0.3387 | 0.5820 |
| No log | 4.3607 | 266 | 0.3413 | 0.8 | 0.3413 | 0.5842 |
| No log | 4.3934 | 268 | 0.3466 | 0.7640 | 0.3466 | 0.5888 |
| No log | 4.4262 | 270 | 0.4685 | 0.7475 | 0.4685 | 0.6845 |
| No log | 4.4590 | 272 | 0.5723 | 0.6580 | 0.5723 | 0.7565 |
| No log | 4.4918 | 274 | 0.5120 | 0.7342 | 0.5120 | 0.7156 |
| No log | 4.5246 | 276 | 0.4463 | 0.76 | 0.4463 | 0.6681 |
| No log | 4.5574 | 278 | 0.4099 | 0.7812 | 0.4099 | 0.6402 |
| No log | 4.5902 | 280 | 0.4304 | 0.7864 | 0.4304 | 0.6560 |
| No log | 4.6230 | 282 | 0.4374 | 0.7129 | 0.4374 | 0.6614 |
| No log | 4.6557 | 284 | 0.4654 | 0.7631 | 0.4654 | 0.6822 |
| No log | 4.6885 | 286 | 0.4739 | 0.7631 | 0.4739 | 0.6884 |
| No log | 4.7213 | 288 | 0.4704 | 0.7631 | 0.4704 | 0.6858 |
| No log | 4.7541 | 290 | 0.4241 | 0.7273 | 0.4241 | 0.6512 |
| No log | 4.7869 | 292 | 0.3672 | 0.8082 | 0.3672 | 0.6060 |
| No log | 4.8197 | 294 | 0.3628 | 0.7709 | 0.3628 | 0.6023 |
| No log | 4.8525 | 296 | 0.4340 | 0.7709 | 0.4340 | 0.6588 |
| No log | 4.8852 | 298 | 0.4600 | 0.6936 | 0.4600 | 0.6782 |
| No log | 4.9180 | 300 | 0.4070 | 0.7709 | 0.4070 | 0.6380 |
| No log | 4.9508 | 302 | 0.4163 | 0.7063 | 0.4163 | 0.6452 |
| No log | 4.9836 | 304 | 0.5164 | 0.6839 | 0.5164 | 0.7186 |
| No log | 5.0164 | 306 | 0.5863 | 0.6839 | 0.5863 | 0.7657 |
| No log | 5.0492 | 308 | 0.5607 | 0.6839 | 0.5607 | 0.7488 |
| No log | 5.0820 | 310 | 0.5424 | 0.6488 | 0.5424 | 0.7365 |
| No log | 5.1148 | 312 | 0.5485 | 0.6216 | 0.5485 | 0.7406 |
| No log | 5.1475 | 314 | 0.5118 | 0.6216 | 0.5118 | 0.7154 |
| No log | 5.1803 | 316 | 0.5628 | 0.6580 | 0.5628 | 0.7502 |
| No log | 5.2131 | 318 | 0.5660 | 0.6580 | 0.5660 | 0.7523 |
| No log | 5.2459 | 320 | 0.5301 | 0.6488 | 0.5301 | 0.7281 |
| No log | 5.2787 | 322 | 0.5850 | 0.6543 | 0.5850 | 0.7649 |
| No log | 5.3115 | 324 | 0.6540 | 0.6543 | 0.6540 | 0.8087 |
| No log | 5.3443 | 326 | 0.7849 | 0.6478 | 0.7849 | 0.8860 |
| No log | 5.3770 | 328 | 0.7930 | 0.6387 | 0.7930 | 0.8905 |
| No log | 5.4098 | 330 | 0.7928 | 0.7042 | 0.7928 | 0.8904 |
| No log | 5.4426 | 332 | 0.7574 | 0.6872 | 0.7574 | 0.8703 |
| No log | 5.4754 | 334 | 0.6866 | 0.7631 | 0.6866 | 0.8286 |
| No log | 5.5082 | 336 | 0.6236 | 0.7631 | 0.6236 | 0.7897 |
| No log | 5.5410 | 338 | 0.5898 | 0.7631 | 0.5898 | 0.7680 |
| No log | 5.5738 | 340 | 0.5833 | 0.7631 | 0.5833 | 0.7637 |
| No log | 5.6066 | 342 | 0.5900 | 0.7631 | 0.5900 | 0.7681 |
| No log | 5.6393 | 344 | 0.6086 | 0.6557 | 0.6086 | 0.7801 |
| No log | 5.6721 | 346 | 0.5767 | 0.7273 | 0.5767 | 0.7594 |
| No log | 5.7049 | 348 | 0.5143 | 0.7273 | 0.5143 | 0.7171 |
| No log | 5.7377 | 350 | 0.4550 | 0.6936 | 0.4550 | 0.6746 |
| No log | 5.7705 | 352 | 0.4206 | 0.7921 | 0.4206 | 0.6485 |
| No log | 5.8033 | 354 | 0.3975 | 0.8082 | 0.3975 | 0.6305 |
| No log | 5.8361 | 356 | 0.4216 | 0.7866 | 0.4216 | 0.6493 |
| No log | 5.8689 | 358 | 0.4361 | 0.7316 | 0.4361 | 0.6604 |
| No log | 5.9016 | 360 | 0.4841 | 0.7267 | 0.4841 | 0.6958 |
| No log | 5.9344 | 362 | 0.5666 | 0.7093 | 0.5666 | 0.7527 |
| No log | 5.9672 | 364 | 0.5554 | 0.7093 | 0.5554 | 0.7452 |
| No log | 6.0 | 366 | 0.5805 | 0.6580 | 0.5805 | 0.7619 |
| No log | 6.0328 | 368 | 0.5489 | 0.6525 | 0.5489 | 0.7409 |
| No log | 6.0656 | 370 | 0.4771 | 0.7354 | 0.4771 | 0.6907 |
| No log | 6.0984 | 372 | 0.4180 | 0.7658 | 0.4180 | 0.6465 |
| No log | 6.1311 | 374 | 0.4375 | 0.7619 | 0.4375 | 0.6614 |
| No log | 6.1639 | 376 | 0.5066 | 0.7016 | 0.5066 | 0.7117 |
| No log | 6.1967 | 378 | 0.5743 | 0.7093 | 0.5743 | 0.7578 |
| No log | 6.2295 | 380 | 0.6659 | 0.6387 | 0.6659 | 0.8160 |
| No log | 6.2623 | 382 | 0.6569 | 0.7093 | 0.6569 | 0.8105 |
| No log | 6.2951 | 384 | 0.5838 | 0.76 | 0.5838 | 0.7641 |
| No log | 6.3279 | 386 | 0.6007 | 0.76 | 0.6007 | 0.7751 |
| No log | 6.3607 | 388 | 0.6672 | 0.7093 | 0.6672 | 0.8168 |
| No log | 6.3934 | 390 | 0.6921 | 0.6580 | 0.6921 | 0.8319 |
| No log | 6.4262 | 392 | 0.6218 | 0.7093 | 0.6218 | 0.7885 |
| No log | 6.4590 | 394 | 0.5373 | 0.7093 | 0.5373 | 0.7330 |
| No log | 6.4918 | 396 | 0.4549 | 0.7407 | 0.4549 | 0.6745 |
| No log | 6.5246 | 398 | 0.4125 | 0.7864 | 0.4125 | 0.6422 |
| No log | 6.5574 | 400 | 0.4324 | 0.7712 | 0.4324 | 0.6576 |
| No log | 6.5902 | 402 | 0.4988 | 0.6755 | 0.4988 | 0.7063 |
| No log | 6.6230 | 404 | 0.6453 | 0.6580 | 0.6453 | 0.8033 |
| No log | 6.6557 | 406 | 0.8246 | 0.5389 | 0.8246 | 0.9081 |
| No log | 6.6885 | 408 | 0.8983 | 0.5389 | 0.8983 | 0.9478 |
| No log | 6.7213 | 410 | 0.8732 | 0.5449 | 0.8732 | 0.9345 |
| No log | 6.7541 | 412 | 0.7490 | 0.6122 | 0.7490 | 0.8655 |
| No log | 6.7869 | 414 | 0.6532 | 0.6789 | 0.6532 | 0.8082 |
| No log | 6.8197 | 416 | 0.6035 | 0.6789 | 0.6035 | 0.7769 |
| No log | 6.8525 | 418 | 0.6023 | 0.6789 | 0.6023 | 0.7761 |
| No log | 6.8852 | 420 | 0.6411 | 0.6580 | 0.6411 | 0.8007 |
| No log | 6.9180 | 422 | 0.7019 | 0.6047 | 0.7019 | 0.8378 |
| No log | 6.9508 | 424 | 0.6952 | 0.6047 | 0.6952 | 0.8338 |
| No log | 6.9836 | 426 | 0.6275 | 0.6138 | 0.6275 | 0.7922 |
| No log | 7.0164 | 428 | 0.5348 | 0.7290 | 0.5348 | 0.7313 |
| No log | 7.0492 | 430 | 0.4611 | 0.7727 | 0.4611 | 0.6790 |
| No log | 7.0820 | 432 | 0.4000 | 0.8218 | 0.4000 | 0.6325 |
| No log | 7.1148 | 434 | 0.3762 | 0.8218 | 0.3762 | 0.6133 |
| No log | 7.1475 | 436 | 0.3881 | 0.8218 | 0.3881 | 0.6230 |
| No log | 7.1803 | 438 | 0.4541 | 0.7727 | 0.4541 | 0.6739 |
| No log | 7.2131 | 440 | 0.5865 | 0.6866 | 0.5865 | 0.7659 |
| No log | 7.2459 | 442 | 0.7672 | 0.5882 | 0.7672 | 0.8759 |
| No log | 7.2787 | 444 | 0.8166 | 0.5812 | 0.8166 | 0.9037 |
| No log | 7.3115 | 446 | 0.7483 | 0.5882 | 0.7483 | 0.8650 |
| No log | 7.3443 | 448 | 0.6317 | 0.6763 | 0.6317 | 0.7948 |
| No log | 7.3770 | 450 | 0.5238 | 0.7455 | 0.5238 | 0.7238 |
| No log | 7.4098 | 452 | 0.4334 | 0.7974 | 0.4334 | 0.6583 |
| No log | 7.4426 | 454 | 0.4086 | 0.7974 | 0.4086 | 0.6392 |
| No log | 7.4754 | 456 | 0.4034 | 0.7974 | 0.4034 | 0.6352 |
| No log | 7.5082 | 458 | 0.4349 | 0.7727 | 0.4349 | 0.6594 |
| No log | 7.5410 | 460 | 0.5037 | 0.7290 | 0.5037 | 0.7097 |
| No log | 7.5738 | 462 | 0.5616 | 0.7290 | 0.5616 | 0.7494 |
| No log | 7.6066 | 464 | 0.5656 | 0.7645 | 0.5656 | 0.7520 |
| No log | 7.6393 | 466 | 0.5380 | 0.7645 | 0.5380 | 0.7335 |
| No log | 7.6721 | 468 | 0.5102 | 0.7455 | 0.5102 | 0.7143 |
| No log | 7.7049 | 470 | 0.5140 | 0.7455 | 0.5140 | 0.7169 |
| No log | 7.7377 | 472 | 0.5520 | 0.7645 | 0.5520 | 0.7429 |
| No log | 7.7705 | 474 | 0.5909 | 0.6975 | 0.5909 | 0.7687 |
| No log | 7.8033 | 476 | 0.5751 | 0.6975 | 0.5751 | 0.7584 |
| No log | 7.8361 | 478 | 0.5510 | 0.7645 | 0.5510 | 0.7423 |
| No log | 7.8689 | 480 | 0.4865 | 0.7645 | 0.4865 | 0.6975 |
| No log | 7.9016 | 482 | 0.4399 | 0.7455 | 0.4399 | 0.6633 |
| No log | 7.9344 | 484 | 0.3999 | 0.7941 | 0.3999 | 0.6324 |
| No log | 7.9672 | 486 | 0.3899 | 0.7941 | 0.3899 | 0.6244 |
| No log | 8.0 | 488 | 0.3902 | 0.7941 | 0.3902 | 0.6247 |
| No log | 8.0328 | 490 | 0.4051 | 0.7941 | 0.4051 | 0.6365 |
| No log | 8.0656 | 492 | 0.4255 | 0.7727 | 0.4255 | 0.6523 |
| No log | 8.0984 | 494 | 0.4534 | 0.7147 | 0.4534 | 0.6733 |
| No log | 8.1311 | 496 | 0.4461 | 0.7727 | 0.4461 | 0.6679 |
| No log | 8.1639 | 498 | 0.4551 | 0.7147 | 0.4551 | 0.6746 |
| 0.461 | 8.1967 | 500 | 0.4727 | 0.7455 | 0.4727 | 0.6875 |
| 0.461 | 8.2295 | 502 | 0.5097 | 0.7455 | 0.5097 | 0.7140 |
| 0.461 | 8.2623 | 504 | 0.5465 | 0.7645 | 0.5465 | 0.7392 |
| 0.461 | 8.2951 | 506 | 0.5405 | 0.7455 | 0.5405 | 0.7352 |
| 0.461 | 8.3279 | 508 | 0.5164 | 0.7455 | 0.5164 | 0.7186 |
| 0.461 | 8.3607 | 510 | 0.5062 | 0.7455 | 0.5062 | 0.7114 |
| 0.461 | 8.3934 | 512 | 0.5136 | 0.7455 | 0.5136 | 0.7166 |
| 0.461 | 8.4262 | 514 | 0.4977 | 0.7455 | 0.4977 | 0.7055 |
| 0.461 | 8.4590 | 516 | 0.4829 | 0.7455 | 0.4829 | 0.6949 |
| 0.461 | 8.4918 | 518 | 0.4878 | 0.7455 | 0.4878 | 0.6984 |
| 0.461 | 8.5246 | 520 | 0.5079 | 0.7290 | 0.5079 | 0.7127 |
| 0.461 | 8.5574 | 522 | 0.5193 | 0.7036 | 0.5193 | 0.7206 |
| 0.461 | 8.5902 | 524 | 0.5285 | 0.7036 | 0.5285 | 0.7270 |
| 0.461 | 8.6230 | 526 | 0.5283 | 0.7036 | 0.5283 | 0.7269 |
| 0.461 | 8.6557 | 528 | 0.4957 | 0.7290 | 0.4957 | 0.7040 |
| 0.461 | 8.6885 | 530 | 0.4536 | 0.7354 | 0.4536 | 0.6735 |
| 0.461 | 8.7213 | 532 | 0.4156 | 0.7619 | 0.4156 | 0.6447 |
| 0.461 | 8.7541 | 534 | 0.3933 | 0.8042 | 0.3933 | 0.6272 |
| 0.461 | 8.7869 | 536 | 0.3951 | 0.7974 | 0.3951 | 0.6286 |
| 0.461 | 8.8197 | 538 | 0.4041 | 0.7866 | 0.4041 | 0.6357 |
| 0.461 | 8.8525 | 540 | 0.4202 | 0.7866 | 0.4202 | 0.6482 |
| 0.461 | 8.8852 | 542 | 0.4347 | 0.7974 | 0.4347 | 0.6593 |
| 0.461 | 8.9180 | 544 | 0.4512 | 0.7974 | 0.4512 | 0.6717 |
| 0.461 | 8.9508 | 546 | 0.4690 | 0.7391 | 0.4690 | 0.6849 |
| 0.461 | 8.9836 | 548 | 0.4967 | 0.7688 | 0.4967 | 0.7048 |
| 0.461 | 9.0164 | 550 | 0.5178 | 0.7688 | 0.5178 | 0.7196 |
| 0.461 | 9.0492 | 552 | 0.5340 | 0.7093 | 0.5340 | 0.7308 |
| 0.461 | 9.0820 | 554 | 0.5525 | 0.7093 | 0.5525 | 0.7433 |
| 0.461 | 9.1148 | 556 | 0.5540 | 0.7093 | 0.5540 | 0.7443 |
| 0.461 | 9.1475 | 558 | 0.5561 | 0.7455 | 0.5561 | 0.7457 |
| 0.461 | 9.1803 | 560 | 0.5640 | 0.7455 | 0.5640 | 0.7510 |
| 0.461 | 9.2131 | 562 | 0.5706 | 0.7455 | 0.5706 | 0.7554 |
| 0.461 | 9.2459 | 564 | 0.5788 | 0.7645 | 0.5788 | 0.7608 |
| 0.461 | 9.2787 | 566 | 0.5696 | 0.7455 | 0.5696 | 0.7547 |
| 0.461 | 9.3115 | 568 | 0.5558 | 0.7455 | 0.5558 | 0.7455 |
| 0.461 | 9.3443 | 570 | 0.5498 | 0.7455 | 0.5498 | 0.7415 |
| 0.461 | 9.3770 | 572 | 0.5597 | 0.7455 | 0.5597 | 0.7481 |
| 0.461 | 9.4098 | 574 | 0.5742 | 0.7645 | 0.5742 | 0.7578 |
| 0.461 | 9.4426 | 576 | 0.5775 | 0.7290 | 0.5775 | 0.7599 |
| 0.461 | 9.4754 | 578 | 0.5787 | 0.7290 | 0.5787 | 0.7607 |
| 0.461 | 9.5082 | 580 | 0.5827 | 0.7290 | 0.5827 | 0.7634 |
| 0.461 | 9.5410 | 582 | 0.5848 | 0.7290 | 0.5848 | 0.7647 |
| 0.461 | 9.5738 | 584 | 0.5779 | 0.7290 | 0.5779 | 0.7602 |
| 0.461 | 9.6066 | 586 | 0.5699 | 0.7290 | 0.5699 | 0.7549 |
| 0.461 | 9.6393 | 588 | 0.5674 | 0.7290 | 0.5674 | 0.7533 |
| 0.461 | 9.6721 | 590 | 0.5722 | 0.7290 | 0.5722 | 0.7564 |
| 0.461 | 9.7049 | 592 | 0.5813 | 0.7290 | 0.5813 | 0.7624 |
| 0.461 | 9.7377 | 594 | 0.5882 | 0.7290 | 0.5882 | 0.7669 |
| 0.461 | 9.7705 | 596 | 0.5934 | 0.7290 | 0.5934 | 0.7703 |
| 0.461 | 9.8033 | 598 | 0.5938 | 0.7290 | 0.5938 | 0.7706 |
| 0.461 | 9.8361 | 600 | 0.5899 | 0.7290 | 0.5899 | 0.7681 |
| 0.461 | 9.8689 | 602 | 0.5838 | 0.7290 | 0.5838 | 0.7641 |
| 0.461 | 9.9016 | 604 | 0.5778 | 0.7290 | 0.5778 | 0.7602 |
| 0.461 | 9.9344 | 606 | 0.5739 | 0.7290 | 0.5739 | 0.7576 |
| 0.461 | 9.9672 | 608 | 0.5715 | 0.7290 | 0.5715 | 0.7560 |
| 0.461 | 10.0 | 610 | 0.5708 | 0.7290 | 0.5708 | 0.7555 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
fbaldassarri/EleutherAI_pythia-70m-deduped-autoround-int4-gs128-sym
|
fbaldassarri
| 2024-11-27T20:39:45Z | 7 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel",
"woq",
"intel-autoround",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-70m-deduped",
"base_model:quantized:EleutherAI/pythia-70m-deduped",
"license:apache-2.0",
"4-bit",
"intel/auto-round",
"region:us"
] |
text-generation
| 2024-11-27T20:39:12Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- woq
- intel-autoround
license: apache-2.0
model_name: Pythia 70m deduped
base_model: EleutherAI/pythia-70m-deduped
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Symmetrical Quantization
- Method AutoRound (WOQ)
Fast and low memory, 2-3X speedup (slight accuracy drop at W4G128)
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-70m-deduped has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel AutoRound wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-70m-deduped"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, True
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-70m-deduped-autoround-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_round', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warranty. It has been developed only for research purposes.
|
lmstudio-community/QwQ-32B-Preview-GGUF
|
lmstudio-community
| 2024-11-27T20:38:02Z | 1,072 | 15 | null |
[
"gguf",
"chat",
"text-generation",
"en",
"base_model:Qwen/QwQ-32B-Preview",
"base_model:quantized:Qwen/QwQ-32B-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-27T20:17:31Z |
---
quantized_by: bartowski
pipeline_tag: text-generation
language:
- en
license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE
license: apache-2.0
tags:
- chat
base_model: Qwen/QwQ-32B-Preview
---
## π« Community Model> QwQ 32B Preview by Qwen
*πΎ [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
**Model creator:** [Qwen](https://huggingface.co/Qwen)<br>
**Original model**: [QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b4191](https://github.com/ggerganov/llama.cpp/releases/tag/b4191)<br>
## Technical Details
Supports a context length of 32k
Experimental research model focused on advancing AI reasoning capabilities
Several important limitations:
Language Mixing and Code-Switching: The model may mix languages or switch between them unexpectedly, affecting response clarity.
Recursive Reasoning Loops: The model may enter circular reasoning patterns, leading to lengthy responses without a conclusive answer.
Safety and Ethical Considerations: The model requires enhanced safety measures to ensure reliable and secure performance, and users should exercise caution when deploying it.
Performance and Benchmark Limitations: The model excels in math and coding but has room for improvement in other areas, such as common sense reasoning and nuanced language understanding.
## Special thanks
π 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.
## Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
|
tcapelle/bias-scorer-smollm2-135m
|
tcapelle
| 2024-11-27T20:36:52Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-classification",
"generated_from_trainer",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T13:50:58Z |
---
library_name: transformers
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-135M
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
- precision
- recall
model-index:
- name: bias-scorer-smollm2-135m
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. -->
# bias-scorer-smollm2-135m
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4030
- F1: 0.8236
- Accuracy: 0.8297
- Precision: 0.8205
- Recall: 0.8297
## 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
- 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
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:------:|:--------:|:---------:|:------:|
| No log | 0 | 0 | 0.6116 | 0.7504 | 0.7313 | 0.7927 | 0.7313 |
| 0.4266 | 0.5044 | 10000 | 0.4032 | 0.8235 | 0.8297 | 0.8204 | 0.8297 |
| 0.3763 | 1.0088 | 20000 | 0.4030 | 0.8236 | 0.8297 | 0.8205 | 0.8297 |
| 0.3956 | 1.5132 | 30000 | 0.4030 | 0.8236 | 0.8297 | 0.8205 | 0.8297 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
mlx-community/OLMo-2-1124-13B-Instruct-4bit
|
mlx-community
| 2024-11-27T20:36:32Z | 53 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo2",
"text-generation",
"mlx",
"conversational",
"en",
"dataset:allenai/RLVR-GSM-MATH-IF-Mixed-Constraints",
"base_model:allenai/OLMo-2-1124-13B-Instruct",
"base_model:quantized:allenai/OLMo-2-1124-13B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:33:05Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
base_model: allenai/OLMo-2-1124-13B-Instruct
library_name: transformers
datasets:
- allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
tags:
- mlx
---
# mlx-community/OLMo-2-1124-13B-Instruct-4bit
The Model [mlx-community/OLMo-2-1124-13B-Instruct-4bit](https://huggingface.co/mlx-community/OLMo-2-1124-13B-Instruct-4bit) was
converted to MLX format from [allenai/OLMo-2-1124-13B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct)
using mlx-lm version **0.20.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/OLMo-2-1124-13B-Instruct-4bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF
|
mradermacher
| 2024-11-27T20:31:19Z | 128 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:zelk12/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B",
"base_model:quantized:zelk12/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T19:34:35Z |
---
base_model: zelk12/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/zelk12/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B
<!-- 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/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q3_K_M.gguf) | Q3_K_M | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q3_K_L.gguf) | Q3_K_L | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.IQ4_XS.gguf) | IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q4_0_4_4.gguf) | Q4_0_4_4 | 5.5 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q4_K_S.gguf) | Q4_K_S | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q4_K_M.gguf) | Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q5_K_S.gguf) | Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q5_K_M.gguf) | Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q6_K.gguf) | Q6_K | 7.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.Q8_0.gguf) | Q8_0 | 9.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B-GGUF/resolve/main/MT-Merge2-GP-gemma-2-MT3g2MT1g2-9B.f16.gguf) | f16 | 18.6 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
sbeltz/QwQ-32B-Preview-Q3_K_S-GGUF
|
sbeltz
| 2024-11-27T20:29:47Z | 14 | 3 |
transformers
|
[
"transformers",
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:Qwen/QwQ-32B-Preview",
"base_model:quantized:Qwen/QwQ-32B-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-27T20:24:58Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/QwQ-32B-Preview
tags:
- chat
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# sbeltz/QwQ-32B-Preview-Q3_K_S-GGUF
This model was converted to GGUF format from [`Qwen/QwQ-32B-Preview`](https://huggingface.co/Qwen/QwQ-32B-Preview) 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/Qwen/QwQ-32B-Preview) for more details on the model.
Should fit on a 16GB VRAM card!
## 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 sbeltz/QwQ-32B-Preview-Q3_K_S-GGUF --hf-file qwq-32b-preview-q3_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo sbeltz/QwQ-32B-Preview-Q3_K_S-GGUF --hf-file qwq-32b-preview-q3_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo sbeltz/QwQ-32B-Preview-Q3_K_S-GGUF --hf-file qwq-32b-preview-q3_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo sbeltz/QwQ-32B-Preview-Q3_K_S-GGUF --hf-file qwq-32b-preview-q3_k_s.gguf -c 2048
```
|
mlx-community/OLMo-2-1124-13B-Instruct-6bit
|
mlx-community
| 2024-11-27T20:26:22Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo2",
"text-generation",
"mlx",
"conversational",
"en",
"dataset:allenai/RLVR-GSM-MATH-IF-Mixed-Constraints",
"base_model:allenai/OLMo-2-1124-13B-Instruct",
"base_model:quantized:allenai/OLMo-2-1124-13B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"6-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:21:38Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
base_model: allenai/OLMo-2-1124-13B-Instruct
library_name: transformers
datasets:
- allenai/RLVR-GSM-MATH-IF-Mixed-Constraints
tags:
- mlx
---
# mlx-community/OLMo-2-1124-13B-Instruct-6bit
The Model [mlx-community/OLMo-2-1124-13B-Instruct-6bit](https://huggingface.co/mlx-community/OLMo-2-1124-13B-Instruct-6bit) was
converted to MLX format from [allenai/OLMo-2-1124-13B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-13B-Instruct)
using mlx-lm version **0.20.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/OLMo-2-1124-13B-Instruct-6bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
fbaldassarri/EleutherAI_pythia-31m-autoawq-int4-gs128-sym
|
fbaldassarri
| 2024-11-27T20:26:09Z | 8 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel",
"awq",
"autoawq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-31m",
"base_model:quantized:EleutherAI/pythia-31m",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:25:44Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- awq
- autoawq
- woq
license: apache-2.0
model_name: Pythia 31m
base_model: EleutherAI/pythia-31m
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-31m](EleutherAI/pythia-31m) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Symmetrical Quantization
- Method AutoAWQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-31m has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel Autoround wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-31m"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, True
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-31m-autoawq-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
|
xw17/Phi-3-mini-4k-instruct_finetuned_s04
|
xw17
| 2024-11-27T20:24:45Z | 139 | 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
| 2024-11-27T20:21:42Z |
---
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 π€ 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]
|
fbaldassarri/EleutherAI_pythia-31m-autogptq-int4-gs128-sym
|
fbaldassarri
| 2024-11-27T20:24:28Z | 8 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel",
"gptq",
"autogptq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-31m",
"base_model:quantized:EleutherAI/pythia-31m",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:24:00Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- gptq
- autogptq
- woq
license: apache-2.0
model_name: Pythia 31m
base_model: EleutherAI/pythia-31m
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-31m](EleutherAI/pythia-31m) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Symmetrical Quantization
- Method AutoGPTQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-31m has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel Autoround wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-31m"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, True
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-31m-autogptq-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
|
fbaldassarri/EleutherAI_pythia-31m-autogptq-int4-gs128-asym
|
fbaldassarri
| 2024-11-27T20:23:41Z | 6 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel",
"gptq",
"autogptq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-31m",
"base_model:quantized:EleutherAI/pythia-31m",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:23:11Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- gptq
- autogptq
- woq
license: apache-2.0
model_name: Pythia 31m
base_model: EleutherAI/pythia-31m
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-31m](EleutherAI/pythia-31m) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Asymmetrical Quantization
- Method AutoGPTQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-31m has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel Autoround wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-31m"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, False
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-31m-autogptq-int4-gs128-asym"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
|
wassimm/Llama-3.1-8B-psycology-w-6k_data
|
wassimm
| 2024-11-27T20:17:41Z | 16 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/Meta-Llama-3.1-8B",
"base_model:finetune:unsloth/Meta-Llama-3.1-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-26T10:57:21Z |
---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** wassimm
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B
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)
|
mlx-community/OLMo-2-1124-7B-Instruct-6bit
|
mlx-community
| 2024-11-27T20:16:45Z | 50 | 0 |
transformers
|
[
"transformers",
"safetensors",
"olmo2",
"text-generation",
"mlx",
"conversational",
"en",
"dataset:allenai/RLVR-GSM",
"base_model:allenai/OLMo-2-1124-7B-Instruct",
"base_model:quantized:allenai/OLMo-2-1124-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"6-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:14:12Z |
---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
base_model: allenai/OLMo-2-1124-7B-Instruct
library_name: transformers
datasets:
- allenai/RLVR-GSM
tags:
- mlx
---
# mlx-community/OLMo-2-1124-7B-Instruct-6bit
The Model [mlx-community/OLMo-2-1124-7B-Instruct-6bit](https://huggingface.co/mlx-community/OLMo-2-1124-7B-Instruct-6bit) was
converted to MLX format from [allenai/OLMo-2-1124-7B-Instruct](https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct)
using mlx-lm version **0.20.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/OLMo-2-1124-7B-Instruct-6bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
fbaldassarri/EleutherAI_pythia-14m-autogptq-int4-gs128-sym
|
fbaldassarri
| 2024-11-27T20:15:51Z | 6 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel",
"autogptq",
"gptq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-14m",
"base_model:quantized:EleutherAI/pythia-14m",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:15:26Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- autogptq
- gptq
- woq
license: apache-2.0
model_name: Pythia 14m
base_model: EleutherAI/pythia-14m
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-14m](EleutherAI/pythia-14m) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Symmetrical Quantization
- Method AutoGPTQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-14m has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel Autoround wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-14m"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, True
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-14m-autogptq-int4-gs128-sym"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
|
fbaldassarri/EleutherAI_pythia-14m-autogptq-int4-gs128-asym
|
fbaldassarri
| 2024-11-27T20:15:08Z | 6 | 0 | null |
[
"safetensors",
"gpt_neox",
"pytorch",
"causal-lm",
"pythia",
"autoround",
"intel",
"autogptq",
"gptq",
"woq",
"text-generation",
"en",
"dataset:EleutherAI/pile",
"base_model:EleutherAI/pythia-14m",
"base_model:quantized:EleutherAI/pythia-14m",
"license:apache-2.0",
"4-bit",
"region:us"
] |
text-generation
| 2024-11-27T20:14:36Z |
---
language:
- en
tags:
- pytorch
- causal-lm
- pythia
- autoround
- intel
- autogptq
- gptq
- woq
license: apache-2.0
model_name: Pythia 14m
base_model: EleutherAI/pythia-14m
inference: false
model_creator: EleutherAI
datasets:
- EleutherAI/pile
pipeline_tag: text-generation
prompt_template: '{prompt}
'
quantized_by: fbaldassarri
---
## Model Information
Quantized version of [EleutherAI/pythia-14m](EleutherAI/pythia-14m) using torch.float32 for quantization tuning.
- 4 bits (INT4)
- group size = 128
- Asymmetrical Quantization
- Method AutoGPTQ
Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
Note: this INT4 version of pythia-14m has been quantized to run inference through CPU.
## Replication Recipe
### Step 1 Install Requirements
I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
```
python -m pip install <package> --upgrade
```
- accelerate==1.0.1
- auto_gptq==0.7.1
- neural_compressor==3.1
- torch==2.3.0+cpu
- torchaudio==2.5.0+cpu
- torchvision==0.18.0+cpu
- transformers==4.45.2
### Step 2 Build Intel Autoround wheel from sources
```
python -m pip install git+https://github.com/intel/auto-round.git
```
### Step 3 Script for Quantization
```
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "EleutherAI/pythia-14m"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
from auto_round import AutoRound
bits, group_size, sym = 4, 128, False
autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
autoround.quantize()
output_dir = "./AutoRound/EleutherAI_pythia-14m-autogptq-int4-gs128-asym"
autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
```
## License
[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
## Disclaimer
This quantized model comes with no warrenty. It has been developed only for research purposes.
|
prateek1509/GPT2-model
|
prateek1509
| 2024-11-27T20:09:13Z | 142 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T20:08:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
ICT3214-Group5/Cryptography_GPT_NEO_v1.0.0
|
ICT3214-Group5
| 2024-11-27T20:07:48Z | 111 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-125m",
"base_model:finetune:EleutherAI/gpt-neo-125m",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-22T16:07:19Z |
---
library_name: transformers
license: mit
base_model: EleutherAI/gpt-neo-125M
tags:
- generated_from_trainer
model-index:
- name: Cryptography_GPT_NEO_v1.0.0
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. -->
# Cryptography_GPT_NEO_v1.0.0
This model is a fine-tuned version of [EleutherAI/gpt-neo-125M](https://huggingface.co/EleutherAI/gpt-neo-125M) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.3
|
TalTechNLP/voxlingua107-epaca-tdnn
|
TalTechNLP
| 2024-11-27T20:06:00Z | 60,071 | 28 |
speechbrain
|
[
"speechbrain",
"audio-classification",
"embeddings",
"Language",
"Identification",
"pytorch",
"ECAPA-TDNN",
"TDNN",
"VoxLingua107",
"multilingual",
"dataset:VoxLingua107",
"license:apache-2.0",
"region:us"
] |
audio-classification
| 2022-03-02T23:29:05Z |
---
language: multilingual
thumbnail:
tags:
- audio-classification
- speechbrain
- embeddings
- Language
- Identification
- pytorch
- ECAPA-TDNN
- TDNN
- VoxLingua107
license: "apache-2.0"
datasets:
- VoxLingua107
metrics:
- Accuracy
widget:
- example_title: English Sample
src: https://cdn-media.huggingface.co/speech_samples/LibriSpeech_61-70968-0000.flac
---
# VoxLingua107 ECAPA-TDNN Spoken Language Identification Model
## Model description
This is a spoken language recognition model trained on the [VoxLingua107 dataset](https://cs.taltech.ee/staff/tanel.alumae/data/voxlingua107/) using SpeechBrain.
The model uses the ECAPA-TDNN architecture that has previously been used for speaker recognition.
The model can classify a speech utterance according to the language spoken.
It covers 107 different languages (
Abkhazian,
Afrikaans,
Amharic,
Arabic,
Assamese,
Azerbaijani,
Bashkir,
Belarusian,
Bulgarian,
Bengali,
Tibetan,
Breton,
Bosnian,
Catalan,
Cebuano,
Czech,
Welsh,
Danish,
German,
Greek,
English,
Esperanto,
Spanish,
Estonian,
Basque,
Persian,
Finnish,
Faroese,
French,
Galician,
Guarani,
Gujarati,
Manx,
Hausa,
Hawaiian,
Hindi,
Croatian,
Haitian,
Hungarian,
Armenian,
Interlingua,
Indonesian,
Icelandic,
Italian,
Hebrew,
Japanese,
Javanese,
Georgian,
Kazakh,
Central Khmer,
Kannada,
Korean,
Latin,
Luxembourgish,
Lingala,
Lao,
Lithuanian,
Latvian,
Malagasy,
Maori,
Macedonian,
Malayalam,
Mongolian,
Marathi,
Malay,
Maltese,
Burmese,
Nepali,
Dutch,
Norwegian Nynorsk,
Norwegian,
Occitan,
Panjabi,
Polish,
Pushto,
Portuguese,
Romanian,
Russian,
Sanskrit,
Scots,
Sindhi,
Sinhala,
Slovak,
Slovenian,
Shona,
Somali,
Albanian,
Serbian,
Sundanese,
Swedish,
Swahili,
Tamil,
Telugu,
Tajik,
Thai,
Turkmen,
Tagalog,
Turkish,
Tatar,
Ukrainian,
Urdu,
Uzbek,
Vietnamese,
Waray,
Yiddish,
Yoruba,
Mandarin Chinese).
## Intended uses & limitations
The model has two uses:
- use 'as is' for spoken language recognition
- use as an utterance-level feature (embedding) extractor, for creating a dedicated language ID model on your own data
The model is trained on automatically collected YouTube data. For more
information about the dataset, see [here](https://cs.taltech.ee/staff/tanel.alumae/data/voxlingua107/).
#### How to use
```python
import torchaudio
from speechbrain.pretrained import EncoderClassifier
language_id = EncoderClassifier.from_hparams(source="TalTechNLP/voxlingua107-epaca-tdnn", savedir="tmp")
# Download Thai language sample from Omniglot and cvert to suitable form
signal = language_id.load_audio("https://omniglot.com/soundfiles/udhr/udhr_th.mp3")
prediction = language_id.classify_batch(signal)
print(prediction)
(tensor([[0.3210, 0.3751, 0.3680, 0.3939, 0.4026, 0.3644, 0.3689, 0.3597, 0.3508,
0.3666, 0.3895, 0.3978, 0.3848, 0.3957, 0.3949, 0.3586, 0.4360, 0.3997,
0.4106, 0.3886, 0.4177, 0.3870, 0.3764, 0.3763, 0.3672, 0.4000, 0.4256,
0.4091, 0.3563, 0.3695, 0.3320, 0.3838, 0.3850, 0.3867, 0.3878, 0.3944,
0.3924, 0.4063, 0.3803, 0.3830, 0.2996, 0.4187, 0.3976, 0.3651, 0.3950,
0.3744, 0.4295, 0.3807, 0.3613, 0.4710, 0.3530, 0.4156, 0.3651, 0.3777,
0.3813, 0.6063, 0.3708, 0.3886, 0.3766, 0.4023, 0.3785, 0.3612, 0.4193,
0.3720, 0.4406, 0.3243, 0.3866, 0.3866, 0.4104, 0.4294, 0.4175, 0.3364,
0.3595, 0.3443, 0.3565, 0.3776, 0.3985, 0.3778, 0.2382, 0.4115, 0.4017,
0.4070, 0.3266, 0.3648, 0.3888, 0.3907, 0.3755, 0.3631, 0.4460, 0.3464,
0.3898, 0.3661, 0.3883, 0.3772, 0.9289, 0.3687, 0.4298, 0.4211, 0.3838,
0.3521, 0.3515, 0.3465, 0.4772, 0.4043, 0.3844, 0.3973, 0.4343]]), tensor([0.9289]), tensor([94]), ['th'])
# The scores in the prediction[0] tensor can be interpreted as cosine scores between
# the languages and the given utterance (i.e., the larger the better)
# The identified language ISO code is given in prediction[3]
print(prediction[3])
['th']
# Alternatively, use the utterance embedding extractor:
emb = language_id.encode_batch(signal)
print(emb.shape)
torch.Size([1, 1, 256])
```
#### Limitations and bias
Since the model is trained on VoxLingua107, it has many limitations and biases, some of which are:
- Probably it's accuracy on smaller languages is quite limited
- Probably it works worse on female speech than male speech (because YouTube data includes much more male speech)
- Based on subjective experiments, it doesn't work well on speech with a foreign accent
- Probably it doesn't work well on children's speech and on persons with speech disorders
## Training data
The model is trained on [VoxLingua107](https://cs.taltech.ee/staff/tanel.alumae/data/voxlingua107/).
VoxLingua107 is a speech dataset for training spoken language identification models.
The dataset consists of short speech segments automatically extracted from YouTube videos and labeled according the language of the video title and description, with some post-processing steps to filter out false positives.
VoxLingua107 contains data for 107 languages. The total amount of speech in the training set is 6628 hours.
The average amount of data per language is 62 hours. However, the real amount per language varies a lot. There is also a seperate development set containing 1609 speech segments from 33 languages, validated by at least two volunteers to really contain the given language.
## Training procedure
We used [SpeechBrain](https://github.com/speechbrain/speechbrain) to train the model.
Training recipe will be published soon.
## Evaluation results
Error rate: 7% on the development dataset
### BibTeX entry and citation info
```bibtex
@inproceedings{valk2021slt,
title={{VoxLingua107}: a Dataset for Spoken Language Recognition},
author={J{\"o}rgen Valk and Tanel Alum{\"a}e},
booktitle={Proc. IEEE SLT Workshop},
year={2021},
}
```
|
MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k2_task1_organization_fold1
|
MayBashendy
| 2024-11-27T20:05:19Z | 165 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T20:02:52Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_run2_AugV5_k2_task1_organization_fold1
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. -->
# Arabic_FineTuningAraBERT_run2_AugV5_k2_task1_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6184
- Qwk: 0.6769
- Mse: 0.6184
- Rmse: 0.7864
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 0.0645 | 2 | 3.0954 | 0.0397 | 3.0954 | 1.7594 |
| No log | 0.1290 | 4 | 2.2926 | 0.125 | 2.2926 | 1.5141 |
| No log | 0.1935 | 6 | 2.0117 | 0.1026 | 2.0117 | 1.4184 |
| No log | 0.2581 | 8 | 1.6250 | 0.0 | 1.6250 | 1.2748 |
| No log | 0.3226 | 10 | 1.3080 | 0.0 | 1.3080 | 1.1437 |
| No log | 0.3871 | 12 | 1.2601 | 0.0 | 1.2601 | 1.1225 |
| No log | 0.4516 | 14 | 1.2575 | 0.0 | 1.2575 | 1.1214 |
| No log | 0.5161 | 16 | 1.1534 | 0.0 | 1.1534 | 1.0740 |
| No log | 0.5806 | 18 | 1.0539 | 0.0 | 1.0539 | 1.0266 |
| No log | 0.6452 | 20 | 1.0060 | 0.0841 | 1.0060 | 1.0030 |
| No log | 0.7097 | 22 | 1.0244 | 0.0 | 1.0244 | 1.0121 |
| No log | 0.7742 | 24 | 1.1019 | 0.0 | 1.1019 | 1.0497 |
| No log | 0.8387 | 26 | 1.1311 | 0.0 | 1.1311 | 1.0635 |
| No log | 0.9032 | 28 | 1.1078 | 0.0 | 1.1078 | 1.0525 |
| No log | 0.9677 | 30 | 1.1050 | 0.0 | 1.1050 | 1.0512 |
| No log | 1.0323 | 32 | 1.0923 | 0.0 | 1.0923 | 1.0452 |
| No log | 1.0968 | 34 | 1.1006 | 0.0841 | 1.1006 | 1.0491 |
| No log | 1.1613 | 36 | 1.1557 | 0.0 | 1.1557 | 1.0750 |
| No log | 1.2258 | 38 | 1.1416 | 0.0 | 1.1416 | 1.0685 |
| No log | 1.2903 | 40 | 1.0933 | 0.0 | 1.0933 | 1.0456 |
| No log | 1.3548 | 42 | 1.0280 | 0.0 | 1.0280 | 1.0139 |
| No log | 1.4194 | 44 | 1.0050 | 0.0 | 1.0050 | 1.0025 |
| No log | 1.4839 | 46 | 1.0047 | 0.0 | 1.0047 | 1.0024 |
| No log | 1.5484 | 48 | 0.9900 | 0.1706 | 0.9900 | 0.9950 |
| No log | 1.6129 | 50 | 0.9606 | 0.2829 | 0.9606 | 0.9801 |
| No log | 1.6774 | 52 | 0.9424 | 0.2613 | 0.9424 | 0.9708 |
| No log | 1.7419 | 54 | 0.9491 | 0.2613 | 0.9491 | 0.9742 |
| No log | 1.8065 | 56 | 0.9417 | 0.3571 | 0.9417 | 0.9704 |
| No log | 1.8710 | 58 | 0.9257 | 0.3834 | 0.9257 | 0.9621 |
| No log | 1.9355 | 60 | 0.9068 | 0.3834 | 0.9068 | 0.9523 |
| No log | 2.0 | 62 | 0.8609 | 0.4842 | 0.8609 | 0.9279 |
| No log | 2.0645 | 64 | 0.7932 | 0.4199 | 0.7932 | 0.8906 |
| No log | 2.1290 | 66 | 0.7497 | 0.4199 | 0.7497 | 0.8658 |
| No log | 2.1935 | 68 | 0.7228 | 0.3708 | 0.7228 | 0.8502 |
| No log | 2.2581 | 70 | 0.7059 | 0.4 | 0.7059 | 0.8402 |
| No log | 2.3226 | 72 | 0.7068 | 0.3488 | 0.7068 | 0.8407 |
| No log | 2.3871 | 74 | 0.7337 | 0.3488 | 0.7337 | 0.8566 |
| No log | 2.4516 | 76 | 0.7535 | 0.2959 | 0.7535 | 0.8681 |
| No log | 2.5161 | 78 | 0.7536 | 0.2959 | 0.7536 | 0.8681 |
| No log | 2.5806 | 80 | 0.7318 | 0.2959 | 0.7318 | 0.8554 |
| No log | 2.6452 | 82 | 0.6737 | 0.3488 | 0.6737 | 0.8208 |
| No log | 2.7097 | 84 | 0.6550 | 0.3708 | 0.6550 | 0.8093 |
| No log | 2.7742 | 86 | 0.6505 | 0.3708 | 0.6505 | 0.8066 |
| No log | 2.8387 | 88 | 0.6498 | 0.3708 | 0.6498 | 0.8061 |
| No log | 2.9032 | 90 | 0.6325 | 0.4199 | 0.6325 | 0.7953 |
| No log | 2.9677 | 92 | 0.6273 | 0.5882 | 0.6273 | 0.7920 |
| No log | 3.0323 | 94 | 0.6184 | 0.5882 | 0.6184 | 0.7864 |
| No log | 3.0968 | 96 | 0.5901 | 0.5882 | 0.5901 | 0.7682 |
| No log | 3.1613 | 98 | 0.5801 | 0.5882 | 0.5801 | 0.7617 |
| No log | 3.2258 | 100 | 0.5668 | 0.5767 | 0.5668 | 0.7528 |
| No log | 3.2903 | 102 | 0.5807 | 0.4717 | 0.5807 | 0.7620 |
| No log | 3.3548 | 104 | 0.6117 | 0.4563 | 0.6117 | 0.7821 |
| No log | 3.4194 | 106 | 0.6239 | 0.4563 | 0.6239 | 0.7899 |
| No log | 3.4839 | 108 | 0.6653 | 0.3978 | 0.6653 | 0.8157 |
| No log | 3.5484 | 110 | 0.6298 | 0.4138 | 0.6298 | 0.7936 |
| No log | 3.6129 | 112 | 0.5572 | 0.5161 | 0.5572 | 0.7465 |
| No log | 3.6774 | 114 | 0.5320 | 0.6016 | 0.5320 | 0.7294 |
| No log | 3.7419 | 116 | 0.5707 | 0.5370 | 0.5707 | 0.7555 |
| No log | 3.8065 | 118 | 0.5859 | 0.5370 | 0.5859 | 0.7654 |
| No log | 3.8710 | 120 | 0.5648 | 0.6486 | 0.5648 | 0.7515 |
| No log | 3.9355 | 122 | 0.6348 | 0.5882 | 0.6348 | 0.7968 |
| No log | 4.0 | 124 | 0.7331 | 0.6471 | 0.7331 | 0.8562 |
| No log | 4.0645 | 126 | 0.7342 | 0.6111 | 0.7342 | 0.8568 |
| No log | 4.1290 | 128 | 0.6880 | 0.6224 | 0.6880 | 0.8294 |
| No log | 4.1935 | 130 | 0.6281 | 0.5116 | 0.6281 | 0.7925 |
| No log | 4.2581 | 132 | 0.6224 | 0.6263 | 0.6224 | 0.7889 |
| No log | 4.3226 | 134 | 0.6356 | 0.6263 | 0.6356 | 0.7972 |
| No log | 4.3871 | 136 | 0.6229 | 0.6164 | 0.6229 | 0.7892 |
| No log | 4.4516 | 138 | 0.6088 | 0.6267 | 0.6088 | 0.7803 |
| No log | 4.5161 | 140 | 0.6040 | 0.6617 | 0.6040 | 0.7772 |
| No log | 4.5806 | 142 | 0.6091 | 0.6617 | 0.6091 | 0.7805 |
| No log | 4.6452 | 144 | 0.5863 | 0.6617 | 0.5863 | 0.7657 |
| No log | 4.7097 | 146 | 0.5682 | 0.6624 | 0.5682 | 0.7538 |
| No log | 4.7742 | 148 | 0.5586 | 0.6624 | 0.5586 | 0.7474 |
| No log | 4.8387 | 150 | 0.5598 | 0.7129 | 0.5598 | 0.7482 |
| No log | 4.9032 | 152 | 0.5728 | 0.6797 | 0.5728 | 0.7569 |
| No log | 4.9677 | 154 | 0.5720 | 0.6441 | 0.5720 | 0.7563 |
| No log | 5.0323 | 156 | 0.5731 | 0.6535 | 0.5731 | 0.7570 |
| No log | 5.0968 | 158 | 0.5978 | 0.6624 | 0.5978 | 0.7732 |
| No log | 5.1613 | 160 | 0.6389 | 0.6667 | 0.6389 | 0.7993 |
| No log | 5.2258 | 162 | 0.6835 | 0.5828 | 0.6835 | 0.8267 |
| No log | 5.2903 | 164 | 0.7011 | 0.5828 | 0.7011 | 0.8373 |
| No log | 5.3548 | 166 | 0.6771 | 0.5911 | 0.6771 | 0.8229 |
| No log | 5.4194 | 168 | 0.6446 | 0.5882 | 0.6446 | 0.8029 |
| No log | 5.4839 | 170 | 0.6524 | 0.5765 | 0.6524 | 0.8077 |
| No log | 5.5484 | 172 | 0.6605 | 0.6164 | 0.6605 | 0.8127 |
| No log | 5.6129 | 174 | 0.6627 | 0.6164 | 0.6627 | 0.8141 |
| No log | 5.6774 | 176 | 0.6731 | 0.6267 | 0.6731 | 0.8205 |
| No log | 5.7419 | 178 | 0.6727 | 0.6557 | 0.6727 | 0.8202 |
| No log | 5.8065 | 180 | 0.6510 | 0.6557 | 0.6510 | 0.8068 |
| No log | 5.8710 | 182 | 0.6214 | 0.6522 | 0.6214 | 0.7883 |
| No log | 5.9355 | 184 | 0.5911 | 0.6522 | 0.5911 | 0.7688 |
| No log | 6.0 | 186 | 0.5762 | 0.6522 | 0.5762 | 0.7591 |
| No log | 6.0645 | 188 | 0.5651 | 0.6879 | 0.5651 | 0.7518 |
| No log | 6.1290 | 190 | 0.5274 | 0.6733 | 0.5274 | 0.7262 |
| No log | 6.1935 | 192 | 0.4827 | 0.7336 | 0.4827 | 0.6948 |
| No log | 6.2581 | 194 | 0.4672 | 0.7034 | 0.4672 | 0.6835 |
| No log | 6.3226 | 196 | 0.4796 | 0.72 | 0.4796 | 0.6925 |
| No log | 6.3871 | 198 | 0.4497 | 0.72 | 0.4497 | 0.6706 |
| No log | 6.4516 | 200 | 0.4274 | 0.7266 | 0.4274 | 0.6537 |
| No log | 6.5161 | 202 | 0.4796 | 0.6624 | 0.4796 | 0.6925 |
| No log | 6.5806 | 204 | 0.5517 | 0.7129 | 0.5517 | 0.7428 |
| No log | 6.6452 | 206 | 0.5682 | 0.7129 | 0.5682 | 0.7538 |
| No log | 6.7097 | 208 | 0.5669 | 0.6624 | 0.5669 | 0.7529 |
| No log | 6.7742 | 210 | 0.5765 | 0.6522 | 0.5765 | 0.7592 |
| No log | 6.8387 | 212 | 0.6114 | 0.6708 | 0.6114 | 0.7819 |
| No log | 6.9032 | 214 | 0.6342 | 0.6708 | 0.6342 | 0.7964 |
| No log | 6.9677 | 216 | 0.6472 | 0.6708 | 0.6472 | 0.8045 |
| No log | 7.0323 | 218 | 0.6363 | 0.6522 | 0.6363 | 0.7977 |
| No log | 7.0968 | 220 | 0.6102 | 0.6522 | 0.6102 | 0.7811 |
| No log | 7.1613 | 222 | 0.5915 | 0.6522 | 0.5915 | 0.7691 |
| No log | 7.2258 | 224 | 0.5831 | 0.6879 | 0.5831 | 0.7636 |
| No log | 7.2903 | 226 | 0.5771 | 0.6879 | 0.5771 | 0.7597 |
| No log | 7.3548 | 228 | 0.5819 | 0.6522 | 0.5819 | 0.7629 |
| No log | 7.4194 | 230 | 0.5975 | 0.6522 | 0.5975 | 0.7730 |
| No log | 7.4839 | 232 | 0.6304 | 0.6606 | 0.6304 | 0.7940 |
| No log | 7.5484 | 234 | 0.6712 | 0.6789 | 0.6712 | 0.8193 |
| No log | 7.6129 | 236 | 0.6837 | 0.6789 | 0.6837 | 0.8268 |
| No log | 7.6774 | 238 | 0.6580 | 0.6789 | 0.6580 | 0.8112 |
| No log | 7.7419 | 240 | 0.6248 | 0.6426 | 0.6248 | 0.7904 |
| No log | 7.8065 | 242 | 0.6025 | 0.6522 | 0.6025 | 0.7762 |
| No log | 7.8710 | 244 | 0.5963 | 0.6769 | 0.5963 | 0.7722 |
| No log | 7.9355 | 246 | 0.5962 | 0.6769 | 0.5962 | 0.7722 |
| No log | 8.0 | 248 | 0.5963 | 0.6769 | 0.5963 | 0.7722 |
| No log | 8.0645 | 250 | 0.5963 | 0.6769 | 0.5963 | 0.7722 |
| No log | 8.1290 | 252 | 0.6029 | 0.6769 | 0.6029 | 0.7764 |
| No log | 8.1935 | 254 | 0.6090 | 0.6667 | 0.6090 | 0.7804 |
| No log | 8.2581 | 256 | 0.6111 | 0.6667 | 0.6111 | 0.7817 |
| No log | 8.3226 | 258 | 0.6049 | 0.6667 | 0.6049 | 0.7778 |
| No log | 8.3871 | 260 | 0.6020 | 0.6667 | 0.6020 | 0.7759 |
| No log | 8.4516 | 262 | 0.6038 | 0.6667 | 0.6038 | 0.7770 |
| No log | 8.5161 | 264 | 0.6099 | 0.6667 | 0.6099 | 0.7809 |
| No log | 8.5806 | 266 | 0.6157 | 0.6667 | 0.6157 | 0.7847 |
| No log | 8.6452 | 268 | 0.6227 | 0.6667 | 0.6227 | 0.7891 |
| No log | 8.7097 | 270 | 0.6279 | 0.6667 | 0.6279 | 0.7924 |
| No log | 8.7742 | 272 | 0.6303 | 0.6667 | 0.6303 | 0.7939 |
| No log | 8.8387 | 274 | 0.6326 | 0.6667 | 0.6326 | 0.7954 |
| No log | 8.9032 | 276 | 0.6350 | 0.6667 | 0.6350 | 0.7969 |
| No log | 8.9677 | 278 | 0.6351 | 0.6667 | 0.6351 | 0.7969 |
| No log | 9.0323 | 280 | 0.6341 | 0.6667 | 0.6341 | 0.7963 |
| No log | 9.0968 | 282 | 0.6323 | 0.6667 | 0.6323 | 0.7952 |
| No log | 9.1613 | 284 | 0.6288 | 0.6667 | 0.6288 | 0.7930 |
| No log | 9.2258 | 286 | 0.6257 | 0.6667 | 0.6257 | 0.7910 |
| No log | 9.2903 | 288 | 0.6253 | 0.6426 | 0.6253 | 0.7908 |
| No log | 9.3548 | 290 | 0.6252 | 0.6426 | 0.6252 | 0.7907 |
| No log | 9.4194 | 292 | 0.6246 | 0.6426 | 0.6246 | 0.7903 |
| No log | 9.4839 | 294 | 0.6228 | 0.6426 | 0.6228 | 0.7892 |
| No log | 9.5484 | 296 | 0.6209 | 0.6667 | 0.6209 | 0.7880 |
| No log | 9.6129 | 298 | 0.6199 | 0.6667 | 0.6199 | 0.7873 |
| No log | 9.6774 | 300 | 0.6194 | 0.6667 | 0.6194 | 0.7870 |
| No log | 9.7419 | 302 | 0.6185 | 0.6769 | 0.6185 | 0.7865 |
| No log | 9.8065 | 304 | 0.6182 | 0.6769 | 0.6182 | 0.7863 |
| No log | 9.8710 | 306 | 0.6182 | 0.6769 | 0.6182 | 0.7862 |
| No log | 9.9355 | 308 | 0.6182 | 0.6769 | 0.6182 | 0.7862 |
| No log | 10.0 | 310 | 0.6184 | 0.6769 | 0.6184 | 0.7864 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
borisf/BBXL
|
borisf
| 2024-11-27T20:04:54Z | 26 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2024-11-27T20:00:03Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: BBXL
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
---
# BBXL
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `BBXL` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
prateek1509/my-t5-model
|
prateek1509
| 2024-11-27T20:03:28Z | 141 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T11:42:28Z |
---
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 π€ 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]
|
orabazes/QwQ-32B-Preview-GGUF
|
orabazes
| 2024-11-27T19:58:08Z | 5 | 2 |
transformers
|
[
"transformers",
"gguf",
"chat",
"llama-cpp",
"text-generation",
"en",
"base_model:Qwen/QwQ-32B-Preview",
"base_model:quantized:Qwen/QwQ-32B-Preview",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-27T19:50:43Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/QwQ-32B-Preview/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: Qwen/QwQ-32B-Preview
tags:
- chat
- llama-cpp
- gguf
library_name: transformers
---
[See original repo for details](https://huggingface.co/Qwen/QwQ-32B-Preview)
|
MayBashendy/Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_organization_fold0
|
MayBashendy
| 2024-11-27T19:57:20Z | 164 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T19:55:17Z |
---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_organization_fold0
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. -->
# Arabic_FineTuningAraBERT_run2_AugV5_k1_task1_organization_fold0
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7644
- Qwk: 0.7173
- Mse: 0.7644
- Rmse: 0.8743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.0952 | 2 | 4.1476 | 0.0 | 4.1476 | 2.0366 |
| No log | 0.1905 | 4 | 2.8830 | -0.2427 | 2.8830 | 1.6979 |
| No log | 0.2857 | 6 | 1.5288 | 0.0976 | 1.5288 | 1.2365 |
| No log | 0.3810 | 8 | 1.4860 | 0.0976 | 1.4860 | 1.2190 |
| No log | 0.4762 | 10 | 1.5236 | 0.1515 | 1.5236 | 1.2343 |
| No log | 0.5714 | 12 | 1.2642 | 0.4878 | 1.2642 | 1.1243 |
| No log | 0.6667 | 14 | 1.1660 | 0.3337 | 1.1660 | 1.0798 |
| No log | 0.7619 | 16 | 1.2612 | 0.3050 | 1.2612 | 1.1230 |
| No log | 0.8571 | 18 | 1.2552 | 0.4057 | 1.2552 | 1.1204 |
| No log | 0.9524 | 20 | 1.2393 | 0.4057 | 1.2393 | 1.1132 |
| No log | 1.0476 | 22 | 1.1814 | 0.4220 | 1.1814 | 1.0869 |
| No log | 1.1429 | 24 | 1.2843 | 0.5633 | 1.2843 | 1.1333 |
| No log | 1.2381 | 26 | 1.3256 | 0.5972 | 1.3256 | 1.1513 |
| No log | 1.3333 | 28 | 1.1869 | 0.5290 | 1.1869 | 1.0895 |
| No log | 1.4286 | 30 | 1.0546 | 0.4134 | 1.0546 | 1.0270 |
| No log | 1.5238 | 32 | 1.1529 | 0.2957 | 1.1529 | 1.0737 |
| No log | 1.6190 | 34 | 1.1855 | 0.2957 | 1.1855 | 1.0888 |
| No log | 1.7143 | 36 | 1.1106 | 0.2957 | 1.1106 | 1.0539 |
| No log | 1.8095 | 38 | 0.9637 | 0.4324 | 0.9637 | 0.9817 |
| No log | 1.9048 | 40 | 0.8819 | 0.4324 | 0.8819 | 0.9391 |
| No log | 2.0 | 42 | 0.8292 | 0.4830 | 0.8292 | 0.9106 |
| No log | 2.0952 | 44 | 0.8042 | 0.5073 | 0.8042 | 0.8968 |
| No log | 2.1905 | 46 | 0.8069 | 0.6280 | 0.8069 | 0.8983 |
| No log | 2.2857 | 48 | 0.8516 | 0.6860 | 0.8516 | 0.9228 |
| No log | 2.3810 | 50 | 0.9242 | 0.5209 | 0.9242 | 0.9614 |
| No log | 2.4762 | 52 | 0.9070 | 0.5589 | 0.9070 | 0.9524 |
| No log | 2.5714 | 54 | 0.9580 | 0.5617 | 0.9580 | 0.9788 |
| No log | 2.6667 | 56 | 0.9099 | 0.5422 | 0.9099 | 0.9539 |
| No log | 2.7619 | 58 | 0.8511 | 0.7086 | 0.8511 | 0.9225 |
| No log | 2.8571 | 60 | 0.8713 | 0.6799 | 0.8713 | 0.9334 |
| No log | 2.9524 | 62 | 0.9175 | 0.6648 | 0.9175 | 0.9579 |
| No log | 3.0476 | 64 | 0.8792 | 0.6996 | 0.8792 | 0.9377 |
| No log | 3.1429 | 66 | 0.8568 | 0.6856 | 0.8568 | 0.9257 |
| No log | 3.2381 | 68 | 0.8561 | 0.7157 | 0.8561 | 0.9252 |
| No log | 3.3333 | 70 | 0.8715 | 0.6260 | 0.8715 | 0.9336 |
| No log | 3.4286 | 72 | 0.9027 | 0.6435 | 0.9027 | 0.9501 |
| No log | 3.5238 | 74 | 0.8920 | 0.6435 | 0.8920 | 0.9444 |
| No log | 3.6190 | 76 | 0.8308 | 0.6860 | 0.8308 | 0.9115 |
| No log | 3.7143 | 78 | 0.8151 | 0.7688 | 0.8151 | 0.9029 |
| No log | 3.8095 | 80 | 0.8414 | 0.6860 | 0.8414 | 0.9173 |
| No log | 3.9048 | 82 | 0.8683 | 0.6435 | 0.8683 | 0.9318 |
| No log | 4.0 | 84 | 0.8942 | 0.6382 | 0.8942 | 0.9456 |
| No log | 4.0952 | 86 | 0.8442 | 0.6917 | 0.8442 | 0.9188 |
| No log | 4.1905 | 88 | 0.8374 | 0.6695 | 0.8374 | 0.9151 |
| No log | 4.2857 | 90 | 0.8685 | 0.6837 | 0.8685 | 0.9320 |
| No log | 4.3810 | 92 | 0.8959 | 0.6837 | 0.8959 | 0.9465 |
| No log | 4.4762 | 94 | 0.8838 | 0.6604 | 0.8838 | 0.9401 |
| No log | 4.5714 | 96 | 0.8947 | 0.6644 | 0.8947 | 0.9459 |
| No log | 4.6667 | 98 | 0.9315 | 0.6677 | 0.9315 | 0.9651 |
| No log | 4.7619 | 100 | 0.9649 | 0.6176 | 0.9649 | 0.9823 |
| No log | 4.8571 | 102 | 1.0185 | 0.5862 | 1.0185 | 1.0092 |
| No log | 4.9524 | 104 | 1.0308 | 0.5532 | 1.0308 | 1.0153 |
| No log | 5.0476 | 106 | 0.9284 | 0.6071 | 0.9284 | 0.9636 |
| No log | 5.1429 | 108 | 0.8662 | 0.6673 | 0.8662 | 0.9307 |
| No log | 5.2381 | 110 | 0.8140 | 0.6797 | 0.8140 | 0.9022 |
| No log | 5.3333 | 112 | 0.8387 | 0.6673 | 0.8387 | 0.9158 |
| No log | 5.4286 | 114 | 0.9334 | 0.6071 | 0.9334 | 0.9661 |
| No log | 5.5238 | 116 | 1.0024 | 0.6071 | 1.0024 | 1.0012 |
| No log | 5.6190 | 118 | 1.0101 | 0.6071 | 1.0101 | 1.0051 |
| No log | 5.7143 | 120 | 0.9022 | 0.6610 | 0.9022 | 0.9498 |
| No log | 5.8095 | 122 | 0.7467 | 0.6684 | 0.7467 | 0.8641 |
| No log | 5.9048 | 124 | 0.7094 | 0.6856 | 0.7094 | 0.8423 |
| No log | 6.0 | 126 | 0.6898 | 0.7119 | 0.6898 | 0.8305 |
| No log | 6.0952 | 128 | 0.6781 | 0.7522 | 0.6781 | 0.8235 |
| No log | 6.1905 | 130 | 0.6675 | 0.6856 | 0.6675 | 0.8170 |
| No log | 6.2857 | 132 | 0.7246 | 0.6266 | 0.7246 | 0.8512 |
| No log | 6.3810 | 134 | 0.8606 | 0.6610 | 0.8606 | 0.9277 |
| No log | 6.4762 | 136 | 0.9918 | 0.6071 | 0.9918 | 0.9959 |
| No log | 6.5714 | 138 | 1.0222 | 0.6071 | 1.0222 | 1.0111 |
| No log | 6.6667 | 140 | 0.9476 | 0.6071 | 0.9476 | 0.9734 |
| No log | 6.7619 | 142 | 0.8654 | 0.6071 | 0.8654 | 0.9303 |
| No log | 6.8571 | 144 | 0.7527 | 0.6972 | 0.7527 | 0.8676 |
| No log | 6.9524 | 146 | 0.7121 | 0.6797 | 0.7121 | 0.8439 |
| No log | 7.0476 | 148 | 0.7029 | 0.6738 | 0.7029 | 0.8384 |
| No log | 7.1429 | 150 | 0.7461 | 0.6972 | 0.7461 | 0.8638 |
| No log | 7.2381 | 152 | 0.7794 | 0.6902 | 0.7794 | 0.8828 |
| No log | 7.3333 | 154 | 0.8216 | 0.6902 | 0.8216 | 0.9064 |
| No log | 7.4286 | 156 | 0.8645 | 0.6902 | 0.8645 | 0.9298 |
| No log | 7.5238 | 158 | 0.8683 | 0.6902 | 0.8683 | 0.9318 |
| No log | 7.6190 | 160 | 0.8835 | 0.6902 | 0.8835 | 0.9399 |
| No log | 7.7143 | 162 | 0.9206 | 0.6902 | 0.9206 | 0.9595 |
| No log | 7.8095 | 164 | 0.8866 | 0.6902 | 0.8866 | 0.9416 |
| No log | 7.9048 | 166 | 0.8692 | 0.6902 | 0.8692 | 0.9323 |
| No log | 8.0 | 168 | 0.8318 | 0.6902 | 0.8318 | 0.9120 |
| No log | 8.0952 | 170 | 0.8187 | 0.6902 | 0.8187 | 0.9048 |
| No log | 8.1905 | 172 | 0.7813 | 0.6732 | 0.7813 | 0.8839 |
| No log | 8.2857 | 174 | 0.7491 | 0.6732 | 0.7491 | 0.8655 |
| No log | 8.3810 | 176 | 0.7350 | 0.7009 | 0.7350 | 0.8573 |
| No log | 8.4762 | 178 | 0.7080 | 0.7009 | 0.7080 | 0.8414 |
| No log | 8.5714 | 180 | 0.7048 | 0.7009 | 0.7048 | 0.8395 |
| No log | 8.6667 | 182 | 0.7225 | 0.7009 | 0.7225 | 0.8500 |
| No log | 8.7619 | 184 | 0.7453 | 0.7173 | 0.7453 | 0.8633 |
| No log | 8.8571 | 186 | 0.7759 | 0.7173 | 0.7759 | 0.8808 |
| No log | 8.9524 | 188 | 0.7920 | 0.7173 | 0.7920 | 0.8900 |
| No log | 9.0476 | 190 | 0.7876 | 0.7173 | 0.7876 | 0.8875 |
| No log | 9.1429 | 192 | 0.7791 | 0.7173 | 0.7791 | 0.8827 |
| No log | 9.2381 | 194 | 0.7682 | 0.7173 | 0.7682 | 0.8765 |
| No log | 9.3333 | 196 | 0.7664 | 0.7173 | 0.7664 | 0.8755 |
| No log | 9.4286 | 198 | 0.7734 | 0.7173 | 0.7734 | 0.8794 |
| No log | 9.5238 | 200 | 0.7719 | 0.7173 | 0.7719 | 0.8786 |
| No log | 9.6190 | 202 | 0.7721 | 0.7173 | 0.7721 | 0.8787 |
| No log | 9.7143 | 204 | 0.7656 | 0.7173 | 0.7656 | 0.8750 |
| No log | 9.8095 | 206 | 0.7637 | 0.7173 | 0.7637 | 0.8739 |
| No log | 9.9048 | 208 | 0.7652 | 0.7173 | 0.7652 | 0.8747 |
| No log | 10.0 | 210 | 0.7644 | 0.7173 | 0.7644 | 0.8743 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
pierrelouisdelx/cygon_10k
|
pierrelouisdelx
| 2024-11-27T19:43:17Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T19:42:58Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** pierrelouisdelx
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Claudio-Mqz/model
|
Claudio-Mqz
| 2024-11-27T19:30:33Z | 138 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T19:29:41Z |
---
base_model: unsloth/llama-3.2-1b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Claudio-Mqz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-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)
|
CPSC532/src_False_llm_gpt-4o-mini_r16
|
CPSC532
| 2024-11-27T19:27:01Z | 7 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T19:26:28Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CPSC532
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
artucathur/swin-tiny-patch4-window7-224-finetuned-eurosat
|
artucathur
| 2024-11-27T19:14:42Z | 215 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"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
| 2024-11-27T18:34:27Z |
---
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: 0.0890
- Accuracy: 0.9706
## 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 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.5186 | 0.9979 | 351 | 0.1378 | 0.9544 |
| 0.3586 | 1.9986 | 703 | 0.0987 | 0.9662 |
| 0.3499 | 2.9936 | 1053 | 0.0890 | 0.9706 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
autoprogrammer/Llama-3.2-1B-Instruct-Ja-version2
|
autoprogrammer
| 2024-11-27T19:11:34Z | 190 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T19:08:48Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
Triangle104/llama-3.2-3b-chat-doctor-Q4_K_M-GGUF
|
Triangle104
| 2024-11-27T19:09:48Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"medical-qa",
"healthcare",
"llama",
"fine-tuned",
"llama-cpp",
"gguf-my-repo",
"dataset:ruslanmv/ai-medical-chatbot",
"base_model:Ellbendls/llama-3.2-3b-chat-doctor",
"base_model:quantized:Ellbendls/llama-3.2-3b-chat-doctor",
"license:llama3.2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T19:09:04Z |
---
library_name: transformers
tags:
- medical-qa
- healthcare
- llama
- fine-tuned
- llama-cpp
- gguf-my-repo
license: llama3.2
datasets:
- ruslanmv/ai-medical-chatbot
base_model: Ellbendls/llama-3.2-3b-chat-doctor
---
# Triangle104/llama-3.2-3b-chat-doctor-Q4_K_M-GGUF
This model was converted to GGUF format from [`Ellbendls/llama-3.2-3b-chat-doctor`](https://huggingface.co/Ellbendls/llama-3.2-3b-chat-doctor) 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/Ellbendls/llama-3.2-3b-chat-doctor) for more details on the model.
---
Model details:
-
Llama-3.2-3B-Chat-Doctor is a specialized medical question-answering model based on the Llama 3.2 3B architecture. This model has been fine-tuned specifically for providing accurate and helpful responses to medical-related queries.
Developed by: Ellbendl Satria
Model type: Language Model (Conversational AI)
Language: English
Base Model: Meta Llama-3.2-3B-Instruct
Model Size: 3 Billion Parameters
Specialization: Medical Question Answering
License: llama3.2
Model Capabilities
Provides informative responses to medical questions
Assists in understanding medical terminology and health-related concepts
Offers preliminary medical information (not a substitute for professional medical advice)
Direct Use
This model can be used for:
Providing general medical information
Explaining medical conditions and symptoms
Offering basic health-related guidance
Supporting medical education and patient communication
Limitations and Important Disclaimers
β οΈ CRITICAL WARNINGS:
NOT A MEDICAL PROFESSIONAL: This model is NOT a substitute for professional medical advice, diagnosis, or treatment.
Always consult a qualified healthcare provider for medical concerns.
The model's responses should be treated as informational only and not as medical recommendations.
Out-of-Scope Use
The model SHOULD NOT be used for:
Providing emergency medical advice
Diagnosing specific medical conditions
Replacing professional medical consultation
Making critical healthcare decisions
Bias, Risks, and Limitations
Potential Biases
May reflect biases present in the training data
Responses might not account for individual patient variations
Limited by the comprehensiveness of the training dataset
Technical Limitations
Accuracy is limited to the knowledge in the training data
May not capture the most recent medical research or developments
Cannot perform physical examinations or medical tests
Recommendations
Always verify medical information with professional healthcare providers
Use the model as a supplementary information source
Be aware of potential inaccuracies or incomplete information
Training Details
Training Data
Source Dataset: ruslanmv/ai-medical-chatbot
Base Model: Meta Llama-3.2-3B-Instruct
Training Procedure
[Provide details about the fine-tuning process, if available]
Fine-tuning approach
Computational resources used
Training duration
Specific techniques applied during fine-tuning
How to Use the Model
Hugging Face Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Ellbendls/llama-3.2-3b-chat-doctor"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example usage
input_text = "I had a surgery which ended up with some failures. What can I do to fix it?"
# Prepare inputs with explicit padding and attention mask
inputs = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True)
# Generate response with more explicit parameters
outputs = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_new_tokens=150, # Specify max new tokens to generate
do_sample=True, # Enable sampling for more diverse responses
temperature=0.7, # Control randomness of output
top_p=0.9, # Nucleus sampling to maintain quality
num_return_sequences=1 # Number of generated sequences
)
# Decode the generated response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
Ethical Considerations
This model is developed with the intent to provide helpful, accurate, and responsible medical information. Users are encouraged to:
Use the model responsibly
Understand its limitations
Seek professional medical advice for serious health concerns
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/llama-3.2-3b-chat-doctor-Q4_K_M-GGUF --hf-file llama-3.2-3b-chat-doctor-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/llama-3.2-3b-chat-doctor-Q4_K_M-GGUF --hf-file llama-3.2-3b-chat-doctor-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 Triangle104/llama-3.2-3b-chat-doctor-Q4_K_M-GGUF --hf-file llama-3.2-3b-chat-doctor-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/llama-3.2-3b-chat-doctor-Q4_K_M-GGUF --hf-file llama-3.2-3b-chat-doctor-q4_k_m.gguf -c 2048
```
|
mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF
|
mradermacher
| 2024-11-27T19:05:09Z | 53 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:brianmatzelle/llama3.1-8b-instruct-political-subreddits",
"base_model:quantized:brianmatzelle/llama3.1-8b-instruct-political-subreddits",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-27T15:18:09Z |
---
base_model: brianmatzelle/llama3.1-8b-instruct-political-subreddits
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/brianmatzelle/llama3.1-8b-instruct-political-subreddits
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-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/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q6_K-GGUF
|
Triangle104
| 2024-11-27T19:03:06Z | 18 | 0 |
transformers
|
[
"transformers",
"gguf",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:rombodawg/Rombos-Coder-V2.5-Qwen-14b",
"base_model:quantized:rombodawg/Rombos-Coder-V2.5-Qwen-14b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-27T19:00:39Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-14B/blob/main/LICENSE
language:
- en
base_model: rombodawg/Rombos-Coder-V2.5-Qwen-14b
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- qwen
- qwen-coder
- codeqwen
- llama-cpp
- gguf-my-repo
---
# Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q6_K-GGUF
This model was converted to GGUF format from [`rombodawg/Rombos-Coder-V2.5-Qwen-14b`](https://huggingface.co/rombodawg/Rombos-Coder-V2.5-Qwen-14b) 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/rombodawg/Rombos-Coder-V2.5-Qwen-14b) for more details on the model.
---
Model details:
-
Rombos-Coder-V2.5-Qwen-14b is a continues finetuned version of Qwen2.5-Coder-14B-Instruct. I took it upon myself to merge the instruct model with the base model myself using the Ties merge method as demonstrated in my own "Continuous Finetuning" method (Linked bellow).
https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing
This version of the model shows higher performance than the original instruct and base models.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q6_K-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q6_K-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q6_K-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q6_K-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q6_k.gguf -c 2048
```
|
MalyO2/working
|
MalyO2
| 2024-11-27T19:02:11Z | 31 | 0 |
transformers
|
[
"transformers",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50-dc5",
"base_model:finetune:facebook/detr-resnet-50-dc5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2024-11-27T17:13:05Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50-dc5
tags:
- generated_from_trainer
model-index:
- name: facebook/detr-resnet-50-dc5
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. -->
# facebook/detr-resnet-50-dc5
This model is a fine-tuned version of [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5836
- Map: 0.5257
- Map 50: 0.6508
- Map 75: 0.6241
- Map Small: 0.0
- Map Medium: 0.4752
- Map Large: 0.7513
- Mar 1: 0.1853
- Mar 10: 0.6
- Mar 100: 0.7147
- Mar Small: 0.0
- Mar Medium: 0.6684
- Mar Large: 0.8923
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|
| 4.1002 | 0.7692 | 10 | 4.1741 | 0.0003 | 0.001 | 0.0003 | 0.0 | 0.0062 | 0.0002 | 0.0 | 0.0 | 0.0441 | 0.0 | 0.0474 | 0.0462 |
| 1.772 | 1.5385 | 20 | 1.4577 | 0.0298 | 0.05 | 0.0286 | 0.0 | 0.0185 | 0.0656 | 0.0294 | 0.1206 | 0.4882 | 0.0 | 0.3421 | 0.7769 |
| 1.5665 | 2.3077 | 30 | 1.3869 | 0.0339 | 0.0549 | 0.0351 | 0.0 | 0.0407 | 0.0516 | 0.0029 | 0.0824 | 0.6059 | 0.0 | 0.5158 | 0.8308 |
| 2.0258 | 3.0769 | 40 | 1.2246 | 0.0561 | 0.0797 | 0.0593 | 0.0 | 0.0398 | 0.1166 | 0.0265 | 0.1206 | 0.6441 | 0.0 | 0.5789 | 0.8385 |
| 1.5082 | 3.8462 | 50 | 1.1988 | 0.0477 | 0.0869 | 0.0542 | 0.0 | 0.0927 | 0.063 | 0.0235 | 0.0853 | 0.6471 | 0.0 | 0.6316 | 0.7692 |
| 1.3716 | 4.6154 | 60 | 1.1917 | 0.0549 | 0.1014 | 0.0602 | 0.0 | 0.0902 | 0.0761 | 0.0588 | 0.1618 | 0.5971 | 0.0 | 0.5421 | 0.7692 |
| 1.2398 | 5.3846 | 70 | 1.0554 | 0.1329 | 0.1674 | 0.1485 | 0.0 | 0.1462 | 0.1957 | 0.0765 | 0.1882 | 0.7294 | 0.0 | 0.7474 | 0.8154 |
| 1.401 | 6.1538 | 80 | 0.9179 | 0.1176 | 0.1821 | 0.1315 | 0.0 | 0.0835 | 0.2295 | 0.0529 | 0.1794 | 0.7294 | 0.0 | 0.7211 | 0.8538 |
| 2.0328 | 6.9231 | 90 | 0.9198 | 0.1361 | 0.2109 | 0.1554 | 0.0 | 0.0937 | 0.2424 | 0.0559 | 0.2088 | 0.6882 | 0.0 | 0.6368 | 0.8692 |
| 1.6358 | 7.6923 | 100 | 0.9298 | 0.2252 | 0.2898 | 0.2523 | 0.0 | 0.2279 | 0.3487 | 0.1059 | 0.3176 | 0.6882 | 0.0 | 0.6263 | 0.8846 |
| 0.8849 | 8.4615 | 110 | 0.8894 | 0.1893 | 0.2435 | 0.2248 | 0.0 | 0.1438 | 0.3337 | 0.0971 | 0.2265 | 0.7265 | 0.0 | 0.7263 | 0.8385 |
| 1.1906 | 9.2308 | 120 | 0.8505 | 0.2105 | 0.2704 | 0.2598 | 0.0 | 0.1879 | 0.3317 | 0.1324 | 0.2706 | 0.6853 | 0.0 | 0.6474 | 0.8462 |
| 1.0404 | 10.0 | 130 | 0.7320 | 0.2508 | 0.2998 | 0.29 | 0.0 | 0.2031 | 0.4149 | 0.1588 | 0.2971 | 0.7471 | 0.0 | 0.7421 | 0.8692 |
| 1.1534 | 10.7692 | 140 | 0.7996 | 0.2832 | 0.374 | 0.3479 | 0.0 | 0.2502 | 0.411 | 0.1676 | 0.3647 | 0.6647 | 0.0 | 0.6263 | 0.8231 |
| 1.1725 | 11.5385 | 150 | 0.7990 | 0.3115 | 0.4464 | 0.3745 | 0.0 | 0.2972 | 0.4147 | 0.1294 | 0.3735 | 0.6588 | 0.0 | 0.6158 | 0.8231 |
| 0.891 | 12.3077 | 160 | 0.9007 | 0.2856 | 0.3519 | 0.3449 | 0.0 | 0.2607 | 0.3788 | 0.1029 | 0.3529 | 0.6735 | 0.0 | 0.6263 | 0.8462 |
| 1.1 | 13.0769 | 170 | 0.7376 | 0.2642 | 0.3608 | 0.3377 | 0.0 | 0.2281 | 0.4018 | 0.1176 | 0.3676 | 0.7176 | 0.0 | 0.7 | 0.8538 |
| 1.2631 | 13.8462 | 180 | 0.7162 | 0.306 | 0.4363 | 0.3899 | 0.0 | 0.2997 | 0.3933 | 0.1412 | 0.45 | 0.7059 | 0.0 | 0.7053 | 0.8154 |
| 1.0496 | 14.6154 | 190 | 0.7276 | 0.2811 | 0.3866 | 0.3483 | 0.0 | 0.3061 | 0.3685 | 0.1471 | 0.3882 | 0.7235 | 0.0 | 0.7316 | 0.8231 |
| 0.8883 | 15.3846 | 200 | 0.6855 | 0.3373 | 0.4578 | 0.4385 | 0.0 | 0.3441 | 0.4654 | 0.15 | 0.4824 | 0.7412 | 0.0 | 0.7579 | 0.8308 |
| 0.8471 | 16.1538 | 210 | 0.6733 | 0.4351 | 0.5932 | 0.5367 | 0.0 | 0.3702 | 0.6215 | 0.15 | 0.5412 | 0.7206 | 0.0 | 0.7158 | 0.8385 |
| 0.9084 | 16.9231 | 220 | 0.6526 | 0.4279 | 0.5632 | 0.4848 | 0.0 | 0.4011 | 0.572 | 0.1824 | 0.5647 | 0.7294 | 0.0 | 0.7105 | 0.8692 |
| 0.8872 | 17.6923 | 230 | 0.6218 | 0.4376 | 0.5753 | 0.5274 | 0.0 | 0.3879 | 0.6215 | 0.1559 | 0.5853 | 0.7382 | 0.0 | 0.7263 | 0.8692 |
| 0.9739 | 18.4615 | 240 | 0.6590 | 0.4494 | 0.6293 | 0.505 | 0.0 | 0.3889 | 0.65 | 0.1471 | 0.5853 | 0.7029 | 0.0 | 0.6895 | 0.8308 |
| 0.7596 | 19.2308 | 250 | 0.6367 | 0.4625 | 0.6229 | 0.5322 | 0.0 | 0.4106 | 0.6581 | 0.1529 | 0.5853 | 0.7118 | 0.0 | 0.7053 | 0.8308 |
| 0.7124 | 20.0 | 260 | 0.6601 | 0.4619 | 0.6411 | 0.5327 | 0.0 | 0.39 | 0.6852 | 0.1559 | 0.5765 | 0.6794 | 0.0 | 0.6421 | 0.8385 |
| 0.8369 | 20.7692 | 270 | 0.6363 | 0.4736 | 0.64 | 0.5738 | 0.0 | 0.3993 | 0.737 | 0.1559 | 0.5853 | 0.6853 | 0.0 | 0.6474 | 0.8462 |
| 0.8608 | 21.5385 | 280 | 0.6304 | 0.496 | 0.6406 | 0.5583 | 0.0 | 0.4484 | 0.6973 | 0.1588 | 0.5912 | 0.7 | 0.0 | 0.6579 | 0.8692 |
| 0.6174 | 22.3077 | 290 | 0.6825 | 0.4808 | 0.6714 | 0.5569 | 0.0 | 0.4264 | 0.6738 | 0.1529 | 0.5765 | 0.6735 | 0.0 | 0.6158 | 0.8615 |
| 0.5903 | 23.0769 | 300 | 0.6037 | 0.5187 | 0.6804 | 0.6126 | 0.0 | 0.4604 | 0.709 | 0.1824 | 0.6118 | 0.7206 | 0.0 | 0.6842 | 0.8846 |
| 0.6325 | 23.8462 | 310 | 0.6373 | 0.529 | 0.6819 | 0.6246 | 0.0 | 0.4489 | 0.7601 | 0.1765 | 0.5941 | 0.7088 | 0.0 | 0.6579 | 0.8923 |
| 0.8569 | 24.6154 | 320 | 0.6131 | 0.5382 | 0.6684 | 0.6357 | 0.0 | 0.4862 | 0.7382 | 0.1794 | 0.6147 | 0.7294 | 0.0 | 0.7 | 0.8846 |
| 0.7056 | 25.3846 | 330 | 0.5700 | 0.5244 | 0.6545 | 0.6089 | 0.0 | 0.4891 | 0.6871 | 0.1824 | 0.6176 | 0.75 | 0.0 | 0.7421 | 0.8769 |
| 0.5988 | 26.1538 | 340 | 0.5738 | 0.5437 | 0.7119 | 0.651 | 0.0 | 0.5362 | 0.6823 | 0.1853 | 0.6206 | 0.7529 | 0.0 | 0.7579 | 0.8615 |
| 0.5209 | 26.9231 | 350 | 0.6136 | 0.5153 | 0.6944 | 0.6047 | 0.0 | 0.4772 | 0.7054 | 0.1824 | 0.5882 | 0.7059 | 0.0 | 0.6789 | 0.8538 |
| 0.6547 | 27.6923 | 360 | 0.6338 | 0.5166 | 0.6645 | 0.6224 | 0.0 | 0.4842 | 0.7072 | 0.1882 | 0.5971 | 0.7088 | 0.0 | 0.6842 | 0.8538 |
| 0.6324 | 28.4615 | 370 | 0.6083 | 0.5143 | 0.6543 | 0.6279 | 0.0 | 0.4683 | 0.729 | 0.1853 | 0.6 | 0.7118 | 0.0 | 0.6789 | 0.8692 |
| 0.6323 | 29.2308 | 380 | 0.5748 | 0.529 | 0.6552 | 0.637 | 0.0 | 0.48 | 0.7529 | 0.1853 | 0.6088 | 0.7206 | 0.0 | 0.6842 | 0.8846 |
| 0.4509 | 30.0 | 390 | 0.5758 | 0.5311 | 0.652 | 0.6325 | 0.0 | 0.4923 | 0.7454 | 0.1882 | 0.6206 | 0.7324 | 0.0 | 0.7053 | 0.8846 |
| 0.8259 | 30.7692 | 400 | 0.5836 | 0.5257 | 0.6508 | 0.6241 | 0.0 | 0.4752 | 0.7513 | 0.1853 | 0.6 | 0.7147 | 0.0 | 0.6684 | 0.8923 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.0
|
mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF
|
mradermacher
| 2024-11-27T19:01:37Z | 9 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:brianmatzelle/llama3.1-8b-instruct-political-subreddits",
"base_model:quantized:brianmatzelle/llama3.1-8b-instruct-political-subreddits",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T11:37:31Z |
---
base_model: brianmatzelle/llama3.1-8b-instruct-political-subreddits
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/brianmatzelle/llama3.1-8b-instruct-political-subreddits
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-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/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama3.1-8b-instruct-political-subreddits-GGUF/resolve/main/llama3.1-8b-instruct-political-subreddits.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF
|
Triangle104
| 2024-11-27T18:47:03Z | 9 | 0 |
transformers
|
[
"transformers",
"gguf",
"code",
"qwen",
"qwen-coder",
"codeqwen",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:rombodawg/Rombos-Coder-V2.5-Qwen-14b",
"base_model:quantized:rombodawg/Rombos-Coder-V2.5-Qwen-14b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-11-27T18:43:16Z |
---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-14B/blob/main/LICENSE
language:
- en
base_model: rombodawg/Rombos-Coder-V2.5-Qwen-14b
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- qwen
- qwen-coder
- codeqwen
- llama-cpp
- gguf-my-repo
---
# Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF
This model was converted to GGUF format from [`rombodawg/Rombos-Coder-V2.5-Qwen-14b`](https://huggingface.co/rombodawg/Rombos-Coder-V2.5-Qwen-14b) 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/rombodawg/Rombos-Coder-V2.5-Qwen-14b) for more details on the model.
---
Model details:
-
Rombos-Coder-V2.5-Qwen-14b is a continues finetuned version of Qwen2.5-Coder-14B-Instruct. I took it upon myself to merge the instruct model with the base model myself using the Ties merge method as demonstrated in my own "Continuous Finetuning" method (Linked bellow).
https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing
This version of the model shows higher performance than the original instruct and base models.
---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF --hf-file rombos-coder-v2.5-qwen-14b-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 Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q4_k_m.gguf -c 2048
```
|
RyanYr/reflect_mini8Bit_om2_sft-t1
|
RyanYr
| 2024-11-27T18:36:46Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:mistralai/Ministral-8B-Instruct-2410",
"base_model:finetune:mistralai/Ministral-8B-Instruct-2410",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-21T18:36:42Z |
---
base_model: mistralai/Ministral-8B-Instruct-2410
library_name: transformers
model_name: reflect_mini8Bit_om2_sft-t1
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for reflect_mini8Bit_om2_sft-t1
This model is a fine-tuned version of [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410).
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="RyanYr/reflect_mini8Bit_om2_sft-t1", 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/yyr/huggingface/runs/iogzg8nb)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.45.2
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## 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 GallouΓ©dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
suku9/gpt2-moses-pt
|
suku9
| 2024-11-27T18:36:34Z | 104 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2024-11-27T18:36:28Z |
---
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 π€ 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]
|
dhivyeshrk/bart-large-cnn-samsum
|
dhivyeshrk
| 2024-11-27T18:34:24Z | 230 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"arxiv:1910.13461",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-29T15:40:08Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: pegasus-samsum
results: []
datasets:
- samsum
metrics:
- rouge
library_name: transformers
---
<!-- 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. -->
# pegasus-samsum
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the Samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4963
## Model description
Original bart (Bidirectional Auto Regressive Transformers) paper : https://arxiv.org/abs/1910.13461
## Training and evaluation data
Fine-Tuned over 1 epoch. The improvements over facebook/bart-large-cnn over the rouge benchmark is as follows : <br>
Rouge1 : 30.6 % <br>
Rouge2 : 103 % <br>
RougeL : 33.18 % <br>
RougeLSum : 33.18 % <br>
## Training procedure
Please refer to https://github.com/dhivyeshrk/FineTuning-Facebook-bart-large-cnn
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3689 | 0.54 | 500 | 1.4963 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
xw17/gemma-2-2b-it_finetuned_s03
|
xw17
| 2024-11-27T18:31:21Z | 89 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T18:28:14Z |
---
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 π€ 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]
|
AMomozZz/model4
|
AMomozZz
| 2024-11-27T18:30:38Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-11-27T18:23:44Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** AMomozZz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
CPSC532/src_True_llm_gpt-4o-mini_r128
|
CPSC532
| 2024-11-27T18:22:58Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T18:22:25Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CPSC532
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/Qwestion-14B-GGUF
|
mradermacher
| 2024-11-27T18:21:45Z | 61 | 2 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:CultriX/Qwestion-14B",
"base_model:quantized:CultriX/Qwestion-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-26T16:36:26Z |
---
base_model: CultriX/Qwestion-14B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/CultriX/Qwestion-14B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwestion-14B-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/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q4_0_4_4.gguf) | Q4_0_4_4 | 8.6 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-GGUF/resolve/main/Qwestion-14B.Q8_0.gguf) | Q8_0 | 15.8 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Qwestion-14B-i1-GGUF
|
mradermacher
| 2024-11-27T18:21:29Z | 248 | 2 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:CultriX/Qwestion-14B",
"base_model:quantized:CultriX/Qwestion-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-26T21:24:46Z |
---
base_model: CultriX/Qwestion-14B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/CultriX/Qwestion-14B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwestion-14B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ1_M.gguf) | i1-IQ1_M | 4.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ2_M.gguf) | i1-IQ2_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 8.6 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 8.6 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 8.6 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwestion-14B-i1-GGUF/resolve/main/Qwestion-14B.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ippoBox/my_awesome_qa_model
|
ippoBox
| 2024-11-27T18:20:51Z | 119 | 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
| 2024-11-27T14:45:37Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_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_awesome_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: 3.5176
## 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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 50 | 4.1022 |
| No log | 2.0 | 100 | 3.6243 |
| No log | 3.0 | 150 | 3.5176 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
M1LL1X/microsoft-phi-3_5-mini-trained-abliterated-QA-400-all-modules-merged
|
M1LL1X
| 2024-11-27T18:20:20Z | 136 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T18:12:59Z |
---
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 π€ 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]
|
xw17/gemma-2-2b-it_finetuned_s01
|
xw17
| 2024-11-27T18:14:56Z | 88 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T18:12:02Z |
---
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 π€ 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]
|
mradermacher/SeQwence-14B-GGUF
|
mradermacher
| 2024-11-27T18:05:50Z | 41 | 2 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"en",
"base_model:CultriX/SeQwence-14B",
"base_model:quantized:CultriX/SeQwence-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-21T07:32:21Z |
---
base_model: CultriX/SeQwence-14B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- llama-factory
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/CultriX/SeQwence-14B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/SeQwence-14B-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/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q4_0_4_4.gguf) | Q4_0_4_4 | 8.6 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14B-GGUF/resolve/main/SeQwence-14B.Q8_0.gguf) | Q8_0 | 15.8 | 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. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mohammed112/Lameh-page-classifier
|
mohammed112
| 2024-11-27T18:03:45Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T18:02:55Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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
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#### Metrics
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[More Information Needed]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## Model Card Contact
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|
mohammed112/Lameh-general-classifier
|
mohammed112
| 2024-11-27T18:02:37Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T18:01:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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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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## Model Card Contact
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|
mohammed112/Lameh-bank-classifier
|
mohammed112
| 2024-11-27T18:00:53Z | 77 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-27T18:00:01Z |
---
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 π€ 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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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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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
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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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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## More Information [optional]
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## Model Card Contact
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|
mradermacher/SeQwence-14Bv1-i1-GGUF
|
mradermacher
| 2024-11-27T18:00:23Z | 55 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:CultriX/SeQwence-14Bv1",
"base_model:quantized:CultriX/SeQwence-14Bv1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-11-26T01:01:23Z |
---
base_model: CultriX/SeQwence-14Bv1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/CultriX/SeQwence-14Bv1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/SeQwence-14Bv1-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/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ1_M.gguf) | i1-IQ1_M | 4.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ2_M.gguf) | i1-IQ2_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 8.6 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 8.6 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 8.6 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/SeQwence-14Bv1-i1-GGUF/resolve/main/SeQwence-14Bv1.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Condensed_Milk-8B-Model_Stock-GGUF
|
mradermacher
| 2024-11-27T17:59:10Z | 32 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T09:55:21Z |
---
base_model: DreadPoor/Condensed_Milk-8B-Model_Stock
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/DreadPoor/Condensed_Milk-8B-Model_Stock
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-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/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Condensed_Milk-8B-Model_Stock-GGUF/resolve/main/Condensed_Milk-8B-Model_Stock.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
CPSC532/src_True_llm_gpt-4o-mini_r16
|
CPSC532
| 2024-11-27T17:58:30Z | 11 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-11-27T17:57:58Z |
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CPSC532
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
HashirAhmad123/your_model_name
|
HashirAhmad123
| 2024-11-27T17:57:53Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-11-22T23:40:42Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
Muadil/t5-small_sum_DPO_50k_64_10ep
|
Muadil
| 2024-11-27T17:46:50Z | 9 | 0 | null |
[
"safetensors",
"t5",
"license:apache-2.0",
"region:us"
] | null | 2024-11-27T17:45:57Z |
---
license: apache-2.0
---
|
hoangttrang/my_awesome_eli5_clm-model
|
hoangttrang
| 2024-11-27T17:43:56Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:finetune:facebook/opt-350m",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T16:51:59Z |
---
library_name: transformers
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5057
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.4943 | 1.0 | 1303 | 3.4740 |
| 3.1229 | 2.0 | 2606 | 3.4802 |
| 2.8844 | 3.0 | 3909 | 3.5057 |
### Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Amadeus99/parler-tts-mini-v1-pbl-v2.1
|
Amadeus99
| 2024-11-27T17:42:44Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-11-27T12:52:34Z |
---
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 π€ 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]
|
CultriX/SeQwence-14B-EvolMerge
|
CultriX
| 2024-11-27T17:42:40Z | 125 | 2 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:CultriX/Qwen2.5-14B-Wernicke",
"base_model:merge:CultriX/Qwen2.5-14B-Wernicke",
"base_model:CultriX/Qwestion-14B",
"base_model:merge:CultriX/Qwestion-14B",
"base_model:CultriX/SeQwence-14Bv1",
"base_model:merge:CultriX/SeQwence-14Bv1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T00:45:10Z |
---
base_model:
- CultriX/SeQwence-14Bv1
- CultriX/Qwestion-14B
- CultriX/Qwen2.5-14B-Wernicke
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
---
# final_model
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [CultriX/SeQwence-14Bv1](https://huggingface.co/CultriX/SeQwence-14Bv1) as a base.
### Models Merged
The following models were included in the merge:
* [CultriX/Qwestion-14B](https://huggingface.co/CultriX/Qwestion-14B)
* [CultriX/Qwen2.5-14B-Wernicke](https://huggingface.co/CultriX/Qwen2.5-14B-Wernicke)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: CultriX/SeQwence-14Bv1
dtype: bfloat16
merge_method: dare_ties
parameters:
int8_mask: 1.0
normalize: 1.0
slices:
- sources:
- layer_range: [0, 48]
model: CultriX/SeQwence-14Bv1
parameters:
density: [0.9723868064882017, 1.0, 1.0, 1.0, 1.0, 0.9714039829478123]
weight: [0.303941801676895, 0.364404551023674, 0.315900913803921, 0.3276032249804535,
0.32167313684876814, 0.4385348686221433]
- layer_range: [0, 48]
model: CultriX/Qwestion-14B
parameters:
density: [1.0, 0.9914516102369406, 1.0, 0.8035966798672015, 0.8192028457518323,
0.9514479609471497]
weight: [0.23754044230348376, 0.26302919982461254, 0.26313082788173275, 0.17815237275761467,
0.34301750695974753, 0.5374787613924082]
- layer_range: [0, 48]
model: CultriX/Qwen2.5-14B-Wernicke
parameters:
density: [0.9250003667144193, 0.9603820599250329, 0.8766642760655986, 1.0, 0.9993615706551808,
0.7459506348277176]
weight: [0.48038202535582214, 0.5870170049221364, 0.27054455623315504, 0.06016442415521043,
0.4012739361231067, 0.26890177448533076]
```
|
Balaaditya/Llama-3.2-3B-Instruct-Finetuned-Emotion-Classification
|
Balaaditya
| 2024-11-27T17:35:41Z | 136 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-11-27T17:33:00Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ 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]
|
Goshective/kazparc_ru_en_model_2
|
Goshective
| 2024-11-27T17:35:26Z | 114 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-11-27T16:46:29Z |
---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: kazparc_ru_en_model_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# kazparc_ru_en_model_2
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4729
- Bleu: 11.1641
- Gen Len: 13.7274
## 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 adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 2.9762 | 1.0 | 4750 | 2.7105 | 6.2012 | 13.8513 |
| 2.7842 | 2.0 | 9500 | 2.5247 | 9.8558 | 13.6808 |
| 2.7194 | 3.0 | 14250 | 2.4729 | 11.1641 | 13.7274 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
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The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.