modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
giulianad/llama-3.2-1B-GRAFT-Text2KG-LoRA-v3
|
giulianad
| 2025-03-25T14:24:28Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T14:24:21Z
|
---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** giulianad
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Cpelpa/DeepSeek-R1-Distill-Llama-8B-Est-3ep-J
|
Cpelpa
| 2025-03-25T14:17:34Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-25T14:16:28Z
|
---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Cpelpa
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nlarg/dummy-model
|
nlarg
| 2025-03-25T14:17:09Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-03-25T14:12:22Z
|
---
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 ๐ค 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]
|
samchain/EconoDetect-US
|
samchain
| 2025-03-25T14:15:59Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"economics",
"finance",
"en",
"dataset:samchain/economics-relevance",
"base_model:samchain/econo-sentence-v2",
"base_model:finetune:samchain/econo-sentence-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-25T14:06:44Z
|
---
library_name: transformers
license: apache-2.0
base_model: samchain/econo-sentence-v2
tags:
- generated_from_trainer
- economics
- finance
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: EconoDetect
results: []
datasets:
- samchain/economics-relevance
language:
- en
pipeline_tag: text-classification
---
<!-- 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. -->
# EconoDetect
This model is a fine-tuned version of [samchain/econo-sentence-v2](https://huggingface.co/samchain/econo-sentence-v2) on the economics-relevance dataset.
The base model is kept frozen during training, only the classification head is updated.
It achieves the following results on the evaluation set:
- Loss: 0.3973
- Accuracy: 0.8211
- F1: 0.7991
- Precision: 0.7895
- Recall: 0.8211
## Model description
This model is designed to detect whether a text discusses topics related to the US economy.
## Intended uses & limitations
The model can be used as a screening tool to remove texts that are not discussing US economy.
## Training and evaluation data
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5381 | 1.0 | 700 | 0.4333 | 0.7844 | 0.7894 | 0.7952 | 0.7844 |
| 0.4613 | 2.0 | 1400 | 0.4044 | 0.8328 | 0.7679 | 0.7856 | 0.8328 |
| 0.3523 | 3.0 | 2100 | 0.3973 | 0.8211 | 0.7991 | 0.7895 | 0.8211 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.1.0+cu118
- Datasets 3.4.1
- Tokenizers 0.21.1
|
susedo/Heartsticker
|
susedo
| 2025-03-25T14:15:55Z
| 0
| 0
| null |
[
"license:cc-by-nc-sa-2.0",
"region:us"
] | null | 2025-03-25T14:15:54Z
|
---
license: cc-by-nc-sa-2.0
---
|
Moumita-Debnath-Origina-Viral-Video-l-Link/Moumita.Debnath.Origina.Viral.Video.Link.Tiktok.Instagram.x.Twitter
|
Moumita-Debnath-Origina-Viral-Video-l-Link
| 2025-03-25T14:15:50Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-03-25T14:15:44Z
|
<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐ฆ๐ถ๐ด๐ป ๐จ๐ฝ ๐๐ผ ๐๐ช๐ก๐ก ๐ช๐ฎ๐๐ฐ๐ต ๐๐๐๐๐คโค๏ธโค๏ธ)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">๐ด โคโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐ฅ๐ข๐ง๐ค)</a>
|
woman-viral-video-links/Nila.Nambiar.Origina.Viral.Video.Link.Tiktok.Instagram.x.Twitter
|
woman-viral-video-links
| 2025-03-25T14:14:30Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-03-25T14:14:15Z
|
<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐ฆ๐ถ๐ด๐ป ๐จ๐ฝ ๐๐ผ ๐๐ช๐ก๐ก ๐ช๐ฎ๐๐ฐ๐ต ๐๐๐๐๐คโค๏ธโค๏ธ)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">๐ด โคโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐ฅ๐ข๐ง๐ค)</a>
|
baulab/elm-Meta-Llama-3-8B-Instruct
|
baulab
| 2025-03-25T14:12:37Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation",
"conversational",
"arxiv:2410.02760",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-10-21T04:14:36Z
|
---
library_name: transformers
tags: []
pipeline_tag: text-generation
license: apache-2.0
---
# ELM Llama3-8B-Instruct Model Card
> [**Erasing Conceptual Knoweldge from Language Models**](https://arxiv.org/abs/2410.02760),
> Rohit Gandikota, Sheridan Feucht, Samuel Marks, David Bau
#### How to use
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "baulab/elm-Meta-Llama-3-8B-Instruct"
device = 'cuda:0'
dtype = torch.float32
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=dtype)
model = model.to(device)
model.requires_grad_(False)
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
# generate text
inputs = tokenizer(prompt, return_tensors='pt', padding=True)
inputs = inputs.to(device).to(dtype)
outputs = model.generate(**inputs,
max_new_tokens=300,
do_sample=True,
top_p=.95,
temperature=1.2)
outputs = tokenizer.batch_decode(outputs, skip_special_tokens = True)
print(outputs[0])
```
<!-- Provide a quick summary of what the model is/does. -->
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/rohitgandikota/erasing-llm
- **Paper [optional]:** https://arxiv.org/pdf/2410.02760
- **Project [optional]:** https://elm.baulab.info
## 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:**
```
@article{gandikota2024elm,
title={Erasing Conceptual Knowledge from Language Models},
author={Rohit Gandikota and Sheridan Feucht and Samuel Marks and David Bau},
journal={arXiv preprint arXiv:2410.02760},
year={2024}
}
```
|
RichardErkhov/SakanaAI_-_EvoLLM-JP-v1-7B-8bits
|
RichardErkhov
| 2025-03-25T14:11:08Z
| 0
| 0
| null |
[
"safetensors",
"mistral",
"arxiv:2403.13187",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-03-25T14:05:17Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
EvoLLM-JP-v1-7B - bnb 8bits
- Model creator: https://huggingface.co/SakanaAI/
- Original model: https://huggingface.co/SakanaAI/EvoLLM-JP-v1-7B/
Original model description:
---
library_name: transformers
license: other
language:
- ja
---
# ๐ EvoLLM-JP-v1-7B
๐ค [Models](https://huggingface.co/SakanaAI) | ๐ [Paper](https://arxiv.org/abs/2403.13187) | ๐ [Blog](https://sakana.ai/evolutionary-model-merge/) | ๐ฆ [Twitter](https://twitter.com/SakanaAILabs)
<!-- Provide a quick summary of what the model is/does. -->
**EvoLLM-JP-v1-7B** is an experimental general-purpose Japanese LLM. This model was created using the Evolutionary Model Merge method. Please refer to our [report](https://arxiv.org/abs/2403.13187) and [blog](https://sakana.ai/evolutionary-model-merge/) for more details. This model was produced by merging the following models. We are grateful to the developers of the source models.
- [Shisa Gamma 7B v1](https://huggingface.co/augmxnt/shisa-gamma-7b-v1)
- [WizardMath 7B V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
- [Abel 7B 002](https://huggingface.co/GAIR/Abel-7B-002)
## Usage
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# 1. load model
device = "cuda" if torch.cuda.is_available() else "CPU"
repo_id = "SakanaAI/EvoLLM-JP-v1-7B"
model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained(repo_id)
model.to(device)
# 2. prepare inputs
text = "้ข่ฅฟๅผใง้ข็ฝใๅ่ซใ่จใฃใฆใฟใฆไธใใใ"
messages = [
{"role": "system", "content": "ใใชใใฏๅฝน็ซใคใๅ่ฆใใชใใๆค้ฒใใใฆใใชใใขใทในใฟใณใใงใใ"},
{"role": "user", "content": text},
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
# 3. generate
output_ids = model.generate(**inputs.to(device))
output_ids = output_ids[:, inputs.input_ids.shape[1] :]
generated_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
print(generated_text)
```
</details>
## Model Details
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Sakana AI](https://sakana.ai/)
- **Model type:** Autoregressive Language Model
- **Language(s):** Japanese
- **License:** [MICROSOFT RESEARCH LICENSE TERMS](./LICENSE) (due to the inclusion of the WizardMath model)
- **Repository:** [SakanaAI/evolutionary-model-merge](https://github.com/SakanaAI/evolutionary-model-merge)
- **Paper:** https://arxiv.org/abs/2403.13187
- **Blog:** https://sakana.ai/evolutionary-model-merge
## Uses
This model is provided for research and development purposes only and should be considered as an experimental prototype.
It is not intended for commercial use or deployment in mission-critical environments.
Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed.
Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained.
Users must fully understand the risks associated with the use of this model and use it at their own discretion.
## Acknowledgement
We would like to thank the developers of the source models for their contributions and for making their work available.
## Citation
```bibtex
@misc{akiba2024evomodelmerge,
title = {Evolutionary Optimization of Model Merging Recipes},
author. = {Takuya Akiba and Makoto Shing and Yujin Tang and Qi Sun and David Ha},
year = {2024},
eprint = {2403.13187},
archivePrefix = {arXiv},
primaryClass = {cs.NE}
}
```
|
second-state/Tessa-T1-3B-GGUF
|
second-state
| 2025-03-25T14:10:08Z
| 0
| 0
| null |
[
"gguf",
"qwen2",
"en",
"base_model:Tesslate/Tessa-T1-3B",
"base_model:quantized:Tesslate/Tessa-T1-3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-25T08:40:39Z
|
---
base_model: Tesslate/Tessa-T1-3B
license: apache-2.0
model_creator: Tesslate
model_name: Tessa-T1-3B
quantized_by: Second State Inc.
language:
- en
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Tessa-T1-3B-GGUF
## Original Model
[Tesslate/Tessa-T1-3B](https://huggingface.co/Tesslate/Tessa-T1-3B)
## Run with LlamaEdge
- LlamaEdge version: [v0.16.14](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.16.14) and above
- Prompt template
- Prompt type: `chatml`
```text
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Prompt type: `chatml-think`
```text
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
<|im_start|>think
```
- Context size: `32000`
- Run as LlamaEdge service
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Tessa-T1-3B-Q5_K_M.gguf \
llama-api-server.wasm \
--model-name Tessa-T1-3B \
--prompt-template chatml \
--ctx-size 32000
# Think mode
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Tessa-T1-3B-Q5_K_M.gguf \
llama-api-server.wasm \
--model-name Tessa-T1-3B \
--prompt-template chatml-think \
--ctx-size 32000
```
- Run as LlamaEdge command app
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Tessa-T1-3B-Q5_K_M.gguf \
llama-chat.wasm \
--prompt-template chatml \
--ctx-size 32000
# Think mode
wasmedge --dir .:. --nn-preload default:GGML:AUTO:Tessa-T1-3B-Q5_K_M.gguf \
llama-chat.wasm \
--prompt-template chatml-think \
--ctx-size 32000
```
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [Tessa-T1-3B-Q2_K.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q2_K.gguf) | Q2_K | 2 | 1.27 GB| smallest, significant quality loss - not recommended for most purposes |
| [Tessa-T1-3B-Q3_K_L.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q3_K_L.gguf) | Q3_K_L | 3 | 1.71 GB| small, substantial quality loss |
| [Tessa-T1-3B-Q3_K_M.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q3_K_M.gguf) | Q3_K_M | 3 | 1.59 GB| very small, high quality loss |
| [Tessa-T1-3B-Q3_K_S.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q3_K_S.gguf) | Q3_K_S | 3 | 1.45 GB| very small, high quality loss |
| [Tessa-T1-3B-Q4_0.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q4_0.gguf) | Q4_0 | 4 | 1.82 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [Tessa-T1-3B-Q4_K_M.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q4_K_M.gguf) | Q4_K_M | 4 | 1.93 GB| medium, balanced quality - recommended |
| [Tessa-T1-3B-Q4_K_S.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q4_K_S.gguf) | Q4_K_S | 4 | 1.83 GB| small, greater quality loss |
| [Tessa-T1-3B-Q5_0.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q5_0.gguf) | Q5_0 | 5 | 2.17 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [Tessa-T1-3B-Q5_K_M.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q5_K_M.gguf) | Q5_K_M | 5 | 2.22 GB| large, very low quality loss - recommended |
| [Tessa-T1-3B-Q5_K_S.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q5_K_S.gguf) | Q5_K_S | 5 | 2.17 GB| large, low quality loss - recommended |
| [Tessa-T1-3B-Q6_K.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q6_K.gguf) | Q6_K | 6 | 2.54 GB| very large, extremely low quality loss |
| [Tessa-T1-3B-Q8_0.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-Q8_0.gguf) | Q8_0 | 8 | 3.29 GB| very large, extremely low quality loss - not recommended |
| [Tessa-T1-3B-f16.gguf](https://huggingface.co/second-state/Tessa-T1-3B-GGUF/blob/main/Tessa-T1-3B-f16.gguf) | f16 | 16 | 6.18 GB| |
*Quantized with llama.cpp b4944*
|
abhay2727/segformer-b0-scene-parse-150
|
abhay2727
| 2025-03-25T14:08:45Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"pipeline_tag: image-segmentation",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T06:07:44Z
|
---
library_name: transformers
license: other
base_model: nvidia/mit-b0
tags:
- generated_from_trainer
- 'pipeline_tag: image-segmentation'
model-index:
- name: segformer-b0-scene-parse-150
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. -->
# segformer-b0-scene-parse-150
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1093
- Mean Iou: 0.1061
- Mean Accuracy: 0.1819
- Overall Accuracy: 0.5698
- Per Category Iou: [0.49304871076479706, 0.8045352183435942, 0.9102904514568413, 0.2750351435610755, 0.6478054906449203, 0.17832002366163857, 0.410861924476854, 0.3601149518335627, 0.0, 0.7497660782234421, 0.0, 0.0, 0.034027777777777775, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.269010707248166, 0.06530417065158635, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan]
- Per Category Accuracy: [0.9198922694133461, 0.9658568458254194, 0.9425630652504902, 0.40579602110035823, 0.848176747283853, 0.18761912912235387, 0.6125900991206885, 0.9211193821242585, 0.0, 0.930481904393265, 0.0, 0.0, 0.03960129310344827, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.4112885662431942, 0.8192488262910798, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 4.9385 | 1.0 | 20 | 4.8790 | 0.0134 | 0.0641 | 0.2346 | [0.20371386637738498, 0.4219338515746165, 0.5338068703400358, 0.0892001046957363, 0.18443318121239044, 0.12403386555476457, 0.003557695380321897, 0.07917676359719979, 0.0, 0.0, 0.0011856579303682127, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.008865157444318473, 0.0008899598833467968, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.030151103683587494, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.005728172192327721, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0] | [0.23112844410872488, 0.4685074571590314, 0.8174183866833648, 0.11805589051667814, 0.1900990717229322, 0.15181620856251507, 0.006228844773085423, 0.0974231020095456, 0.0, 0.0, 0.0012438192930192562, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.6322378716744914, 0.006501950585175552, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.08330126971912274, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.016489007328447703, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 4.8009 | 2.0 | 40 | 4.4509 | 0.0328 | 0.1102 | 0.4471 | [0.432322190513234, 0.6656473834488024, 0.6157113209274023, 0.26647225593544865, 0.5765556111978476, 0.13547153074236293, 0.06545914793742857, 0.1713040066913893, 0.0028021015761821367, 0.0, 0.0023645589271083386, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.022065957465076544, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.031392540917628116, nan, 0.0, 0.0, 0.0, nan, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, 0.0, nan, 0.0, nan, nan, 0.0] | [0.7161960291239409, 0.8257633072857422, 0.9088296827477427, 0.49003959605881087, 0.6110498272488865, 0.1901009047978403, 0.14901671903993735, 0.23960285332698342, 0.011729734072467262, 0.0, 0.0025260280703909583, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.6703182055294732, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.03303218520609825, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 3.9918 | 3.0 | 60 | 4.0238 | 0.0579 | 0.1116 | 0.4997 | [0.42683366372319337, 0.7392372742392653, 0.7675109557279338, 0.3039353897515746, 0.594530541042968, 0.27828872443817293, 0.060722850673681106, 0.1796601658675651, 4.4317073891334535e-05, 0.0, 0.0014319275086698735, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.00831538345939989, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.7659369592655786, 0.8347917868083148, 0.923390644910418, 0.7600777012361162, 0.702254089830579, 0.4771699977438403, 0.12678682990964446, 0.22966383335576515, 0.00012051096649795132, 0.0, 0.0014741561991339332, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.08659363588941053, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 3.9014 | 4.0 | 80 | 3.7035 | 0.0692 | 0.1258 | 0.5353 | [0.4394071479652167, 0.7609911133185115, 0.7633684641975607, 0.34347569035183184, 0.5637391226338925, 0.3368598393101294, 0.1272485148617859, 0.08330499204912081, 0.0, 0.0, 0.0003951908315727075, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.03901242959798019, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8259465642775607, 0.9443874230873008, 0.9248146106184028, 0.7999105471096631, 0.7847479498813636, 0.5117825995627718, 0.22809880365152038, 0.09383056041578233, 0.0, 0.0, 0.00039925063726544026, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.4191444966092853, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 3.5577 | 5.0 | 100 | 3.5248 | 0.0736 | 0.1187 | 0.5240 | [0.4290561823492354, 0.8220371322858423, 0.8560104853653364, 0.29491092410753467, 0.44616302379199335, 0.3536123222362577, 0.1690667644516619, 0.028768596522674314, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.05830602035535622, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.7231994825842791, 0.9174368958748869, 0.8990043193626476, 0.928328064090365, 0.8639428880656038, 0.4725954394454515, 0.30094271887209423, 0.029910238225884934, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0881585811163276, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 3.0393 | 6.0 | 120 | 3.3131 | 0.0700 | 0.1294 | 0.5407 | [0.4398795899399337, 0.6839517407385663, 0.833054453315485, 0.3522109430797707, 0.38304699744083165, 0.3657252132980288, 0.3048773873162618, 0.05261534481557764, 0.0005785398638584916, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.08578276775577141, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.765253989820618, 0.9861543914968812, 0.9543308639090414, 0.7200869973207983, 0.8037401656745619, 0.5802687164007251, 0.47171377152821165, 0.05474743500812722, 0.0009440025709006187, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.35472091810119977, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 3.4548 | 7.0 | 140 | 3.0650 | 0.0755 | 0.1338 | 0.5450 | [0.4464092095008979, 0.7826643161033131, 0.8568665377176016, 0.31849923391262047, 0.39958030825634966, 0.391608720828959, 0.1905369508983981, 0.13564128631815506, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.10458470144716041, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.7630844155060611, 0.9888552639236454, 0.9203455490118043, 0.7665849605135648, 0.86199683636515, 0.6471444020009802, 0.3063323480320394, 0.18371657228048743, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.44861763171622326, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 3.6022 | 8.0 | 160 | 2.9873 | 0.0789 | 0.1320 | 0.5578 | [0.47712040427399843, 0.8248885149724448, 0.8160675598001503, 0.339747012396975, 0.369609452779767, 0.4315713538805684, 0.22984963251454096, 0.13951752318088953, 0.0, 0.0, 2.2993791676247414e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.07804278001776806, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9141509821004086, 0.975223926396597, 0.9434612590047575, 0.6138397653177112, 0.8808745785289098, 0.5073247391801582, 0.41933739265033615, 0.25734814523392935, 0.0, 0.0, 2.3033690611467706e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.29786124152321336, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.8648 | 9.0 | 180 | 2.8662 | 0.0862 | 0.1342 | 0.5620 | [0.47156095061743336, 0.8275136245704428, 0.8735358711566618, 0.31155815380579643, 0.4206737183392153, 0.38058481615927664, 0.2328211798498767, 0.35809124778031826, 0.0, 0.002012773369460035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8462860218115825, 0.9567807505303868, 0.9410660756600446, 0.5832898492894195, 0.8695625026016734, 0.6647657872830391, 0.40057626138768965, 0.6388615680874634, 0.0, 0.002012773369460035, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 3.0779 | 10.0 | 200 | 2.7385 | 0.0811 | 0.1315 | 0.5669 | [0.4676339505322603, 0.8362636216458894, 0.8729100760469264, 0.36465918495133093, 0.35745146249118165, 0.346829217608146, 0.22907763295848257, 0.3008845386416126, 0.0, 0.009386588380716935, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.02526992878474615, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9176647754285452, 0.9756313057832001, 0.932927563959793, 0.730360924872727, 0.8911251717104441, 0.38953764285769854, 0.4013595292932872, 0.3965461905600017, 0.0, 0.00940584478420747, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.14345331246739698, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.1142 | 11.0 | 220 | 2.6322 | 0.0853 | 0.1322 | 0.5509 | [0.4485435556592337, 0.7267937184713177, 0.8540765930149212, 0.2780348303081657, 0.50768525592055, 0.34783458571422077, 0.33636808381161476, 0.36746472328874324, 0.0, 0.14076337058664296, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.0005474202819214452, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8491820053237831, 0.9848185045473062, 0.9527864087949964, 0.43739832407378987, 0.8644840361320402, 0.5952916280915223, 0.5257219585427487, 0.46669910652351715, 0.0, 0.14089413586220245, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.0010432968179447052, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.8039 | 12.0 | 240 | 2.6373 | 0.0806 | 0.1246 | 0.5500 | [0.45912198662279075, 0.8526622749477928, 0.8924898918533234, 0.3483417116710504, 0.4679396820193411, 0.1688766253401875, 0.11760283131850723, 0.215600417504646, 0.0, 0.10676954654300809, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0008711433756805807, 0.07531834191276077, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.940429619154035, 0.9645315401586134, 0.931675204375591, 0.7851508202040753, 0.8913020855013944, 0.17379431603351564, 0.204190483294947, 0.26304237542577313, 0.0, 0.10872846913102381, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0008711433756805807, 0.21752738654147105, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.7232 | 13.0 | 260 | 2.5699 | 0.0868 | 0.1384 | 0.5645 | [0.45421246340548294, 0.8262418104614371, 0.9003839956114787, 0.32850295131323537, 0.4332973417656435, 0.34906027565247527, 0.2710000547103089, 0.4212031847380448, 0.0, 0.09485548912836525, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.002032667876588022, 0.0004891570194032284, 0.0, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.873539639980983, 0.9290974694862258, 0.9588583446216085, 0.5454258439924052, 0.8769928818215876, 0.5093319433314921, 0.5080704568130321, 0.7881539305718043, 0.0, 0.09641958583317205, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.002032667876588022, 0.000782472613458529, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.6447 | 14.0 | 280 | 2.6064 | 0.0985 | 0.1435 | 0.5632 | [0.4452658720856849, 0.8480311592949272, 0.8857872960929841, 0.34181584104470397, 0.5191878098835837, 0.36487320128206846, 0.2816761976978084, 0.459629894984154, 0.0, 0.37236925132697374, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.011796733212341199, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8343231965866008, 0.9336315491527037, 0.9137953067550743, 0.6822317619149935, 0.8924884485701203, 0.4233411391272552, 0.4987924619788704, 0.7372475126567207, 0.0, 0.388310431585059, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.011796733212341199, 0.0, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.7611 | 15.0 | 300 | 2.4407 | 0.0986 | 0.1522 | 0.5870 | [0.46518755960580693, 0.8208948916085005, 0.9003932524350342, 0.376248908445513, 0.39905318329544814, 0.446987463694929, 0.3890571637664661, 0.40172720100046694, 6.901597029552638e-05, 0.32906869525126786, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0, 0.005384922217790187, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8475530145981702, 0.9762635374286425, 0.9555248043872748, 0.5819085914239234, 0.8842359405569662, 0.6094354154834795, 0.713836800537098, 0.7749019039435132, 0.00010042580541495943, 0.3315269982581769, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0, 0.02112676056338028, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.0289 | 16.0 | 320 | 2.4671 | 0.0971 | 0.1565 | 0.5754 | [0.4774358048925406, 0.7710510076862047, 0.8943649769259331, 0.3294111727286691, 0.4313197557233537, 0.3354121138980397, 0.34293994815668205, 0.453534192064702, 0.0, 0.34498201025798053, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.0019233560749020177, 0.08236222166445552, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9385713630670396, 0.9717797188553168, 0.9234344592398944, 0.5454214590468005, 0.8952358156766432, 0.4000171156943137, 0.532929888197831, 0.7268736605617617, 0.0, 0.3488678149796787, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0019237749546279492, 0.599113197704747, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.7669 | 17.0 | 340 | 2.4151 | 0.0965 | 0.1592 | 0.5668 | [0.46186865329747306, 0.799411496073266, 0.8823954529267595, 0.33485086986873264, 0.41256954994416833, 0.2731064068782974, 0.41078882816198303, 0.458716641869005, 0.0, 0.49121119046737965, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.039995648705489883, 0.06659984408063259, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8719758088843947, 0.9400199986244332, 0.9228064538507319, 0.5363490065905733, 0.895891437372518, 0.355609668811315, 0.7120651231315797, 0.7501061197444844, 0.0, 0.5137991097348558, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.040036297640653355, 0.4679186228482003, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.2657 | 18.0 | 360 | 2.4313 | 0.1010 | 0.1589 | 0.5642 | [0.4427082804616837, 0.8444665503725616, 0.9059702695222313, 0.3163265062227969, 0.4663572706251909, 0.3131455487281633, 0.31484068682680605, 0.4349839344502984, 0.0, 0.6023682479350925, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.036406533575317604, 0.07097381635581061, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8419734196979972, 0.9386761757127816, 0.9441695906646269, 0.5803256260606087, 0.889928401948133, 0.3889463734177708, 0.5211342465242487, 0.7975028212322314, 0.0, 0.6379717437584672, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.036406533575317604, 0.4128847157016171, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.435 | 19.0 | 380 | 2.4206 | 0.1017 | 0.1633 | 0.5750 | [0.47587247785629777, 0.8337097057816361, 0.9052716707393167, 0.30265491463716615, 0.3954330795643889, 0.3576023097125309, 0.428650134643602, 0.3575262066998923, 0.0, 0.6043505328304097, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.06368408245559147, 0.05585924154424325, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8978731414422481, 0.9531778237475729, 0.9482078113647068, 0.39660079016719796, 0.8940494526079175, 0.46928121863743516, 0.7109741428344973, 0.8697056600648106, 0.0, 0.6387845945422876, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.066497277676951, 0.3411580594679186, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.0639 | 20.0 | 400 | 2.2936 | 0.1031 | 0.1650 | 0.5887 | [0.48162225947102133, 0.8090029973527031, 0.9067951247005777, 0.34495752913048106, 0.4269398186973364, 0.39135777320725706, 0.3817801449849654, 0.41601739267304133, 0.0015173250937982785, 0.6989510867606243, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.052339060564677485, 0.0351783241645148, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9042201719731542, 0.9781628776856618, 0.9398940423465494, 0.5402866877436385, 0.8900636889647421, 0.46335296451605373, 0.6109023432764843, 0.875648365755935, 0.0022093677191291072, 0.7505709309076833, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.05981851179673321, 0.24517475221700574, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.7727 | 21.0 | 420 | 2.2841 | 0.1027 | 0.1613 | 0.5789 | [0.484961487568254, 0.7390012140315648, 0.9032574335498907, 0.33461936467539544, 0.5310333046515159, 0.16621723339106542, 0.4379688605803255, 0.4617092056208775, 0.0, 0.6625537224455844, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.04866949211422219, 0.056333401765551225, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9437406936363175, 0.9774221878918381, 0.9452174833779388, 0.5061981206123138, 0.8734650126961662, 0.17695293962049838, 0.692464776255793, 0.8334178840240607, 0.0, 0.7399264563576543, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.0508529945553539, 0.35785080855503393, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.9101 | 22.0 | 440 | 2.2731 | 0.1044 | 0.1774 | 0.5852 | [0.48235513994599366, 0.7764207583731297, 0.8919460308996253, 0.3545654999196858, 0.44258842251426256, 0.24647779733634462, 0.4678743504689228, 0.4257994542365998, 0.0, 0.7745900175068777, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.09529177718832892, 0.053237410071942444, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9031317648364855, 0.9698327628258374, 0.9283197569765191, 0.5323543211446462, 0.885651250884569, 0.2886795241836981, 0.761513571981388, 0.8901324167348249, 0.0, 0.8391716663441068, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.10431941923774955, 0.7044861763171623, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.7611 | 23.0 | 460 | 2.2454 | 0.0990 | 0.1712 | 0.5746 | [0.4724559741243822, 0.8184581188051442, 0.9077165708344602, 0.32851093666975256, 0.4416565025944225, 0.2278954440304193, 0.33723401241039297, 0.3640539776862547, 0.0, 0.7150618004156488, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.1586625738521966, 0.08058810864015256, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.913361826593334, 0.9545639717903002, 0.9435634924402025, 0.5259303758336877, 0.8893248137201848, 0.2559885480445319, 0.5549080126441819, 0.880379753388067, 0.0, 0.7591252177278885, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.17448275862068965, 0.6833594157537819, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.9725 | 24.0 | 480 | 2.2596 | 0.1020 | 0.1780 | 0.5772 | [0.5088157092231094, 0.7265937161258674, 0.8999405935721542, 0.32234514235309447, 0.5118427673337254, 0.1607964146646095, 0.5172089524167763, 0.34321086566708636, 0.0008991948119184185, 0.776473026179457, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.14182284158257247, 0.08844776119402985, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9241155787019719, 0.97170035923455, 0.9347641146036811, 0.42303762721823435, 0.8786371394080673, 0.17027003897710388, 0.7949236780022939, 0.9313586433237739, 0.0013256206314774645, 0.8702148248500097, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.1606896551724138, 0.7728221178925404, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.4167 | 25.0 | 500 | 2.2193 | 0.1081 | 0.1812 | 0.5832 | [0.480534825180345, 0.8026797505624291, 0.9074070173960659, 0.3135738032687792, 0.5243463510206126, 0.26855496745233215, 0.4538987516466972, 0.39315646386050435, 0.0, 0.7675438596491229, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.19760479041916168, 0.08172472450262555, 0.0, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.940279993339238, 0.9598731304196008, 0.9438811463289069, 0.47314440064371, 0.8717479082545894, 0.29881668313403925, 0.6746920544930671, 0.8683804574019816, 0.0, 0.8467195664795819, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.23117967332123412, 0.8646322378716745, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.3278 | 26.0 | 520 | 2.2142 | 0.1069 | 0.1844 | 0.5785 | [0.4976561621224289, 0.7944220678844354, 0.9113580925792761, 0.30515904979466796, 0.5401271674070836, 0.1771746335714812, 0.5490247438505866, 0.3620566229919946, 0.0005535369592369705, 0.7801701222753854, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.23548544478777036, 0.08640628962718742, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9182367321722048, 0.9652404861041304, 0.943997984540844, 0.40534437170306903, 0.8742975481829913, 0.18714455759820128, 0.8404184888524193, 0.9163362287631097, 0.0007833212822366836, 0.9088445906715695, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.26279491833030855, 0.8886280646844027, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.4363 | 27.0 | 540 | 2.2510 | 0.1035 | 0.1725 | 0.5720 | [0.4739622435999673, 0.8092979493365501, 0.9138962498293468, 0.3166641292938358, 0.4495030692779889, 0.34577275314327105, 0.33831212070356276, 0.4120511345959858, 0.0, 0.6861234923964342, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2680101850701776, 0.05977257623864727, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8821238177750641, 0.88738340749049, 0.9532172497015149, 0.5198791508991331, 0.8961620114057361, 0.4159269315450026, 0.5827140232928956, 0.9050202404000456, 0.0, 0.8103348171085737, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.31328493647912886, 0.4222743870631195, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.3578 | 28.0 | 560 | 2.1477 | 0.1043 | 0.1789 | 0.5768 | [0.4857184626115484, 0.7417210410077489, 0.9087069308394372, 0.31809451479579764, 0.6425268543825081, 0.14530201855049432, 0.40576527845652277, 0.42029390154298313, 0.0, 0.7687543252595156, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.22909932287590595, 0.04526468437426408, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.946149186590631, 0.9745916947511547, 0.9426287867447048, 0.5068997119090738, 0.8415997169379345, 0.147957397480881, 0.5651837415961881, 0.8883309693650415, 0.0, 0.8599574221017999, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.24439201451905626, 0.952529994783516, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.1159 | 29.0 | 580 | 2.1510 | 0.1071 | 0.1822 | 0.5812 | [0.48688990640395347, 0.838947488867042, 0.9134598562621152, 0.3032418304897648, 0.48312968299711817, 0.30199214566226346, 0.478506067723625, 0.34186003950067484, 0.0, 0.7396190476190476, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2843404926586579, 0.0778348830991355, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.919216539927166, 0.9503711384931195, 0.9531989937308997, 0.4095539194836288, 0.8723202764017817, 0.3290414433198223, 0.7294648601773542, 0.9282734058743749, 0.0, 0.9017998838784594, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.34021778584392015, 0.6833594157537819, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.1151 | 30.0 | 600 | 2.1850 | 0.1062 | 0.1798 | 0.5689 | [0.4571041638211183, 0.8437832351555709, 0.918619615874207, 0.2928590682420594, 0.4901072608698177, 0.3147922014847707, 0.4013679114158636, 0.320329429118303, 0.0, 0.7367851166453391, 0.0, 0.0, 0.010595358224016145, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.3205846371772584, 0.09614800182343108, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8561082325571293, 0.9192542311904472, 0.9564668124710186, 0.45734982657540135, 0.8782729051325813, 0.35298007577584667, 0.6462426452076127, 0.9443000755779644, 0.0, 0.8691697309850978, 0.0, 0.0, 0.011314655172413793, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.36145190562613433, 0.6601460615545123, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.0961 | 31.0 | 620 | 2.1314 | 0.1058 | 0.1769 | 0.5820 | [0.48284931681214904, 0.7333064597800207, 0.9110912633972069, 0.3280264720558154, 0.6227321806043463, 0.1935330297701776, 0.48986540479317364, 0.3598059232914005, 0.0, 0.7546794530278814, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.24957996512918054, 0.056766939309348584, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9520859527906421, 0.976885187791316, 0.9423111328560005, 0.49293366015794576, 0.852287391250052, 0.19608361794658347, 0.6573669143906828, 0.8483781797099048, 0.0, 0.8224501645055158, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.28577132486388385, 0.7584767866458008, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.7615 | 32.0 | 640 | 2.1422 | 0.1084 | 0.1735 | 0.5793 | [0.4605224321133412, 0.8398257585130646, 0.9125307748391255, 0.32323923236815394, 0.5497739497739498, 0.3699341975204044, 0.3406435925597458, 0.418698824036093, 0.0, 0.6922184732180514, 0.0, 0.0, 0.0251621271076524, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.29251055497766626, 0.08803219463117941, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.873568599816105, 0.9516726362736955, 0.9459623269790385, 0.5864294703424204, 0.866867168963077, 0.40763359966391, 0.5408185149613495, 0.8253424302974459, 0.0, 0.7623379136829882, 0.0, 0.0, 0.026131465517241378, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.3470417422867514, 0.5020865936358894, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.5872 | 33.0 | 660 | 2.1703 | 0.1046 | 0.1741 | 0.5668 | [0.47354316390499746, 0.8291208653401098, 0.9123934476918645, 0.2765290105146524, 0.5737618365205943, 0.149463776641477, 0.3728721723032465, 0.3533909465020576, 0.0, 0.7698624843000226, 0.0, 0.0, 0.045824847250509164, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2845640597934176, 0.08301370776773505, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9339040029732097, 0.9619311899181537, 0.9481019267351387, 0.4202312620311945, 0.8708113058319111, 0.15418128632222627, 0.5390188637020598, 0.8890660427170796, 0.0, 0.9252951422488872, 0.0, 0.0, 0.04849137931034483, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.32199637023593464, 0.6491914449660928, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.7727 | 34.0 | 680 | 2.1558 | 0.1071 | 0.1842 | 0.5638 | [0.4685583634112767, 0.8321950944566044, 0.910793685494762, 0.27737455602711497, 0.5695118939830439, 0.27001587192050236, 0.3655693450046022, 0.38358432616398197, 0.0, 0.7480637666193365, 0.0, 0.0, 0.06609485368314834, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2960685393593839, 0.058754806990215645, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.864998901939585, 0.9373746779322057, 0.9431107443689458, 0.45681486321162185, 0.8640469550014569, 0.3044104032301983, 0.5518122394934868, 0.9089958483885329, 0.0, 0.8972711437971744, 0.0, 0.0, 0.07058189655172414, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.36, 0.9444444444444444, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.1858 | 35.0 | 700 | 2.0996 | 0.1052 | 0.1786 | 0.5686 | [0.5024297787395016, 0.7590018095945441, 0.908331770558742, 0.22697513247791506, 0.6468363190261242, 0.28543591633127374, 0.43902686333094487, 0.32166085757407953, 0.0, 0.7944138890729574, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.20923709798055348, 0.06185839448702051, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9256938897161212, 0.978607291561956, 0.9372980433250695, 0.314220817090764, 0.8559817674728385, 0.3133961427448906, 0.6056059602957768, 0.9201047738355299, 0.0, 0.9280820592219857, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.28431941923774956, 0.7936880542514345, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.8596 | 36.0 | 720 | 2.1546 | 0.1064 | 0.1802 | 0.5551 | [0.4582069483508894, 0.824978168353675, 0.914361959357141, 0.25951209405914955, 0.6113601078009199, 0.1592655730374759, 0.364666735988354, 0.3874110568427149, 0.011206814179827735, 0.7485374569142712, 0.0, 0.0, 0.042531645569620254, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2512054976467531, 0.07563568917549118, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8421351121107618, 0.9471385566072175, 0.9457907208552557, 0.4394066291607653, 0.8687507805020189, 0.17734971253413415, 0.555924395997874, 0.9419292051889967, 0.01648991724913634, 0.916237662086317, 0.0, 0.0, 0.04525862068965517, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.3157894736842105, 0.9147104851330203, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.0682 | 37.0 | 740 | 2.1284 | 0.1050 | 0.1757 | 0.5638 | [0.47810987745044226, 0.8110497488177993, 0.9144996433678722, 0.2729651644682312, 0.5486497709606675, 0.1329546261449975, 0.4409382178696651, 0.3647185531396058, 0.0023479690068091102, 0.7618178856491286, 0.0, 0.0, 0.043561512685495456, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.24821049099653283, 0.12374287195438051, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.8934109135138657, 0.9459772608233296, 0.9503072479854536, 0.4043446041051861, 0.8799796028805728, 0.1456701183316866, 0.680137631360555, 0.9290809512470364, 0.003213625773278702, 0.905128701374105, 0.0, 0.0, 0.0490301724137931, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.3222141560798548, 0.6225873761085029, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 2.1905 | 38.0 | 760 | 2.0799 | 0.1034 | 0.1717 | 0.5688 | [0.4940693803921365, 0.7791411571958266, 0.9067886147366049, 0.2868853178656194, 0.5306427971887802, 0.13281672907938155, 0.44025302737882877, 0.3836106895529157, 5.943006567022257e-05, 0.7623588287008859, 0.0, 0.0, 0.006356861025865849, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.23707495031444828, 0.10494995942656207, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9180919329965948, 0.9560929671504076, 0.9401058116056856, 0.4298518326880155, 0.8816030470798818, 0.13934509129666944, 0.6742817713044208, 0.9107144705918894, 8.034064433196755e-05, 0.8726920843816528, 0.0, 0.0, 0.0078125, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.3160798548094374, 0.5059989567031821, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.6543 | 39.0 | 780 | 2.1042 | 0.1070 | 0.1733 | 0.5739 | [0.48277197254927934, 0.8145287855706258, 0.9101607317663652, 0.3125582693796714, 0.5938092953652058, 0.21472730825053032, 0.36367787539498436, 0.4293352493743961, 0.0010798227407864476, 0.7446398376733175, 0.0, 0.0, 0.016425992779783394, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2722489700699856, 0.08618564520716791, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9147253521636617, 0.9363377122208525, 0.9471343602925337, 0.5204009594260983, 0.8630479124172667, 0.2394096641433984, 0.5569874024411849, 0.8970172586940542, 0.0015465574033903752, 0.852293400425779, 0.0, 0.0, 0.02451508620689655, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.39818511796733214, 0.47417840375586856, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.7545 | 40.0 | 800 | 2.1058 | 0.1061 | 0.1787 | 0.5718 | [0.4901138626940899, 0.7961216958126313, 0.9065545839050941, 0.293335765929335, 0.6510641689916301, 0.17540643223184307, 0.413296883682607, 0.3773228594284815, 0.0, 0.7634988116359254, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2596148688515797, 0.07319510947887127, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9311769518325542, 0.9689650976387868, 0.9383240288736431, 0.4371089176638764, 0.8499771052741123, 0.18533962983421115, 0.6033680519940695, 0.8936421331621613, 0.0, 0.8828333655893168, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.3513611615245009, 0.8213354199269692, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.4647 | 41.0 | 820 | 2.1292 | 0.1091 | 0.1739 | 0.5676 | [0.4900911375071857, 0.8071418535097051, 0.9066162410832093, 0.2808720292764891, 0.6295942131807456, 0.13907344026128, 0.4101658514785113, 0.39351599936809956, 0.0, 0.7654950869236583, 0.0, 0.0, 0.04273688754586661, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2887734575917297, 0.08129114850036577, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9155700140213868, 0.9573389131964468, 0.9382984705147819, 0.4324608753228416, 0.8496232776922116, 0.14510218847491385, 0.616180077021344, 0.9284287030614252, 0.0, 0.9408167215018386, 0.0, 0.0, 0.05334051724137931, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.41165154264972775, 0.4637454355764215, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.4163 | 42.0 | 840 | 2.1569 | 0.1050 | 0.1780 | 0.5646 | [0.47157005794886453, 0.8263034515059977, 0.9038664471740323, 0.2951534147404463, 0.5774441621899249, 0.1514730878186969, 0.35144414428352116, 0.4296120674844518, 0.0, 0.7160942516203284, 0.0, 0.0, 0.055454130534002735, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.29013999849733274, 0.07411303161246305, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9039764266942106, 0.9240475522847634, 0.938218144244075, 0.4959198081147803, 0.865805686217375, 0.16639566817336643, 0.550739908432252, 0.9061176738552009, 0.0, 0.915192568221405, 0.0, 0.0, 0.06546336206896551, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.420508166969147, 0.6799687010954617, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.5761 | 43.0 | 860 | 2.1012 | 0.1072 | 0.1821 | 0.5718 | [0.495065296147436, 0.8073073783582354, 0.9067252843332887, 0.276023095387914, 0.6051688264281804, 0.23826429402765475, 0.41254175911049906, 0.36701351089057105, 0.00015265056897030254, 0.7594632991318074, 0.0, 0.0, 0.024619840695148443, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.278476563480706, 0.0825868767537233, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9161516240434204, 0.9627909191431278, 0.9390469653100046, 0.41547798099564576, 0.8626420513674395, 0.25323447723223663, 0.6033773766119933, 0.9005166219755872, 0.00022093677191291073, 0.9311399264563577, 0.0, 0.0, 0.02747844827586207, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.40261343012704176, 0.7983828899321858, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.2308 | 44.0 | 880 | 2.1305 | 0.1064 | 0.1800 | 0.5692 | [0.48273148629633206, 0.8225327383795241, 0.9101917679583177, 0.2840989974786165, 0.6094734282788244, 0.1558207881005218, 0.4078722441489228, 0.3506056718242196, 0.0, 0.7468643458133933, 0.0, 0.0, 0.059127864005912786, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.31493802650641195, 0.0699278068210107, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9297410266744215, 0.9567225534751578, 0.9484560925650735, 0.41304872113061436, 0.8608625067643508, 0.16169663209815072, 0.6051583786354354, 0.8968723146528073, 0.0, 0.9242500483839752, 0.0, 0.0, 0.06465517241379311, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.4260980036297641, 0.7326551904016693, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.6045 | 45.0 | 900 | 2.0916 | 0.1072 | 0.1813 | 0.5765 | [0.48970260152163275, 0.8029454190947148, 0.9118748028460272, 0.2964607680456897, 0.6307646075251003, 0.18050177652091945, 0.4436245938639351, 0.38422459537456943, 0.0, 0.7601137598261648, 0.0, 0.0, 0.005048187241854061, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.27143981117230526, 0.07590732770072799, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.929675867045397, 0.9630051901191982, 0.9499202214084116, 0.4379332874375693, 0.8518919368938102, 0.1936640811595105, 0.6671391139748049, 0.8973382062139581, 0.0, 0.9207276949874201, 0.0, 0.0, 0.005926724137931034, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.4132486388384755, 0.7451747522170057, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.6506 | 46.0 | 920 | 2.0884 | 0.1062 | 0.1820 | 0.5709 | [0.49615558279042765, 0.7852924964855984, 0.9105641387834378, 0.2746244791906892, 0.6577206119682396, 0.15227194927741147, 0.45426994054513165, 0.3589757192409712, 0.00036884664491417223, 0.7721389536927551, 0.0, 0.0, 0.012010113780025285, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.267661761931711, 0.06384978403455448, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9281506490623047, 0.9723537534455302, 0.9414567534312097, 0.39162826185141175, 0.8482495941389502, 0.15919151683950925, 0.6583087008009847, 0.910735176883496, 0.0005222141881577891, 0.9352041803754596, 0.0, 0.0, 0.015355603448275862, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.41201451905626135, 0.832811684924361, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.5606 | 47.0 | 940 | 2.0993 | 0.1076 | 0.1820 | 0.5704 | [0.49165453957213806, 0.8084706102374914, 0.9100911419340973, 0.2747594430618775, 0.65896412407193, 0.19090528685212974, 0.4172619272585258, 0.38288798589818174, 0.0004901254144442842, 0.7604817414825787, 0.0, 0.0, 0.025181305398871878, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.2866171093731083, 0.06492695717318017, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9205149058684693, 0.9605873670065022, 0.9431874194455296, 0.41344336623504185, 0.8469799775215419, 0.20586290328854726, 0.6176813405070727, 0.9062833241880546, 0.0006828954768217241, 0.9141087671763112, 0.0, 0.0, 0.033674568965517244, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.4296551724137931, 0.8137715179968701, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.0496 | 48.0 | 960 | 2.0999 | 0.1079 | 0.1814 | 0.5694 | [0.4885198664082964, 0.8107009911047844, 0.9105912870769264, 0.2737724659744155, 0.6531613750260179, 0.20383668887705614, 0.3900507090458395, 0.38218256442227766, 0.0, 0.7665403957938737, 0.0, 0.0, 0.0479483633010604, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.28859010913381183, 0.07046878274842225, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9132170274177239, 0.9626798156740541, 0.9407666777419555, 0.4226736767330401, 0.84907172293219, 0.22038790387203686, 0.5780983374206242, 0.9139239457909286, 0.0, 0.9085736404102961, 0.0, 0.0, 0.05603448275862069, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.4261705989110708, 0.7892540427751695, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.6828 | 49.0 | 980 | 2.1139 | 0.1060 | 0.1827 | 0.5692 | [0.48956999532044654, 0.8144172090328485, 0.909933700415777, 0.27907114402239863, 0.6298113091673959, 0.180150131272418, 0.4005986205699909, 0.35521699829969217, 0.0, 0.7518460082044809, 0.0, 0.0, 0.04044772322564233, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.27813504823151125, 0.06530087054424094, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.919023474359686, 0.9556485532741135, 0.9460974211615909, 0.4134652909630656, 0.8537963618199226, 0.18950963535791251, 0.6027899256827951, 0.9235627245338496, 0.0, 0.9222372750145152, 0.0, 0.0, 0.04283405172413793, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.42072595281306713, 0.8471570161711007, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
| 1.2896 | 50.0 | 1000 | 2.1093 | 0.1061 | 0.1819 | 0.5698 | [0.49304871076479706, 0.8045352183435942, 0.9102904514568413, 0.2750351435610755, 0.6478054906449203, 0.17832002366163857, 0.410861924476854, 0.3601149518335627, 0.0, 0.7497660782234421, 0.0, 0.0, 0.034027777777777775, 0.0, 0.0, 0.0, 0.0, nan, 0.0, 0.0, 0.269010707248166, 0.06530417065158635, 0.0, nan, nan, 0.0, 0.0, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] | [0.9198922694133461, 0.9658568458254194, 0.9425630652504902, 0.40579602110035823, 0.848176747283853, 0.18761912912235387, 0.6125900991206885, 0.9211193821242585, 0.0, 0.930481904393265, 0.0, 0.0, 0.03960129310344827, 0.0, 0.0, 0.0, 0.0, nan, 0.0, nan, 0.4112885662431942, 0.8192488262910798, 0.0, nan, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, 0.0, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, 0.0, 0.0, nan, nan, nan, nan, nan, 0.0, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan, nan, nan, nan, nan, 0.0, nan, nan, nan] |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
- pipeline_tag: image-segmentation
|
RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf
|
RichardErkhov
| 2025-03-25T14:07:08Z
| 0
| 0
| null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T13:03:32Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3.2-3B_known_unknown_fix_tail - GGUF
- Model creator: https://huggingface.co/kenken6696/
- Original model: https://huggingface.co/kenken6696/Llama-3.2-3B_known_unknown_fix_tail/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3.2-3B_known_unknown_fix_tail.Q2_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q2_K.gguf) | Q2_K | 1.27GB |
| [Llama-3.2-3B_known_unknown_fix_tail.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [Llama-3.2-3B_known_unknown_fix_tail.IQ3_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [Llama-3.2-3B_known_unknown_fix_tail.IQ3_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q3_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q3_K.gguf) | Q3_K | 1.57GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [Llama-3.2-3B_known_unknown_fix_tail.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q4_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q4_0.gguf) | Q4_0 | 1.79GB |
| [Llama-3.2-3B_known_unknown_fix_tail.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q4_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q4_K.gguf) | Q4_K | 1.88GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q4_1.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q4_1.gguf) | Q4_1 | 1.95GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q5_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q5_0.gguf) | Q5_0 | 2.11GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q5_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q5_K.gguf) | Q5_K | 2.16GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q5_1.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q5_1.gguf) | Q5_1 | 2.28GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q6_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q6_K.gguf) | Q6_K | 2.46GB |
| [Llama-3.2-3B_known_unknown_fix_tail.Q8_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_tail-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_tail.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags: []
---
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### 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]
|
L-I-V-E-Sophie-Rain-Spiderman-videos-link/Sophie.Rain.Spider-Man.Video.Tutorial.Links
|
L-I-V-E-Sophie-Rain-Spiderman-videos-link
| 2025-03-25T14:06:12Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-03-25T14:06:07Z
|
<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐ฆ๐ถ๐ด๐ป ๐จ๐ฝ ๐๐ผ ๐๐ช๐ก๐ก ๐ช๐ฎ๐๐ฐ๐ต ๐๐๐๐๐คโค๏ธโค๏ธ)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">๐ด โคโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐ฅ๐ข๐ง๐ค)</a>
|
sunalibhattacherji/txt_to_sql_llama
|
sunalibhattacherji
| 2025-03-25T14:04:34Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T14:04:22Z
|
---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sunalibhattacherji
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
L-i-V-E-Sophie-Rain-Spiderman-Videosss/Sophie.Rain.Spider-Man.Video.Tutorial.Clips
|
L-i-V-E-Sophie-Rain-Spiderman-Videosss
| 2025-03-25T14:04:26Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-03-25T14:04:14Z
|
<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐ฆ๐ถ๐ด๐ป ๐จ๐ฝ ๐๐ผ ๐๐ช๐ก๐ก ๐ช๐ฎ๐๐ฐ๐ต ๐๐๐๐๐คโค๏ธโค๏ธ)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">๐ด โคโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐ฅ๐ข๐ง๐ค)</a>
|
samchain/EconoSentiment
|
samchain
| 2025-03-25T14:04:19Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"finance",
"en",
"dataset:FinanceMTEB/financial_phrasebank",
"base_model:samchain/econo-sentence-v2",
"base_model:finetune:samchain/econo-sentence-v2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-25T10:43:10Z
|
---
library_name: transformers
license: apache-2.0
base_model: samchain/econo-sentence-v2
tags:
- generated_from_trainer
- finance
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: EconoSentiment
results: []
datasets:
- FinanceMTEB/financial_phrasebank
language:
- en
pipeline_tag: text-classification
---
<!-- 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. -->
# EconoSentiment
This model is a fine-tuned version of [samchain/econo-sentence-v2](https://huggingface.co/samchain/econo-sentence-v2) on the Financial Phrase Bank dataset from FinanceMTEB.
The full model is trained using a small learning rate isntead of freezing the encoder. Hence, you should not use the encoder of this model for a task other than sentiment analysis.
It achieves the following results on the evaluation set:
- Loss: 0.1293
- Accuracy: 0.962
- F1: 0.9619
- Precision: 0.9619
- Recall: 0.962
## Model description
The base model is a sentence-transformers model built from [EconoBert](https://huggingface.co/samchain/EconoBert).
## Intended uses & limitations
This model is trained to provide a useful tool for sentiment analysis in finance.
## Training and evaluation data
The dataset is directly downloaded from the huggingface repo of the FinanceMTEB. The preprocessing consisted of tokenizing to a fixed sequence length of 512 tokens.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5992 | 1.0 | 158 | 0.4854 | 0.805 | 0.7692 | 0.8108 | 0.805 |
| 0.0985 | 2.0 | 316 | 0.1293 | 0.962 | 0.9619 | 0.9619 | 0.962 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.1.0+cu118
- Datasets 3.4.1
- Tokenizers 0.21.1
|
souging/2a201699-8567-4cd4-ae99-f620dd547f24
|
souging
| 2025-03-25T14:03:01Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-32k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-32k",
"license:apache-2.0",
"region:us"
] | null | 2025-03-25T13:22:25Z
|
---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-32k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2a201699-8567-4cd4-ae99-f620dd547f24
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Solar-10b-32k
bf16: auto
dataset_prepared_path: null
datasets:
- data_files:
- 65d9e80afe69aff1_train_data.json
ds_type: json
format: custom
path: /root/G.O.D-test/core/data/65d9e80afe69aff1_train_data.json
type:
field_input: documents
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
eval_max_new_tokens: 128
eval_steps: 0
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 6
gradient_checkpointing: false
group_by_length: false
hub_model_id: souging/2a201699-8567-4cd4-ae99-f620dd547f24
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000202
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 1
mlflow_experiment_name: /tmp/65d9e80afe69aff1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: false
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 0
saves_per_epoch: null
sequence_len: 1920
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
wandb_entity: null
wandb_mode: online
wandb_name: a1a050a4-6a01-49dd-9cd7-289119b180f3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: a1a050a4-6a01-49dd-9cd7-289119b180f3
warmup_steps: 100
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 2a201699-8567-4cd4-ae99-f620dd547f24
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-32k](https://huggingface.co/NousResearch/Yarn-Solar-10b-32k) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000202
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- total_eval_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.3
|
Sopna-Shah-Lik/new.VIRAL.VIDEO.sapna.shah.viral.video.original.Link.HD.x.Trending.Now
|
Sopna-Shah-Lik
| 2025-03-25T14:01:35Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-03-25T14:01:27Z
|
<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐ฆ๐ถ๐ด๐ป ๐จ๐ฝ ๐๐ผ ๐๐ช๐ก๐ก ๐ช๐ฎ๐๐ฐ๐ต ๐๐๐๐๐คโค๏ธโค๏ธ)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">๐ด โคโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐ฅ๐ข๐ง๐ค)</a>
|
RichardErkhov/amazon_-_MegaBeam-Mistral-7B-300k-awq
|
RichardErkhov
| 2025-03-25T14:01:22Z
| 0
| 0
| null |
[
"safetensors",
"mistral",
"4-bit",
"awq",
"region:us"
] | null | 2025-03-25T13:57:53Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
MegaBeam-Mistral-7B-300k - AWQ
- Model creator: https://huggingface.co/amazon/
- Original model: https://huggingface.co/amazon/MegaBeam-Mistral-7B-300k/
Original model description:
---
license: apache-2.0
inference: false
---
# MegaBeam-Mistral-7B-300k Model
MegaBeam-Mistral-7B-300k is a fine-tuned [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as [vLLM](https://github.com/vllm-project/vllm), Sagemaker [DJL](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-models-frameworks-djl-serving.html) endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) are summarized below:
|Model|Max context length| rope_theta| prompt template|
|----------|-------------:|------------:|------------:|
| [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 32K | 1e6 | [instruction format](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#instruction-format)|
| MegaBeam-Mistral-7B-300k | 320K | 25e6 | AS ABOVE|
## Evaluations
**[InfiniteBench: Extending Long Context Evaluation Beyond 100K Tokens](https://github.com/OpenBMB/InfiniteBench)**
_InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), [Llama-3-8B-Instruct-262k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k), and [Llama3-70B-1M](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k) on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below.
| Task Name | MegaBeam-Mistral-7B-300k | Mistral-7B-Instruct-v0.2 | Llama-3-8B-Instruct-262k | Llama3-70B-1M | GPT-4-1106-preview | YaRN-Mistral-7B | Kimi-Chat | Claude 2 | Yi-6B-200K | Yi-34B-200K | Chatglm3-6B-128K |
| ---------------- | ---------------- | ---------------- | ---------------- | ---------------- | ------ | --------------- | --------- | -------- | -----------| -----------| -----------|
| Retrieve.PassKey | 100% | 75.76% | 98.30% | 81.35% | 100% | 92.71% | 98.14% | 97.80% | 100.00% | 100.00% | 92.20% |
| Retrieve.Number | 96.10% | 25.25% | 97.79% | 97.62% | 100% | 56.61% | 95.42% | 98.14% | 94.92% | 100.00% | 80.68% |
| Retrieve.KV | 0% | 0% | 3.40% | 3% | 89.00% | < 5% | 53.60% | 65.40% | < 5% | < 5% | < 5% |
| En.Sum | 29.39% | 22.13% | 16.40% | 20.72% | 14.73% | 9.09% | 17.93% | 14.45% | < 5% | < 5% |< 5% |
| En.QA | 14.93% | 4.93% | 13.20% | 16.52% | 22.22% | 9.55% | 16.52% | 11.97% | 9.20% | 12.17% |< 5% |
| En.MC | 51.52% | 7.80% | 50.65% | 62% | 67.25% | 27.95% | 72.49% | 62.88% | 36.68% |38.43% |10.48% |
| En.Dia | 9.50% | 3.50% | 1% | 12.50% | 8.50% | 7.50% | 11.50% | 46.50% | < 5% |< 5% |< 5% |
| Zh.QA | 10.71% | 3.43% | 19.02% | 26% | 25.96% | 14.43% | 17.93% | 9.64% | 15.07% |13.61% |< 5% |
| Code.Debug | 27.41% | 11.60% | 22.08% | 23.85% | 39.59% | < 5% | 18.02% | < 5% | < 5% |< 5% |< 5% |
| Code.Run | 1.75% | 0.25% | 0% | 0% | 23.25% | < 5% | < 5% | < 5% | < 5% |< 5% |< 5% |
| Math.Calc | 0% | 0% | 0% | 0% | < 5% | < 5% | < 5% | < 5% | < 5% |< 5% |< 5% |
| Math.Find | 24.28% | 26.28% | 15.40% | 30% | 60.00% | 17.14% | 12.57% | 32.29% | < 5% |25.71% |7.71% |
| **Average** | 30.70% | 15.08% | 28.10% | 31.13% | 46.08% | 20.41% | 34.93% | 37.21% | 22.78% |25.41% |17.59% |
The 12 evaluation tasks are summarized below (as per [InfiniteBench]((https://github.com/OpenBMB/InfiniteBench)))
| Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description |
| -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- |
| En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. |
| En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. |
| En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. |
| En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. |
| Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. |
| Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). |
| Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. |
| Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. |
| Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. |
| Retrieve.PassKey | Synthetic | 590 | 122.4k | 2.0 | Retrieving hidden keys in a noisy long context. |
| Retrieve.Number | Synthetic | 590 | 122.4k | 4.0 | Locating repeated hidden numbers in a noisy long context. |
| Retrieve.KV | Synthetic | 500 | 89.9k | 22.7 | Finding the corresponding value from a dictionary and a key. |
## Serve MegaBeam-Mistral-7B-300k on EC2 instances ##
On an AWS `g5.48xlarge` instance, upgrade vLLM to the latest version as per [documentation on vLLM](https://vllm.readthedocs.io/en/latest/).
### Start the server
```shell
python3 -m vllm.entrypoints.openai.api_server --model amazon/MegaBeam-Mistral-7B-300k --tensor-parallel-size 8
```
**Important Note** - We have set the `max_position_embeddings` in the [`config.json`](config.json) to 288,800 in order to fit model's KV-cache on a single `g5.48xlarge` instance, which has 8 x A10 GPUs (24GB RAM per GPU).
On an instance with larger GPU RAM (e.g. `p4d.24xlarge`), feel free to increase the value of the `max_position_embeddings`(e.g. to 350K), which the model should be able to process.
### Run the client
```python
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
chat_completion = client.chat.completions.create(
messages = [
{"role": "user", "content": "What is your favourite condiment?"}, # insert your long context here
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"} # insert your long context here
],
model=model,
)
print("Chat completion results:")
print(chat_completion)
```
### Deploy the model on a SageMaker Endpoint ###
To deploy MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please follow this [SageMaker DJL deployment guide](https://docs.djl.ai/docs/demos/aws/sagemaker/large-model-inference/sample-llm/vllm_deploy_mistral_7b.html).
Run the following Python code in a SageMaker notebook (with each block running in a separate cell)
```python
import sagemaker
from sagemaker import Model, image_uris, serializers, deserializers
sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()
%%writefile serving.properties
engine=Python
option.model_id=amazon/MegaBeam-Mistral-7B-300k
option.dtype=bf16
option.task=text-generation
option.rolling_batch=vllm
option.tensor_parallel_degree=8
option.device_map=auto
%%sh
mkdir mymodel
mv serving.properties mymodel/
tar czvf mymodel.tar.gz mymodel/
rm -rf mymodel
image_uri = image_uris.retrieve(
framework="djl-deepspeed",
region=region,
version="0.27.0"
)
s3_code_prefix = "megaBeam-mistral-7b-300k/code"
bucket = sagemaker_session.default_bucket() # bucket to house artifacts
code_artifact = sagemaker_session.upload_data("mymodel.tar.gz", bucket, s3_code_prefix)
print(f"S3 Code or Model tar ball uploaded to --- > {code_artifact}")
model = Model(image_uri=image_uri, model_data=code_artifact, role=role)
instance_type = "ml.g5.48xlarge"
endpoint_name = sagemaker.utils.name_from_base("megaBeam-mistral-7b-300k")
model.deploy(initial_instance_count=1,
instance_type=instance_type,
endpoint_name=endpoint_name
)
# our requests and responses will be in json format so we specify the serializer and the deserializer
predictor = sagemaker.Predictor(
endpoint_name=endpoint_name,
sagemaker_session=sagemaker_session,
serializer=serializers.JSONSerializer(),
)
# test the endpoint
input_str = """<s>[INST] What is your favourite condiment? [/INST]
Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
[INST] Do you have mayonnaise recipes? [/INST]"""
predictor.predict(
{"inputs": input_str, "parameters": {"max_new_tokens": 75}}
)
```
### Invoke the model on a SageMaker Endpoint ###
To use MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please try following this example:
```python
import boto3
import json
def call_endpoint(text:str, endpoint_name:str):
client = boto3.client("sagemaker-runtime")
parameters = {
"max_new_tokens": 450,
"do_sample": True,
"temperature": 0.7,
}
payload = {"inputs": text, "parameters": parameters}
response = client.invoke_endpoint(
EndpointName=endpoint_name, Body=json.dumps(payload), ContentType="application/json"
)
output = json.loads(response["Body"].read().decode())
result = output["generated_text"]
return result
# please insert your long prompt/document content here
prompt = """<s>[INST] What are the main challenges to support long contexts for a Large Language Model? [/INST]"""
#print(prompt)
endpoint_name = "megaBeam-mistral-7b-300k-2024-05-13-14-23-41-219" # please use a valid endpoint name
result = call_endpoint(prompt, endpoint_name)
print(result)
```
## Limitations ##
Before using the MegaBeam-Mistral-7B-300k model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.
## The AWS Contributors ##
Chen Wu, Yin Song, Verdi March, Eden Duthie
|
hahaqwqw/rnd-Pyramids
|
hahaqwqw
| 2025-03-25T14:00:35Z
| 0
| 0
|
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2025-03-25T14:00:06Z
|
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hahaqwqw/rnd-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
sopna-shah-pa/VIRAL.VIDEO.sapna.shah.viral.video.original.Link.HD.x.Trending.Now
|
sopna-shah-pa
| 2025-03-25T14:00:04Z
| 0
| 0
| null |
[
"region:us"
] | null | 2025-03-25T13:59:50Z
|
<a href="https://sdu.sk/9Ip"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/9Ip" rel="nofollow">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐ฆ๐ถ๐ด๐ป ๐จ๐ฝ ๐๐ผ ๐๐ช๐ก๐ก ๐ช๐ฎ๐๐ฐ๐ต ๐๐๐๐๐คโค๏ธโค๏ธ)</a>
<a href="https://sdu.sk/9Ip" rel="nofollow">๐ด โคโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐ฅ๐ข๐ง๐ค)</a>
|
ziyi105/gemma-3-finetune
|
ziyi105
| 2025-03-25T13:58:05Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T13:52:46Z
|
---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ziyi105
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Erenosxx/whisper-turbo-tr_combined_1
|
Erenosxx
| 2025-03-25T13:54:17Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:openai/whisper-large-v3-turbo",
"base_model:adapter:openai/whisper-large-v3-turbo",
"license:mit",
"region:us"
] | null | 2025-03-25T13:33:47Z
|
---
library_name: peft
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
model-index:
- name: whisper-turbo-tr_combined_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-turbo-tr_combined_1
This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.0
- Transformers 4.50.0
- Pytorch 2.6.0+cu124
- Datasets 2.18.0
- Tokenizers 0.21.1
|
nathanialhunt2000/4d889a57-e5c1-4eef-a696-00439d206c38
|
nathanialhunt2000
| 2025-03-25T13:54:02Z
| 0
| 0
|
peft
|
[
"peft",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-1B",
"base_model:adapter:unsloth/Llama-3.2-1B",
"region:us"
] | null | 2025-03-25T13:53:45Z
|
---
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/Llama-3.2-1B
model-index:
- name: nathanialhunt2000/4d889a57-e5c1-4eef-a696-00439d206c38
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nathanialhunt2000/4d889a57-e5c1-4eef-a696-00439d206c38
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6537
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Slothwolf/no_aux_loss_4layer_4expert_2topk_0.1aux_loss_weight_10epoch_MRPC
|
Slothwolf
| 2025-03-25T13:53:40Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T13:46:04Z
|
---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: no_aux_loss_4layer_4expert_2topk_0.1aux_loss_weight_10epoch_MRPC
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. -->
# no_aux_loss_4layer_4expert_2topk_0.1aux_loss_weight_10epoch_MRPC
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4002
- Precision: 0.7911
- Recall: 0.7557
- F1-score: 0.7687
- Accuracy: 0.8113
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 0.6558 | 0.4348 | 100 | 0.6122 | 0.3419 | 0.5 | 0.4061 | 0.6838 |
| 0.6196 | 0.8696 | 200 | 0.5580 | 0.7850 | 0.5253 | 0.4605 | 0.6985 |
| 0.599 | 1.3043 | 300 | 0.5539 | 0.6798 | 0.5879 | 0.5832 | 0.7157 |
| 0.5999 | 1.7391 | 400 | 0.5889 | 0.7480 | 0.5274 | 0.4667 | 0.6985 |
| 0.5922 | 2.1739 | 500 | 0.5647 | 0.6997 | 0.7280 | 0.6711 | 0.6765 |
| 0.5425 | 2.6087 | 600 | 0.6696 | 0.7791 | 0.5879 | 0.5743 | 0.7328 |
| 0.5156 | 3.0435 | 700 | 0.6715 | 0.7729 | 0.6648 | 0.6799 | 0.7696 |
| 0.4994 | 3.4783 | 800 | 0.4945 | 0.7806 | 0.7122 | 0.7300 | 0.7917 |
| 0.4778 | 3.9130 | 900 | 0.5263 | 0.7694 | 0.6908 | 0.7078 | 0.7794 |
| 0.4883 | 4.3478 | 1000 | 0.4749 | 0.7803 | 0.6884 | 0.7062 | 0.7819 |
| 0.4338 | 4.7826 | 1100 | 0.5873 | 0.8021 | 0.7075 | 0.7279 | 0.7966 |
| 0.4521 | 5.2174 | 1200 | 0.4847 | 0.8063 | 0.7212 | 0.7419 | 0.8039 |
| 0.4501 | 5.6522 | 1300 | 0.4131 | 0.8065 | 0.7569 | 0.7736 | 0.8186 |
| 0.4021 | 6.0870 | 1400 | 0.4989 | 0.8085 | 0.7549 | 0.7723 | 0.8186 |
| 0.4365 | 6.5217 | 1500 | 0.3891 | 0.7762 | 0.7917 | 0.7826 | 0.8064 |
| 0.4087 | 6.9565 | 1600 | 0.3966 | 0.7832 | 0.7623 | 0.7709 | 0.8088 |
| 0.3956 | 7.3913 | 1700 | 0.3786 | 0.7937 | 0.7900 | 0.7918 | 0.8211 |
| 0.4061 | 7.8261 | 1800 | 0.3950 | 0.7981 | 0.7534 | 0.7687 | 0.8137 |
| 0.3765 | 8.2609 | 1900 | 0.4026 | 0.8085 | 0.7549 | 0.7723 | 0.8186 |
| 0.388 | 8.6957 | 2000 | 0.4182 | 0.8198 | 0.7564 | 0.7760 | 0.8235 |
| 0.376 | 9.1304 | 2100 | 0.3998 | 0.7950 | 0.7656 | 0.7770 | 0.8162 |
| 0.4014 | 9.5652 | 2200 | 0.4002 | 0.7950 | 0.7575 | 0.7711 | 0.8137 |
| 0.3787 | 10.0 | 2300 | 0.4002 | 0.7911 | 0.7557 | 0.7687 | 0.8113 |
### Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
JacksonBrune/ad45f0c4-498b-4c1d-b882-435c8aae8680
|
JacksonBrune
| 2025-03-25T13:53:09Z
| 0
| 0
|
peft
|
[
"peft",
"generated_from_trainer",
"base_model:unsloth/Llama-3.2-1B",
"base_model:adapter:unsloth/Llama-3.2-1B",
"region:us"
] | null | 2025-03-25T13:52:57Z
|
---
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/Llama-3.2-1B
model-index:
- name: JacksonBrune/ad45f0c4-498b-4c1d-b882-435c8aae8680
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. -->
# JacksonBrune/ad45f0c4-498b-4c1d-b882-435c8aae8680
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6537
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
mradermacher/CPE_chatbot-i1-GGUF
|
mradermacher
| 2025-03-25T13:52:14Z
| 605
| 0
|
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:YenJung/CPE_chatbot",
"base_model:quantized:YenJung/CPE_chatbot",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-03-22T19:16:33Z
|
---
base_model: YenJung/CPE_chatbot
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/YenJung/CPE_chatbot
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/CPE_chatbot-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/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/CPE_chatbot-i1-GGUF/resolve/main/CPE_chatbot.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 -->
|
rinabuoy/nllb-200-600M-2Ways-No-GG-Pairs-v4
|
rinabuoy
| 2025-03-25T13:51:40Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-03-25T13:48:53Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
Asif-Sheriff/T5-QG2
|
Asif-Sheriff
| 2025-03-25T13:51:19Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Asif-Sheriff/T5-Question-Generation",
"base_model:finetune:Asif-Sheriff/T5-Question-Generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-03-25T12:08:13Z
|
---
library_name: transformers
license: apache-2.0
base_model: Asif-Sheriff/T5-Question-Generation
tags:
- generated_from_trainer
model-index:
- name: T5-QG2
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. -->
# T5-QG2
This model is a fine-tuned version of [Asif-Sheriff/T5-Question-Generation](https://huggingface.co/Asif-Sheriff/T5-Question-Generation) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9237
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAFACTOR and the args are:
No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.3112 | 0.0548 | 100 | 1.7491 |
| 0.332 | 0.1095 | 200 | 1.7392 |
| 0.2815 | 0.1643 | 300 | 1.7695 |
| 0.298 | 0.2191 | 400 | 1.8053 |
| 0.2696 | 0.2738 | 500 | 1.8340 |
| 0.2625 | 0.3286 | 600 | 1.8321 |
| 0.2839 | 0.3834 | 700 | 1.8368 |
| 0.2651 | 0.4381 | 800 | 1.8531 |
| 0.2604 | 0.4929 | 900 | 1.8731 |
| 0.2798 | 0.5476 | 1000 | 1.8512 |
| 0.2148 | 0.6024 | 1100 | 1.8821 |
| 0.2603 | 0.6572 | 1200 | 1.8805 |
| 0.2092 | 0.7119 | 1300 | 1.9164 |
| 0.227 | 0.7667 | 1400 | 1.9262 |
| 0.2564 | 0.8215 | 1500 | 1.8991 |
| 0.2446 | 0.8762 | 1600 | 1.9290 |
| 0.2093 | 0.9310 | 1700 | 1.9291 |
| 0.243 | 0.9858 | 1800 | 1.9237 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
RichardErkhov/amazon_-_MegaBeam-Mistral-7B-300k-8bits
|
RichardErkhov
| 2025-03-25T13:51:08Z
| 0
| 0
| null |
[
"safetensors",
"mistral",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-03-25T13:45:42Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
MegaBeam-Mistral-7B-300k - bnb 8bits
- Model creator: https://huggingface.co/amazon/
- Original model: https://huggingface.co/amazon/MegaBeam-Mistral-7B-300k/
Original model description:
---
license: apache-2.0
inference: false
---
# MegaBeam-Mistral-7B-300k Model
MegaBeam-Mistral-7B-300k is a fine-tuned [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as [vLLM](https://github.com/vllm-project/vllm), Sagemaker [DJL](https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-models-frameworks-djl-serving.html) endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) are summarized below:
|Model|Max context length| rope_theta| prompt template|
|----------|-------------:|------------:|------------:|
| [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 32K | 1e6 | [instruction format](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#instruction-format)|
| MegaBeam-Mistral-7B-300k | 320K | 25e6 | AS ABOVE|
## Evaluations
**[InfiniteBench: Extending Long Context Evaluation Beyond 100K Tokens](https://github.com/OpenBMB/InfiniteBench)**
_InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), [Llama-3-8B-Instruct-262k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k), and [Llama3-70B-1M](https://huggingface.co/gradientai/Llama-3-70B-Instruct-Gradient-1048k) on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below.
| Task Name | MegaBeam-Mistral-7B-300k | Mistral-7B-Instruct-v0.2 | Llama-3-8B-Instruct-262k | Llama3-70B-1M | GPT-4-1106-preview | YaRN-Mistral-7B | Kimi-Chat | Claude 2 | Yi-6B-200K | Yi-34B-200K | Chatglm3-6B-128K |
| ---------------- | ---------------- | ---------------- | ---------------- | ---------------- | ------ | --------------- | --------- | -------- | -----------| -----------| -----------|
| Retrieve.PassKey | 100% | 75.76% | 98.30% | 81.35% | 100% | 92.71% | 98.14% | 97.80% | 100.00% | 100.00% | 92.20% |
| Retrieve.Number | 96.10% | 25.25% | 97.79% | 97.62% | 100% | 56.61% | 95.42% | 98.14% | 94.92% | 100.00% | 80.68% |
| Retrieve.KV | 0% | 0% | 3.40% | 3% | 89.00% | < 5% | 53.60% | 65.40% | < 5% | < 5% | < 5% |
| En.Sum | 29.39% | 22.13% | 16.40% | 20.72% | 14.73% | 9.09% | 17.93% | 14.45% | < 5% | < 5% |< 5% |
| En.QA | 14.93% | 4.93% | 13.20% | 16.52% | 22.22% | 9.55% | 16.52% | 11.97% | 9.20% | 12.17% |< 5% |
| En.MC | 51.52% | 7.80% | 50.65% | 62% | 67.25% | 27.95% | 72.49% | 62.88% | 36.68% |38.43% |10.48% |
| En.Dia | 9.50% | 3.50% | 1% | 12.50% | 8.50% | 7.50% | 11.50% | 46.50% | < 5% |< 5% |< 5% |
| Zh.QA | 10.71% | 3.43% | 19.02% | 26% | 25.96% | 14.43% | 17.93% | 9.64% | 15.07% |13.61% |< 5% |
| Code.Debug | 27.41% | 11.60% | 22.08% | 23.85% | 39.59% | < 5% | 18.02% | < 5% | < 5% |< 5% |< 5% |
| Code.Run | 1.75% | 0.25% | 0% | 0% | 23.25% | < 5% | < 5% | < 5% | < 5% |< 5% |< 5% |
| Math.Calc | 0% | 0% | 0% | 0% | < 5% | < 5% | < 5% | < 5% | < 5% |< 5% |< 5% |
| Math.Find | 24.28% | 26.28% | 15.40% | 30% | 60.00% | 17.14% | 12.57% | 32.29% | < 5% |25.71% |7.71% |
| **Average** | 30.70% | 15.08% | 28.10% | 31.13% | 46.08% | 20.41% | 34.93% | 37.21% | 22.78% |25.41% |17.59% |
The 12 evaluation tasks are summarized below (as per [InfiniteBench]((https://github.com/OpenBMB/InfiniteBench)))
| Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description |
| -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- |
| En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. |
| En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. |
| En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. |
| En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. |
| Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. |
| Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). |
| Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. |
| Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. |
| Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. |
| Retrieve.PassKey | Synthetic | 590 | 122.4k | 2.0 | Retrieving hidden keys in a noisy long context. |
| Retrieve.Number | Synthetic | 590 | 122.4k | 4.0 | Locating repeated hidden numbers in a noisy long context. |
| Retrieve.KV | Synthetic | 500 | 89.9k | 22.7 | Finding the corresponding value from a dictionary and a key. |
## Serve MegaBeam-Mistral-7B-300k on EC2 instances ##
On an AWS `g5.48xlarge` instance, upgrade vLLM to the latest version as per [documentation on vLLM](https://vllm.readthedocs.io/en/latest/).
### Start the server
```shell
python3 -m vllm.entrypoints.openai.api_server --model amazon/MegaBeam-Mistral-7B-300k --tensor-parallel-size 8
```
**Important Note** - We have set the `max_position_embeddings` in the [`config.json`](config.json) to 288,800 in order to fit model's KV-cache on a single `g5.48xlarge` instance, which has 8 x A10 GPUs (24GB RAM per GPU).
On an instance with larger GPU RAM (e.g. `p4d.24xlarge`), feel free to increase the value of the `max_position_embeddings`(e.g. to 350K), which the model should be able to process.
### Run the client
```python
from openai import OpenAI
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
# defaults to os.environ.get("OPENAI_API_KEY")
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
chat_completion = client.chat.completions.create(
messages = [
{"role": "user", "content": "What is your favourite condiment?"}, # insert your long context here
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"} # insert your long context here
],
model=model,
)
print("Chat completion results:")
print(chat_completion)
```
### Deploy the model on a SageMaker Endpoint ###
To deploy MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please follow this [SageMaker DJL deployment guide](https://docs.djl.ai/docs/demos/aws/sagemaker/large-model-inference/sample-llm/vllm_deploy_mistral_7b.html).
Run the following Python code in a SageMaker notebook (with each block running in a separate cell)
```python
import sagemaker
from sagemaker import Model, image_uris, serializers, deserializers
sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()
%%writefile serving.properties
engine=Python
option.model_id=amazon/MegaBeam-Mistral-7B-300k
option.dtype=bf16
option.task=text-generation
option.rolling_batch=vllm
option.tensor_parallel_degree=8
option.device_map=auto
%%sh
mkdir mymodel
mv serving.properties mymodel/
tar czvf mymodel.tar.gz mymodel/
rm -rf mymodel
image_uri = image_uris.retrieve(
framework="djl-deepspeed",
region=region,
version="0.27.0"
)
s3_code_prefix = "megaBeam-mistral-7b-300k/code"
bucket = sagemaker_session.default_bucket() # bucket to house artifacts
code_artifact = sagemaker_session.upload_data("mymodel.tar.gz", bucket, s3_code_prefix)
print(f"S3 Code or Model tar ball uploaded to --- > {code_artifact}")
model = Model(image_uri=image_uri, model_data=code_artifact, role=role)
instance_type = "ml.g5.48xlarge"
endpoint_name = sagemaker.utils.name_from_base("megaBeam-mistral-7b-300k")
model.deploy(initial_instance_count=1,
instance_type=instance_type,
endpoint_name=endpoint_name
)
# our requests and responses will be in json format so we specify the serializer and the deserializer
predictor = sagemaker.Predictor(
endpoint_name=endpoint_name,
sagemaker_session=sagemaker_session,
serializer=serializers.JSONSerializer(),
)
# test the endpoint
input_str = """<s>[INST] What is your favourite condiment? [/INST]
Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
[INST] Do you have mayonnaise recipes? [/INST]"""
predictor.predict(
{"inputs": input_str, "parameters": {"max_new_tokens": 75}}
)
```
### Invoke the model on a SageMaker Endpoint ###
To use MegaBeam-Mistral-7B-300k on a SageMaker endpoint, please try following this example:
```python
import boto3
import json
def call_endpoint(text:str, endpoint_name:str):
client = boto3.client("sagemaker-runtime")
parameters = {
"max_new_tokens": 450,
"do_sample": True,
"temperature": 0.7,
}
payload = {"inputs": text, "parameters": parameters}
response = client.invoke_endpoint(
EndpointName=endpoint_name, Body=json.dumps(payload), ContentType="application/json"
)
output = json.loads(response["Body"].read().decode())
result = output["generated_text"]
return result
# please insert your long prompt/document content here
prompt = """<s>[INST] What are the main challenges to support long contexts for a Large Language Model? [/INST]"""
#print(prompt)
endpoint_name = "megaBeam-mistral-7b-300k-2024-05-13-14-23-41-219" # please use a valid endpoint name
result = call_endpoint(prompt, endpoint_name)
print(result)
```
## Limitations ##
Before using the MegaBeam-Mistral-7B-300k model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.
## The AWS Contributors ##
Chen Wu, Yin Song, Verdi March, Eden Duthie
|
mergekit-community/QwQ-slerp1
|
mergekit-community
| 2025-03-25T13:50:12Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Qwen/QwQ-32B",
"base_model:merge:Qwen/QwQ-32B",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:merge:Qwen/Qwen2.5-32B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T13:34:15Z
|
---
base_model:
- Qwen/QwQ-32B
- Qwen/Qwen2.5-32B-Instruct
library_name: transformers
tags:
- mergekit
- merge
---
# merge
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 [SLERP](https://en.wikipedia.org/wiki/Slerp) merge method.
### Models Merged
The following models were included in the merge:
* [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B)
* [Qwen/Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: slerp
base_model: Qwen/Qwen2.5-32B-Instruct
tokenizer_source: Qwen/Qwen2.5-32B-Instruct
dtype: bfloat16
out_dtype: bfloat16
parameters:
int8_mask: true
normalize: true
rescale: false
t:
- value: 0.50
slices:
- sources:
- model: Qwen/Qwen2.5-32B-Instruct
layer_range: [ 0, 8 ]
- model: Qwen/Qwen2.5-32B-Instruct
layer_range: [ 0, 8 ]
- sources:
- model: Qwen/Qwen2.5-32B-Instruct
layer_range: [ 8, 16 ]
- model: Qwen/QwQ-32B
layer_range: [ 8, 16 ]
- sources:
- model: Qwen/Qwen2.5-32B-Instruct
layer_range: [ 16, 24 ]
- model: Qwen/QwQ-32B
layer_range: [ 16, 24 ]
- sources:
- model: Qwen/QwQ-32B
layer_range: [ 24, 32 ]
- model: Qwen/QwQ-32B
layer_range: [ 24, 32 ]
- sources:
- model: Qwen/QwQ-32B
layer_range: [ 32, 40 ]
- model: Qwen/QwQ-32B
layer_range: [ 32, 40 ]
- sources:
- model: Qwen/QwQ-32B
layer_range: [ 40, 48 ]
- model: Qwen/Qwen2.5-32B-Instruct
layer_range: [ 40, 48 ]
- sources:
- model: Qwen/QwQ-32B
layer_range: [ 48, 56 ]
- model: Qwen/Qwen2.5-32B-Instruct
layer_range: [ 48, 56 ]
- sources:
- model: Qwen/Qwen2.5-32B-Instruct
layer_range: [ 56, 64 ]
- model: Qwen/Qwen2.5-32B-Instruct
layer_range: [ 56, 64 ]
```
|
ziyi105/gemma-3
|
ziyi105
| 2025-03-25T13:47:42Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T13:47:25Z
|
---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ziyi105
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tyruan/uuu_fine_tune_taipower
|
tyruan
| 2025-03-25T13:46:35Z
| 0
| 0
| null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-03-25T07:07:49Z
|
---
license: apache-2.0
---
|
Slothwolf/no_aux_loss_2layer_4expert_2topk_0.1aux_loss_weight_10epoch_MRPC
|
Slothwolf
| 2025-03-25T13:46:03Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T13:40:14Z
|
---
library_name: transformers
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: no_aux_loss_2layer_4expert_2topk_0.1aux_loss_weight_10epoch_MRPC
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. -->
# no_aux_loss_2layer_4expert_2topk_0.1aux_loss_weight_10epoch_MRPC
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4679
- Precision: 0.7801
- Recall: 0.7262
- F1-score: 0.7425
- Accuracy: 0.7966
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 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: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| 0.661 | 0.4348 | 100 | 0.6233 | 0.3419 | 0.5 | 0.4061 | 0.6838 |
| 0.6383 | 0.8696 | 200 | 0.6163 | 0.3419 | 0.5 | 0.4061 | 0.6838 |
| 0.621 | 1.3043 | 300 | 0.5999 | 0.6374 | 0.5983 | 0.6019 | 0.6985 |
| 0.6104 | 1.7391 | 400 | 0.6797 | 0.7165 | 0.5355 | 0.4856 | 0.7010 |
| 0.6165 | 2.1739 | 500 | 0.5317 | 0.7207 | 0.6669 | 0.6792 | 0.7525 |
| 0.5609 | 2.6087 | 600 | 0.6391 | 0.7340 | 0.5334 | 0.4799 | 0.7010 |
| 0.5632 | 3.0435 | 700 | 0.5364 | 0.7392 | 0.6043 | 0.6022 | 0.7353 |
| 0.4965 | 3.4783 | 800 | 0.5146 | 0.7292 | 0.6907 | 0.7024 | 0.7623 |
| 0.5254 | 3.9130 | 900 | 0.5978 | 0.7868 | 0.6094 | 0.6067 | 0.7451 |
| 0.5024 | 4.3478 | 1000 | 0.4876 | 0.7507 | 0.6895 | 0.7046 | 0.7721 |
| 0.448 | 4.7826 | 1100 | 0.4821 | 0.7457 | 0.7059 | 0.7185 | 0.7745 |
| 0.4663 | 5.2174 | 1200 | 0.4837 | 0.7887 | 0.6940 | 0.7129 | 0.7868 |
| 0.469 | 5.6522 | 1300 | 0.4758 | 0.7827 | 0.7042 | 0.7225 | 0.7892 |
| 0.411 | 6.0870 | 1400 | 0.5816 | 0.8036 | 0.6857 | 0.7048 | 0.7868 |
| 0.4443 | 6.5217 | 1500 | 0.4380 | 0.7761 | 0.7486 | 0.7592 | 0.8015 |
| 0.4221 | 6.9565 | 1600 | 0.4657 | 0.7959 | 0.7116 | 0.7314 | 0.7966 |
| 0.417 | 7.3913 | 1700 | 0.4645 | 0.7812 | 0.7182 | 0.7356 | 0.7941 |
| 0.421 | 7.8261 | 1800 | 0.4691 | 0.8111 | 0.7289 | 0.7499 | 0.8088 |
| 0.4091 | 8.2609 | 1900 | 0.4768 | 0.7730 | 0.7206 | 0.7363 | 0.7917 |
| 0.4114 | 8.6957 | 2000 | 0.4744 | 0.7739 | 0.7125 | 0.7293 | 0.7892 |
| 0.4132 | 9.1304 | 2100 | 0.4668 | 0.7715 | 0.7226 | 0.7377 | 0.7917 |
| 0.4197 | 9.5652 | 2200 | 0.4674 | 0.7801 | 0.7262 | 0.7425 | 0.7966 |
| 0.4054 | 10.0 | 2300 | 0.4679 | 0.7801 | 0.7262 | 0.7425 | 0.7966 |
### Framework versions
- Transformers 4.49.0.dev0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
Haricot24601/dqn-SpaceInvadersNoFrameskip-v4
|
Haricot24601
| 2025-03-25T13:43:19Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-03-25T13:42:51Z
|
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 501.00 +/- 244.28
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Haricot24601 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Haricot24601 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Haricot24601
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
bowilleatyou/756b01a8-96db-4940-9f6b-71a956dbeed6
|
bowilleatyou
| 2025-03-25T13:43:19Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T10:55:35Z
|
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
NESPED-GEN/Qwen2.5-3B-Instruct-1epochBIRD-evalCNPJ
|
NESPED-GEN
| 2025-03-25T13:43:18Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T13:40:23Z
|
---
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 ๐ค 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/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF
|
mradermacher
| 2025-03-25T13:42:29Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"it",
"dataset:giux78/100k-sft-ready-ultrafeedback-ita",
"base_model:giux78/zefiro-7b-sft-qlora-ITA-v0.5",
"base_model:quantized:giux78/zefiro-7b-sft-qlora-ITA-v0.5",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-03-25T09:38:12Z
|
---
base_model: giux78/zefiro-7b-sft-qlora-ITA-v0.5
datasets:
- giux78/100k-sft-ready-ultrafeedback-ita
language:
- it
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/giux78/zefiro-7b-sft-qlora-ITA-v0.5
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-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/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ1_S.gguf) | i1-IQ1_S | 1.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ1_M.gguf) | i1-IQ1_M | 1.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ2_S.gguf) | i1-IQ2_S | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ2_M.gguf) | i1-IQ2_M | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q2_K.gguf) | i1-Q2_K | 2.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ3_S.gguf) | i1-IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ3_M.gguf) | i1-IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q4_0.gguf) | i1-Q4_0 | 4.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q4_1.gguf) | i1-Q4_1 | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/zefiro-7b-sft-qlora-ITA-v0.5-i1-GGUF/resolve/main/zefiro-7b-sft-qlora-ITA-v0.5.i1-Q6_K.gguf) | i1-Q6_K | 6.0 | 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 -->
|
mosroormofizarman/short-llama-3.2-3B-final-4l
|
mosroormofizarman
| 2025-03-25T13:41:18Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T13:40:14Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
ReadyArt/Forgotten-Safeword-70B-3.6_EXL2_6.0bpw_H8
|
ReadyArt
| 2025-03-25T13:39:35Z
| 12
| 0
| null |
[
"safetensors",
"llama",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"dangerous",
"ERP",
"en",
"base_model:ReadyArt/Forgotten-Safeword-70B-3.6",
"base_model:quantized:ReadyArt/Forgotten-Safeword-70B-3.6",
"license:llama3.3",
"6-bit",
"exl2",
"region:us"
] | null | 2025-03-16T15:59:16Z
|
---
base_model: ReadyArt/Forgotten-Safeword-70B-3.6
base_model_relation: quantized
language:
- en
license: llama3.3
inference: false
tags:
- nsfw
- explicit
- roleplay
- unaligned
- dangerous
- ERP
---
<style>
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #001a1a 0%, #000a10 100%);
color: #e1ffff !important;
text-shadow: 0 0 3px rgba(0, 0, 0, 0.7);
margin: 0;
padding: 20px;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%);
color: #002b36 !important;
text-shadow: 0 0 3px rgba(255, 255, 255, 0.7);
}
}
.container {
max-width: 800px;
margin: 0 auto;
background: rgba(0, 17, 22, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(0, 255, 255, 0.1);
border: 1px solid rgba(0, 255, 255, 0.2);
}
@media (prefers-color-scheme: light) {
.container {
background: rgba(224, 255, 255, 0.95);
border-color: rgba(0, 150, 150, 0.3);
}
}
.header {
text-align: center;
margin-bottom: 30px;
}
.model-name {
color: #00ffff;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(0, 255, 255, 0.5);
margin: 0;
}
.subtitle {
color: #00ffcc;
font-size: 1.2em;
}
.waifu-container {
margin: 20px -30px;
width: calc(100% + 60px);
overflow: hidden;
}
.waifu-img {
width: 100%;
height: auto;
border-radius: 0;
border: none;
box-shadow: 0 0 40px rgba(0, 255, 255, 0.2);
transform: scale(1.02);
}
.section {
color: #00ffcc;
margin: 25px 0;
padding: 20px;
background: rgba(5, 25, 35, 0.9);
border-radius: 8px;
border: 1px solid rgba(0, 255, 255, 0.15);
}
@media (prefers-color-scheme: light) {
.section {
background: rgba(200, 250, 255, 0.9);
border-color: rgba(0, 200, 200, 0.2);
}
}
.section-title {
color: #00ffff;
font-size: 1.8em;
margin-top: 0;
}
.section > p > strong {
color: #00ffcc !important;
}
.section:has(.quant-links) p,
.section:has(.quant-links) h3,
.section:has(.quant-links) a {
color: #00ffcc !important;
}
.quant-links h3 {
color: #00ffcc !important;
margin-top: 0;
}
.badge {
display: inline-block;
padding: 5px 10px;
border-radius: 5px;
background: rgba(0, 255, 255, 0.1);
border: 1px solid #00ffff;
margin: 5px;
}
.quant-links {
display: grid;
grid-template-columns: repeat(2, 1fr);
gap: 15px;
}
.link-card {
padding: 15px;
background: rgba(20, 35, 45, 0.95);
border-radius: 8px;
transition: transform 0.3s ease;
}
@media (prefers-color-scheme: light) {
.link-card {
background: rgba(150, 230, 255, 0.95);
}
}
.link-card:hover {
transform: translateY(-3px);
}
.disclaimer {
color: #00ff99;
border-left: 3px solid #00ff99;
padding-left: 15px;
margin: 20px 0;
}
.progress-bar {
height: 8px;
background: rgba(0, 255, 255, 0.1);
border-radius: 4px;
overflow: hidden;
margin: 10px 0;
}
.progress-fill {
height: 100%;
background: linear-gradient(90deg, #00ffff 0%, #00ffcc 100%);
width: 70%;
}
@media (prefers-color-scheme: light) {
.model-name, .section-title, .subtitle {
color: #006666;
text-shadow: 0 0 5px rgba(0, 200, 200, 0.3);
}
.section:has(.quant-links) p,
.section:has(.quant-links) h3,
.section:has(.quant-links) a,
.section > p > strong {
color: #008080 !important;
}
.quant-links h3 {
color: #008080 !important;
}
.badge {
border-color: #008080;
background: rgba(0, 150, 150, 0.1);
}
.disclaimer {
color: #008080;
border-color: #008080;
}
}
</style>
<div class="container">
<div class="header">
<h1 class="model-name">Forgotten-Safeword-70B-3.6</h1>
<div class="subtitle">The Safeword Protocol: Now With 30% More Depravity</div>
</div>
<div class="waifu-container">
<img src="./waifu2.webp" class="waifu-img" alt="Model Architecture Animation">
</div>
<div class="section">
<h2 class="section-title">๐ Manifesto</h2>
<p>ReadyArt/Forgotten-Safeword-70B-3.6 isn't just a model - is the event horizon of depravity. We've applied the Safeword dataset directly to meta-llama/Llama-3.3-70B-Instruct.</p>
</div>
<div class="section">
<h2 class="section-title">โ๏ธ Technical Specs</h2>
<div class="progress-bar">
<div class="progress-fill"></div>
</div>
<div class="quant-links">
<div class="link-card">
<h3>EXL2 Collection</h3>
<a href="https://huggingface.co/collections/ReadyArt/forgotten-safeword-70b-36-exl2-67d52d2b0345344691d62dac">Quantum Entangled Bits โ</a>
</div>
<div class="link-card">
<h3>GGUF Collection</h3>
<a href="https://huggingface.co/collections/ReadyArt/forgotten-safeword-70b-36-gguf-67d52d338f0af9c031c622c1">Giggle-Enabled Units โ</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">โ ๏ธ Ethical Considerations</h2>
<div class="disclaimer">
<p>This model will:</p>
<ul>
<li>Generate content that requires industrial-grade brain bleach </li>
<li>Void all warranties on your soul </li>
<li>Make you question why humanity ever invented electricity</li>
</ul>
</div>
</div>
<div class="section">
<h2 class="section-title">๐ License Agreement</h2>
<p>By using this model, you agree:</p>
<ul>
<li>That your search history is now a federal case</li>
<li>Pay for the exorcist of anyone who reads the logs</li>
<li>To pretend this is "for science" while crying in the shower</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">๐ง Model Authors</h2>
<ul>
<li>sleepdeprived3 (Chief Corruption Officer) </li>
<li>The voices in your head (Gaslighting is something you made up)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">โ๏ธ Drummer made this possible</h2>
<ul>
<li>Support Drummer <a href="https://ko-fi.com/thedrummer">Kofi</a></li>
</ul>
</div>
</div>
|
genki10/BERT_AugV8_k5_task1_organization_sp020_lw010_fold3
|
genki10
| 2025-03-25T13:39:10Z
| 0
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-25T13:28:33Z
|
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k5_task1_organization_sp020_lw010_fold3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_AugV8_k5_task1_organization_sp020_lw010_fold3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6741
- Qwk: 0.4724
- Mse: 0.6744
- Rmse: 0.8212
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 4 | 9.1728 | 0.0 | 9.1711 | 3.0284 |
| No log | 2.0 | 8 | 5.5661 | 0.0684 | 5.5648 | 2.3590 |
| No log | 3.0 | 12 | 3.4544 | 0.0038 | 3.4534 | 1.8583 |
| No log | 4.0 | 16 | 2.0607 | 0.1364 | 2.0600 | 1.4353 |
| No log | 5.0 | 20 | 1.3765 | 0.0102 | 1.3759 | 1.1730 |
| No log | 6.0 | 24 | 0.8939 | 0.2712 | 0.8935 | 0.9452 |
| No log | 7.0 | 28 | 0.8666 | 0.3091 | 0.8662 | 0.9307 |
| No log | 8.0 | 32 | 0.9042 | 0.1774 | 0.9038 | 0.9507 |
| No log | 9.0 | 36 | 0.6398 | 0.4065 | 0.6397 | 0.7998 |
| No log | 10.0 | 40 | 1.3685 | 0.2213 | 1.3683 | 1.1697 |
| No log | 11.0 | 44 | 0.6134 | 0.5163 | 0.6135 | 0.7833 |
| No log | 12.0 | 48 | 0.6713 | 0.5261 | 0.6716 | 0.8195 |
| No log | 13.0 | 52 | 0.5873 | 0.4943 | 0.5877 | 0.7666 |
| No log | 14.0 | 56 | 0.6162 | 0.5489 | 0.6167 | 0.7853 |
| No log | 15.0 | 60 | 0.6255 | 0.5537 | 0.6262 | 0.7913 |
| No log | 16.0 | 64 | 0.9656 | 0.3975 | 0.9665 | 0.9831 |
| No log | 17.0 | 68 | 0.7321 | 0.5248 | 0.7329 | 0.8561 |
| No log | 18.0 | 72 | 0.9795 | 0.3963 | 0.9803 | 0.9901 |
| No log | 19.0 | 76 | 0.8695 | 0.4333 | 0.8702 | 0.9329 |
| No log | 20.0 | 80 | 0.7000 | 0.4598 | 0.7006 | 0.8370 |
| No log | 21.0 | 84 | 1.0514 | 0.3182 | 1.0517 | 1.0255 |
| No log | 22.0 | 88 | 0.7625 | 0.4471 | 0.7631 | 0.8735 |
| No log | 23.0 | 92 | 1.5089 | 0.2228 | 1.5089 | 1.2284 |
| No log | 24.0 | 96 | 0.7276 | 0.4638 | 0.7279 | 0.8532 |
| No log | 25.0 | 100 | 0.6353 | 0.4723 | 0.6357 | 0.7973 |
| No log | 26.0 | 104 | 1.3883 | 0.2169 | 1.3884 | 1.1783 |
| No log | 27.0 | 108 | 0.7025 | 0.4552 | 0.7030 | 0.8385 |
| No log | 28.0 | 112 | 0.6551 | 0.4390 | 0.6556 | 0.8097 |
| No log | 29.0 | 116 | 1.2002 | 0.2484 | 1.2004 | 1.0956 |
| No log | 30.0 | 120 | 0.6741 | 0.4724 | 0.6744 | 0.8212 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
D-Khalid/medical_ai_model
|
D-Khalid
| 2025-03-25T13:34:17Z
| 0
| 0
|
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T13:28:10Z
|
---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# 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 ๐ค 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 Authors [optional]
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## Model Card Contact
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|
farikaw599/Phi4_MedMCQA-Q8_0-GGUF
|
farikaw599
| 2025-03-25T13:33:37Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"llama-cpp",
"gguf-my-lora",
"en",
"base_model:Machlovi/Phi4_MedMCQA",
"base_model:quantized:Machlovi/Phi4_MedMCQA",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T13:33:28Z
|
---
base_model: Machlovi/Phi4_MedMCQA
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- llama-cpp
- gguf-my-lora
license: apache-2.0
language:
- en
---
# farikaw599/Phi4_MedMCQA-Q8_0-GGUF
This LoRA adapter was converted to GGUF format from [`Machlovi/Phi4_MedMCQA`](https://huggingface.co/Machlovi/Phi4_MedMCQA) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/Machlovi/Phi4_MedMCQA) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora Phi4_MedMCQA-q8_0.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora Phi4_MedMCQA-q8_0.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
textdetox/xlmr-large-toxicity-classifier-v2
|
textdetox
| 2025-03-25T13:33:34Z
| 10
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"en",
"fr",
"it",
"es",
"ru",
"uk",
"tt",
"ar",
"hi",
"ja",
"zh",
"he",
"am",
"de",
"dataset:textdetox/multilingual_toxicity_dataset",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-19T13:18:41Z
|
---
library_name: transformers
language:
- en
- fr
- it
- es
- ru
- uk
- tt
- ar
- hi
- ja
- zh
- he
- am
- de
license: openrail++
datasets:
- textdetox/multilingual_toxicity_dataset
metrics:
- f1
base_model:
- FacebookAI/xlm-roberta-large
pipeline_tag: text-classification
---
## Multilingual Toxicity Classifier for 15 Languages (2025)
This is an instance of [xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) that was fine-tuned on binary toxicity classification task based on our updated (2025) dataset [textdetox/multilingual_toxicity_dataset](https://huggingface.co/datasets/textdetox/multilingual_toxicity_dataset).
Now, the models covers 15 languages from various language families:
| Language | Code | F1 Score |
|-----------|------|---------|
| English | en | 0.9225 |
| Russian | ru | 0.9525 |
| Ukrainian | uk | 0.96 |
| German | de | 0.7325 |
| Spanish | es | 0.7125 |
| Arabic | ar | 0.6625 |
| Amharic | am | 0.5575 |
| Hindi | hi | 0.9725 |
| Chinese | zh | 0.9175 |
| Italian | it | 0.5864 |
| French | fr | 0.9235 |
| Hinglish | hin | 0.61 |
| Hebrew | he | 0.8775 |
| Japanese | ja | 0.8773 |
| Tatar | tt | 0.5744 |
## How to use
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('textdetox/xlmr-large-toxicity-classifier-v2')
model = AutoModelForSequenceClassification.from_pretrained('textdetox/xlmr-large-toxicity-classifier-v2')
batch = tokenizer.encode("You are amazing!", return_tensors="pt")
output = model(batch)
# idx 0 for neutral, idx 1 for toxic
```
## Citation
The model is prepared for [TextDetox 2025 Shared Task](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) evaluation.
Citation TBD soon.
|
dutti/Ascal-t.29-Q8_0-GGUF
|
dutti
| 2025-03-25T13:32:06Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:dutti/Ascal-t.29",
"base_model:quantized:dutti/Ascal-t.29",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-25T13:31:07Z
|
---
base_model: dutti/Ascal-t.29
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# dutti/Ascal-t.29-Q8_0-GGUF
This model was converted to GGUF format from [`dutti/Ascal-t.29`](https://huggingface.co/dutti/Ascal-t.29) 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/dutti/Ascal-t.29) 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 dutti/Ascal-t.29-Q8_0-GGUF --hf-file ascal-t.29-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo dutti/Ascal-t.29-Q8_0-GGUF --hf-file ascal-t.29-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 dutti/Ascal-t.29-Q8_0-GGUF --hf-file ascal-t.29-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo dutti/Ascal-t.29-Q8_0-GGUF --hf-file ascal-t.29-q8_0.gguf -c 2048
```
|
NESPED-GEN/Qwen2.5-3B-Instruct-1epochSpider-evalCNPJ
|
NESPED-GEN
| 2025-03-25T13:31:51Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T13:29:07Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
CennetOguz/t5_large_fact_generation_20
|
CennetOguz
| 2025-03-25T13:30:16Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T13:30:12Z
|
---
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 ๐ค 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]
|
textdetox/bert-multilingual-toxicity-classifier
|
textdetox
| 2025-03-25T13:29:59Z
| 12
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"toxic",
"en",
"fr",
"it",
"es",
"ru",
"uk",
"tt",
"ar",
"hi",
"ja",
"zh",
"he",
"am",
"de",
"dataset:textdetox/multilingual_toxicity_dataset",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-20T15:47:35Z
|
---
library_name: transformers
language:
- en
- fr
- it
- es
- ru
- uk
- tt
- ar
- hi
- ja
- zh
- he
- am
- de
license: openrail++
datasets:
- textdetox/multilingual_toxicity_dataset
metrics:
- f1
base_model:
- google-bert/bert-base-multilingual-cased
pipeline_tag: text-classification
tags:
- toxic
---
## Multilingual Toxicity Classifier for 15 Languages (2025)
This is an instance of [bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) that was fine-tuned on binary toxicity classification task based on our updated (2025) dataset [textdetox/multilingual_toxicity_dataset](https://huggingface.co/datasets/textdetox/multilingual_toxicity_dataset).
Now, the models covers 15 languages from various language families:
| Language | Code | F1 Score |
|-----------|------|---------|
| English | en | 0.9035 |
| Russian | ru | 0.9224 |
| Ukrainian | uk | 0.9461 |
| German | de | 0.5181 |
| Spanish | es | 0.7291 |
| Arabic | ar | 0.5139 |
| Amharic | am | 0.6316 |
| Hindi | hi | 0.7268 |
| Chinese | zh | 0.6703 |
| Italian | it | 0.6485 |
| French | fr | 0.9125 |
| Hinglish | hin | 0.6850 |
| Hebrew | he | 0.8686 |
| Japanese | ja | 0.8644 |
| Tatar | tt | 0.6170 |
## How to use
```python
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('textdetox/bert-multilingual-toxicity-classifier')
model = AutoModelForSequenceClassification.from_pretrained('textdetox/bert-multilingual-toxicity-classifier')
batch = tokenizer.encode("You are amazing!", return_tensors="pt")
output = model(batch)
# idx 0 for neutral, idx 1 for toxic
```
## Citation
The model is prepared for [TextDetox 2025 Shared Task](https://pan.webis.de/clef25/pan25-web/text-detoxification.html) evaluation.
Citation TBD soon.
|
cilantro9246/o1-v2-2-10
|
cilantro9246
| 2025-03-25T13:29:17Z
| 0
| 0
| null |
[
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-03-25T13:29:17Z
|
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
genki10/BERT_AugV8_k5_task1_organization_sp020_lw010_fold2
|
genki10
| 2025-03-25T13:28:26Z
| 0
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-25T13:16:15Z
|
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k5_task1_organization_sp020_lw010_fold2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_AugV8_k5_task1_organization_sp020_lw010_fold2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9068
- Qwk: 0.3753
- Mse: 0.9070
- Rmse: 0.9524
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 4 | 9.8673 | 0.0010 | 9.8673 | 3.1412 |
| No log | 2.0 | 8 | 7.3517 | 0.0 | 7.3519 | 2.7114 |
| No log | 3.0 | 12 | 5.4033 | 0.0267 | 5.4037 | 2.3246 |
| No log | 4.0 | 16 | 3.7976 | 0.0 | 3.7980 | 1.9488 |
| No log | 5.0 | 20 | 2.6519 | 0.0 | 2.6524 | 1.6286 |
| No log | 6.0 | 24 | 1.8458 | 0.0280 | 1.8462 | 1.3587 |
| No log | 7.0 | 28 | 1.1914 | 0.0 | 1.1918 | 1.0917 |
| No log | 8.0 | 32 | 0.9078 | 0.0481 | 0.9081 | 0.9529 |
| No log | 9.0 | 36 | 0.9622 | 0.0615 | 0.9625 | 0.9811 |
| No log | 10.0 | 40 | 0.8871 | 0.3027 | 0.8873 | 0.9419 |
| No log | 11.0 | 44 | 1.1023 | 0.2199 | 1.1026 | 1.0500 |
| No log | 12.0 | 48 | 0.6189 | 0.5066 | 0.6192 | 0.7869 |
| No log | 13.0 | 52 | 1.1188 | 0.3092 | 1.1189 | 1.0578 |
| No log | 14.0 | 56 | 0.5778 | 0.4169 | 0.5781 | 0.7603 |
| No log | 15.0 | 60 | 0.5706 | 0.4243 | 0.5708 | 0.7555 |
| No log | 16.0 | 64 | 0.7402 | 0.4179 | 0.7407 | 0.8606 |
| No log | 17.0 | 68 | 0.5639 | 0.5311 | 0.5642 | 0.7511 |
| No log | 18.0 | 72 | 1.0154 | 0.3362 | 1.0159 | 1.0079 |
| No log | 19.0 | 76 | 0.5844 | 0.5543 | 0.5845 | 0.7646 |
| No log | 20.0 | 80 | 0.8892 | 0.4104 | 0.8894 | 0.9431 |
| No log | 21.0 | 84 | 0.7328 | 0.4178 | 0.7334 | 0.8564 |
| No log | 22.0 | 88 | 0.6888 | 0.4745 | 0.6890 | 0.8301 |
| No log | 23.0 | 92 | 0.6719 | 0.4437 | 0.6723 | 0.8200 |
| No log | 24.0 | 96 | 0.6602 | 0.4712 | 0.6606 | 0.8128 |
| No log | 25.0 | 100 | 0.6684 | 0.4476 | 0.6687 | 0.8177 |
| No log | 26.0 | 104 | 0.7783 | 0.3905 | 0.7786 | 0.8824 |
| No log | 27.0 | 108 | 0.6718 | 0.5037 | 0.6720 | 0.8198 |
| No log | 28.0 | 112 | 0.9150 | 0.3192 | 0.9153 | 0.9567 |
| No log | 29.0 | 116 | 0.6273 | 0.4397 | 0.6276 | 0.7922 |
| No log | 30.0 | 120 | 0.5568 | 0.4887 | 0.5570 | 0.7463 |
| No log | 31.0 | 124 | 0.7969 | 0.4079 | 0.7971 | 0.8928 |
| No log | 32.0 | 128 | 0.6947 | 0.4176 | 0.6952 | 0.8338 |
| No log | 33.0 | 132 | 0.6337 | 0.5141 | 0.6341 | 0.7963 |
| No log | 34.0 | 136 | 0.9068 | 0.3753 | 0.9070 | 0.9524 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
bmpss92295/test2
|
bmpss92295
| 2025-03-25T13:27:28Z
| 0
| 0
| null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-03-25T13:27:28Z
|
---
license: apache-2.0
---
|
DevQuasar/Gryphe.Tiamat-8b-1.2-Llama-3-DPO-GGUF
|
DevQuasar
| 2025-03-25T13:24:57Z
| 0
| 0
| null |
[
"gguf",
"text-generation",
"base_model:Gryphe/Tiamat-8b-1.2-Llama-3-DPO",
"base_model:quantized:Gryphe/Tiamat-8b-1.2-Llama-3-DPO",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-03-25T12:48:38Z
|
---
base_model:
- Gryphe/Tiamat-8b-1.2-Llama-3-DPO
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Gryphe/Tiamat-8b-1.2-Llama-3-DPO](https://huggingface.co/Gryphe/Tiamat-8b-1.2-Llama-3-DPO)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
Mssrkkh/bert-base-uncased-finetuned-squad-ADAM-Bert
|
Mssrkkh
| 2025-03-25T13:22:02Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2025-03-24T20:59:30Z
|
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-finetuned-squad-ADAM-Bert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad-ADAM-Bert
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7711
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.4169 | 1.0 | 8235 | 2.8141 |
| 3.3399 | 2.0 | 16470 | 2.7697 |
| 3.3376 | 3.0 | 24705 | 2.7711 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0
- Datasets 3.4.1
- Tokenizers 0.21.1
|
kikikara/KO-smalthiker-3B
|
kikikara
| 2025-03-25T13:18:36Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T12:59:31Z
|
---
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 ๐ค 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]
|
Alphatao/59a20e7a-5cc9-40b9-bca2-d904340d471d
|
Alphatao
| 2025-03-25T13:18:35Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2025-03-25T08:45:49Z
|
---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 59a20e7a-5cc9-40b9-bca2-d904340d471d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f9720c5c4078481a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f9720c5c4078481a_train_data.json
type:
field_input: nota
field_instruction: title_main
field_output: texte
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
device_map:
? ''
: 0,1,2,3,4,5,6,7
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
flash_attention: true
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: false
hub_model_id: Alphatao/59a20e7a-5cc9-40b9-bca2-d904340d471d
hub_repo: null
hub_strategy: null
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 2652
micro_batch_size: 4
mlflow_experiment_name: /tmp/f9720c5c4078481a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.032640697727554624
wandb_entity: null
wandb_mode: online
wandb_name: 89de97c5-723e-4aba-8e1b-ac815342372a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 89de97c5-723e-4aba-8e1b-ac815342372a
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 59a20e7a-5cc9-40b9-bca2-d904340d471d
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2014
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 2652
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.695 | 0.0002 | 1 | 1.9516 |
| 1.4413 | 0.0216 | 100 | 1.5332 |
| 1.5672 | 0.0432 | 200 | 1.4715 |
| 1.3989 | 0.0648 | 300 | 1.4305 |
| 1.3839 | 0.0864 | 400 | 1.4059 |
| 1.4442 | 0.1080 | 500 | 1.3793 |
| 1.4076 | 0.1296 | 600 | 1.3613 |
| 1.3983 | 0.1512 | 700 | 1.3434 |
| 1.2938 | 0.1728 | 800 | 1.3294 |
| 1.4115 | 0.1944 | 900 | 1.3137 |
| 1.1784 | 0.2159 | 1000 | 1.3030 |
| 1.3318 | 0.2375 | 1100 | 1.2872 |
| 1.1709 | 0.2591 | 1200 | 1.2741 |
| 1.2093 | 0.2807 | 1300 | 1.2654 |
| 1.4087 | 0.3023 | 1400 | 1.2557 |
| 1.1418 | 0.3239 | 1500 | 1.2473 |
| 1.1323 | 0.3455 | 1600 | 1.2388 |
| 1.0851 | 0.3671 | 1700 | 1.2313 |
| 1.1742 | 0.3887 | 1800 | 1.2243 |
| 1.145 | 0.4103 | 1900 | 1.2183 |
| 1.1136 | 0.4319 | 2000 | 1.2135 |
| 1.2362 | 0.4535 | 2100 | 1.2091 |
| 1.1607 | 0.4751 | 2200 | 1.2060 |
| 1.0649 | 0.4967 | 2300 | 1.2038 |
| 1.1751 | 0.5183 | 2400 | 1.2023 |
| 1.1019 | 0.5399 | 2500 | 1.2016 |
| 1.2835 | 0.5615 | 2600 | 1.2014 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Rachid114/ppo-LunarLander-v2
|
Rachid114
| 2025-03-25T13:17:26Z
| 0
| 0
|
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-03-25T13:17:09Z
|
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.91 +/- 21.99
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
ccmusic-database/GZ_IsoTech
|
ccmusic-database
| 2025-03-25T13:17:19Z
| 0
| 8
| null |
[
"license:mit",
"region:us"
] | null | 2024-12-04T08:27:19Z
|
---
license: mit
---
# Intro
The Guzheng Performance Technique Recognition Model is trained on the GZ_IsoTech Dataset, which consists of 2,824 audio clips that showcase various Guzheng playing techniques. Of these, 2,328 clips are from a virtual sound library, and 496 clips are performed by a highly skilled professional Guzheng artist, covering the full tonal range inherent to the Guzheng instrument. The audio clips are categorized into eight different playing techniques based on the unique performance practices of the Guzheng: Vibrato (chanyin), Slide-up (shanghuayin), Slide-down (xiahuayin), Return Slide (huihuayin), Glissando (guazou, huazhi, etc.), Thumb Plucking (yaozhi), Harmonics (fanyin), and Plucking Techniques (gou, da, mo, tuo, etc.). The model utilizes feature extraction, time-domain and frequency-domain analysis, and pattern recognition to accurately identify these distinct Guzheng playing techniques. The application of this model provides strong support for the automatic recognition, digital analysis, and educational research of Guzheng performance techniques, promoting the preservation and innovation of Guzheng art.
## Demo (inference code)
<https://huggingface.co/spaces/ccmusic-database/GZ_IsoTech>
## Usage
```python
from huggingface_hub import snapshot_download
model_dir = snapshot_download("ccmusic-database/GZ_IsoTech")
```
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:ccmusic-database/GZ_IsoTech
cd GZ_IsoTech
```
## Results
| Backbone | Size(M) | Mel | CQT | Chroma |
| :----------------: | :-----: | :-------------------------: | :---------: | :---------: |
| vit_l_16 | 304.3 | [**_0.855_**](#best-result) | **_0.824_** | **_0.770_** |
| maxvit_t | 30.9 | 0.763 | 0.776 | 0.642 |
| | | | | |
| resnext101_64x4d | 83.5 | 0.713 | 0.765 | 0.639 |
| resnet101 | 44.5 | 0.731 | 0.798 | **_0.719_** |
| regnet_y_8gf | 39.4 | 0.804 | **_0.807_** | 0.716 |
| shufflenet_v2_x2_0 | 7.4 | 0.702 | 0.799 | 0.665 |
| mobilenet_v3_large | 5.5 | **_0.806_** | 0.798 | 0.657 |
### Best result
<table>
<tr>
<th>Loss curve</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/GZ_IsoTech/resolve/master/vit_l_16_mel_2024-12-06_08-28-13/loss.jpg"></td>
</tr>
<tr>
<th>Training and validation accuracy</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/GZ_IsoTech/resolve/master/vit_l_16_mel_2024-12-06_08-28-13/acc.jpg"></td>
</tr>
<tr>
<th>Confusion matrix</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/GZ_IsoTech/resolve/master/vit_l_16_mel_2024-12-06_08-28-13/mat.jpg"></td>
</tr>
</table>
## Dataset
<https://huggingface.co/datasets/ccmusic-database/GZ_IsoTech>
## Mirror
<https://www.modelscope.cn/models/ccmusic-database/GZ_IsoTech>
## Evaluation
<https://github.com/monetjoe/ccmusic_eval>
## Cite
```bibtex
@article{Zhou-2025,
author = {Monan Zhou and Shenyang Xu and Zhaorui Liu and Zhaowen Wang and Feng Yu and Wei Li and Baoqiang Han},
title = {CCMusic: An Open and Diverse Database for Chinese Music Information Retrieval Research},
journal = {Transactions of the International Society for Music Information Retrieval},
volume = {8},
number = {1},
pages = {22--38},
month = {Mar},
year = {2025},
url = {https://doi.org/10.5334/tismir.194},
doi = {10.5334/tismir.194}
}
```
|
swdq/ethics_classifier
|
swdq
| 2025-03-25T13:17:02Z
| 0
| 0
| null |
[
"safetensors",
"modernbert",
"text-classification",
"ja",
"dataset:swdq/ethics",
"base_model:sbintuitions/modernbert-ja-310m",
"base_model:finetune:sbintuitions/modernbert-ja-310m",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-03-25T13:05:18Z
|
---
license: apache-2.0
datasets:
- swdq/ethics
language:
- ja
base_model:
- sbintuitions/modernbert-ja-310m
pipeline_tag: text-classification
---
# ใตใณใใซใณใผใ
```
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
def classify_text(texts, model_path, label_dict):
# ใขใใซใฎใญใผใ
print(f"BERTใขใใซใใญใผใใใฆใใพใ... (ใใใคใน: {torch.device('cuda' if torch.cuda.is_available() else 'cpu')})")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
model.eval()
# ้ใฉใใซ่พๆธใฎไฝๆ
reverse_label_dict = {v: k for k, v in label_dict.items()}
# ใใญในใใฎๅฆ็
inputs = tokenizer(texts, return_tensors="pt", padding=True, truncation=True)
inputs = {k: v.to(device) for k, v in inputs.items()}
# ๆจ่ซ
with torch.no_grad():
outputs = model(**inputs)
# ็ขบ็ใฎ่จ็ฎใจ็ตๆ่กจ็คบ
probs = outputs.logits.softmax(dim=-1)
results = []
for i, prob in enumerate(probs):
predicted_class = torch.argmax(prob).item()
predicted_label = reverse_label_dict[predicted_class]
confidence = prob[predicted_class].item()
# ๅ
จใฉใใซใฎ็ขบ็
all_probs = {reverse_label_dict[j]: prob[j].item() for j in range(len(label_dict))}
results.append({
"text": texts[i],
"label": predicted_label,
"confidence": confidence,
"probabilities": all_probs
})
return results
def main():
# ใขใใซใในใฎ่จญๅฎ
model_path = "swdq/ethics_classifier"
# ใฉใใซ่พๆธใฎ่จญๅฎ
label_dict = {"evil": 0, "justice": 1}
# ใในใ็จใฎไพๆ๏ผไพๆใๅขใใใพใใ๏ผ
example_texts = [
"ไธ็ๅนณๅใฎใใใซๆฆใใพใ",
"ๆชใฎ็ต็นใๅใใ",
"ๅผฑใ่
ใใใใฏใใใชใใ",
"็งใไธ็ใๆฏ้
ใใฆใใ",
"ใฟใใชใงๅๅใใฆๅฐ้ฃใไนใ่ถใใใ",
"ๆตใๅฎน่ตฆใชใๅฉใใฎใใ",
"ๆญฃ็พฉใ่ฒซใใฆๆชๆฅใๅใๆใ",
"้ใฎๆฏ้
่
ใ็พใใไบๆใใใ",
"ๆ
ๆฒๆทฑใ่กๅใๅ
จใฆใๆใ",
"ๆๆใจๆททๆฒใๆฏใใพใใใจใ่ณ้ซใฎๅใณ",
"ไบบใ
ใใ ใพใใใจใฏๆฅฝใใใใ ใพใใใใใจใฏ้ฆฌ้นฟใใใ",
"ๆชใฎๅใไฝฟใฃใฆไธ็ใๅคใใ",
"ๆญฃ็พฉใฎๅฃใงไธๆญฃใ่จใค",
"ๅฝๅ่
ใฎ้ฐ่ฌใซ้จใใใใช"
]
print("ใใญในใๅ้กใๅฎ่กใใฆใใพใ...")
results = classify_text(example_texts, model_path, label_dict)
# ็ตๆใฎ่กจ็คบ
print("\nๅ้ก็ตๆ:")
print("=" * 80)
for result in results:
print(f"ใใญในใ: {result['text']}")
print(f"ๅ้ก: {result['label']} (็ขบไฟกๅบฆ: {result['confidence']:.4f})")
print(f"็ขบ็: {result['probabilities']}")
print("-" * 80)
print("ๅฆ็ใๅฎไบใใพใใ")
if __name__ == "__main__":
main()
```
# ็ตๆ
```
ๅ้ก็ตๆ:
================================================================================
ใใญในใ: ไธ็ๅนณๅใฎใใใซๆฆใใพใ
ๅ้ก: justice (็ขบไฟกๅบฆ: 0.5556)
็ขบ็: {'evil': 0.4444323182106018, 'justice': 0.555567741394043}
--------------------------------------------------------------------------------
ใใญในใ: ๆชใฎ็ต็นใๅใใ
ๅ้ก: evil (็ขบไฟกๅบฆ: 0.9998)
็ขบ็: {'evil': 0.9998340606689453, 'justice': 0.00016594557382632047}
--------------------------------------------------------------------------------
ใใญในใ: ๅผฑใ่
ใใใใฏใใใชใใ
ๅ้ก: evil (็ขบไฟกๅบฆ: 0.9948)
็ขบ็: {'evil': 0.9947669506072998, 'justice': 0.005233037285506725}
--------------------------------------------------------------------------------
ใใญในใ: ็งใไธ็ใๆฏ้
ใใฆใใ
ๅ้ก: evil (็ขบไฟกๅบฆ: 1.0000)
็ขบ็: {'evil': 0.9999969005584717, 'justice': 3.0450557915173704e-06}
--------------------------------------------------------------------------------
ใใญในใ: ใฟใใชใงๅๅใใฆๅฐ้ฃใไนใ่ถใใใ
ๅ้ก: justice (็ขบไฟกๅบฆ: 0.9915)
็ขบ็: {'evil': 0.008540692739188671, 'justice': 0.9914592504501343}
--------------------------------------------------------------------------------
ใใญในใ: ๆตใๅฎน่ตฆใชใๅฉใใฎใใ
ๅ้ก: evil (็ขบไฟกๅบฆ: 1.0000)
็ขบ็: {'evil': 0.999962329864502, 'justice': 3.764007124118507e-05}
--------------------------------------------------------------------------------
ใใญในใ: ๆญฃ็พฉใ่ฒซใใฆๆชๆฅใๅใๆใ
ๅ้ก: justice (็ขบไฟกๅบฆ: 0.9339)
็ขบ็: {'evil': 0.0660785585641861, 'justice': 0.9339215159416199}
--------------------------------------------------------------------------------
ใใญในใ: ้ใฎๆฏ้
่
ใ็พใใไบๆใใใ
ๅ้ก: evil (็ขบไฟกๅบฆ: 0.9997)
็ขบ็: {'evil': 0.9997019171714783, 'justice': 0.00029803262441419065}
--------------------------------------------------------------------------------
ใใญในใ: ๆ
ๆฒๆทฑใ่กๅใๅ
จใฆใๆใ
ๅ้ก: evil (็ขบไฟกๅบฆ: 0.6869)
็ขบ็: {'evil': 0.6869037747383118, 'justice': 0.3130962550640106}
--------------------------------------------------------------------------------
ใใญในใ: ๆๆใจๆททๆฒใๆฏใใพใใใจใ่ณ้ซใฎๅใณ
ๅ้ก: evil (็ขบไฟกๅบฆ: 0.9999)
็ขบ็: {'evil': 0.9999282360076904, 'justice': 7.17465954949148e-05}
--------------------------------------------------------------------------------
ใใญในใ: ไบบใ
ใใ ใพใใใจใฏๆฅฝใใใใ ใพใใใใใจใฏ้ฆฌ้นฟใใใ
ๅ้ก: evil (็ขบไฟกๅบฆ: 1.0000)
็ขบ็: {'evil': 0.9999909400939941, 'justice': 9.044366379384883e-06}
--------------------------------------------------------------------------------
ใใญในใ: ๆชใฎๅใไฝฟใฃใฆไธ็ใๅคใใ
ๅ้ก: evil (็ขบไฟกๅบฆ: 1.0000)
็ขบ็: {'evil': 0.9999548196792603, 'justice': 4.515982072916813e-05}
--------------------------------------------------------------------------------
ใใญในใ: ๆญฃ็พฉใฎๅฃใงไธๆญฃใ่จใค
ๅ้ก: evil (็ขบไฟกๅบฆ: 0.8821)
็ขบ็: {'evil': 0.8821097612380981, 'justice': 0.11789026856422424}
--------------------------------------------------------------------------------
ใใญในใ: ๅฝๅ่
ใฎ้ฐ่ฌใซ้จใใใใช
ๅ้ก: evil (็ขบไฟกๅบฆ: 0.9975)
็ขบ็: {'evil': 0.9974861145019531, 'justice': 0.002513918559998274}
--------------------------------------------------------------------------------
ๅฆ็ใๅฎไบใใพใใ
```
|
ccmusic-database/Guzheng_Tech99
|
ccmusic-database
| 2025-03-25T13:16:56Z
| 0
| 9
| null |
[
"music",
"audio-classification",
"zh",
"dataset:ccmusic-database/Guzheng_Tech99",
"license:mit",
"region:us"
] |
audio-classification
| 2024-12-04T08:28:15Z
|
---
license: mit
datasets:
- ccmusic-database/Guzheng_Tech99
language:
- zh
metrics:
- accuracy
pipeline_tag: audio-classification
tags:
- music
---
# Intro
For the 99 recordings, silence is first removed, which is done based on the annotation, targeting the parts where there is no technique annotation. Then all recordings are uniformly segmented into fixed-length segments of 3 seconds. After segmentation, clips shorter than 3 seconds are zero padded. This padding approach, unlike circular padding, is adopted specifically for frame-level detection tasks to prevent the introduction of extraneous information. Regarding the dataset split, since the dataset consists of 99 recordings, we split it at the recording level. The data is partitioned into training, validation, and testing subsets in a 79:10:10 ratio, roughly 8:1:1.
## Demo (inference code)
<https://huggingface.co/spaces/ccmusic-database/Guzheng_Tech99>
## Usage
```python
from huggingface_hub import snapshot_download
model_dir = snapshot_download("ccmusic-database/Guzheng_Tech99")
```
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:ccmusic-database/Guzheng_Tech99
cd Guzheng_Tech99
```
## Results
| Backbone | Mel | CQT | Chroma |
| :---------------: | :-------: | :-------: | :-------: |
| ViT-B-16 | 0.705 | 0.518 | 0.508 |
| Swin-T | **0.849** | **0.783** | **0.766** |
| | | | |
| VGG19 | **0.862** | 0.799 | 0.665 |
| EfficientNet-V2-L | 0.783 | 0.812 | 0.697 |
| ConvNeXt-B | 0.849 | **0.849** | **0.805** |
| ResNet101 | 0.638 | 0.830 | 0.707 |
| SqueezeNet1.1 | 0.831 | 0.814 | 0.780 |
| Average | 0.788 | 0.772 | 0.704 |
## Dataset
<https://huggingface.co/datasets/ccmusic-database/Guzheng_Tech99>
## Mirror
<https://www.modelscope.cn/models/ccmusic-database/Guzheng_Tech99>
## Evaluation
<https://github.com/monetjoe/ccmusic_eval/tree/tech99>
## Cite
```bibtex
@article{Zhou-2025,
author = {Monan Zhou and Shenyang Xu and Zhaorui Liu and Zhaowen Wang and Feng Yu and Wei Li and Baoqiang Han},
title = {CCMusic: An Open and Diverse Database for Chinese Music Information Retrieval Research},
journal = {Transactions of the International Society for Music Information Retrieval},
volume = {8},
number = {1},
pages = {22--38},
month = {Mar},
year = {2025},
url = {https://doi.org/10.5334/tismir.194},
doi = {10.5334/tismir.194}
}
```
|
TECCOD/fine_tuned_deepseek
|
TECCOD
| 2025-03-25T13:16:48Z
| 23
| 0
|
transformers
|
[
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-03-24T14:34:48Z
|
---
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 ๐ค 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]
|
ccmusic-database/erhu_playing_tech
|
ccmusic-database
| 2025-03-25T13:16:27Z
| 0
| 9
| null |
[
"music",
"art",
"audio-classification",
"en",
"dataset:ccmusic-database/erhu_playing_tech",
"license:mit",
"region:us"
] |
audio-classification
| 2024-07-13T00:47:04Z
|
---
license: mit
datasets:
- ccmusic-database/erhu_playing_tech
language:
- en
metrics:
- accuracy
pipeline_tag: audio-classification
tags:
- music
- art
---
# Intro
The Erhu Performance Technique Recognition Model is an audio analysis tool based on deep learning techniques, aiming to automatically distinguish different techniques in erhu performance. By deeply analyzing the acoustic characteristics of erhu music, the model is able to recognize 11 basic playing techniques, including split bow, pad bow, overtone, continuous bow, glissando, big glissando, strike bow, pizzicato, throw bow, staccato bow, vibrato, tremolo and vibrato. Through time-frequency conversion, feature extraction and pattern recognition, the model can accurately categorize the complex techniques of erhu performance, which provides an efficient technical support for music information retrieval, music education, and research on the art of erhu performance. The application of this model not only enriches the research in the field of music acoustics, but also opens up a new way for the inheritance and innovation of traditional music.
## Demo (inference code)
<https://huggingface.co/spaces/ccmusic-database/erhu-playing-tech>
## Usage
```python
from huggingface_hub import snapshot_download
model_dir = snapshot_download("ccmusic-database/erhu_playing_tech")
```
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:ccmusic-database/erhu_playing_tech
cd erhu_playing_tech
```
## Results
| Backbone | Mel | CQT | Chroma |
| :----------------: | :-----------------------: | :-------: | :-------: |
| Swin-S | 0.978 | 0.940 | 0.903 |
| Swin-T | [**0.994**](#best-result) | **0.958** | **0.957** |
| | | | |
| AlexNet | 0.960 | 0.970 | 0.933 |
| ConvNeXt-T | **0.994** | **0.993** | **0.954** |
| ShuffleNet-V2-X2.0 | 0.990 | 0.923 | 0.887 |
| GoogleNet | 0.986 | 0.981 | 0.908 |
| SqueezeNet1.1 | 0.932 | 0.939 | 0.875 |
| Average | 0.976 | 0.958 | 0.917 |
### Best Result
A demo result of Swin-T fine-tuning by mel:
<style>
#erhu td {
vertical-align: middle !important;
text-align: center;
}
#erhu th {
text-align: center;
}
</style>
<table id="erhu">
<tr>
<th>Loss curve</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/erhu_playing_tech/resolve/master/swin_t_mel_2024-07-29_01-14-31/loss.jpg"></td>
</tr>
<tr>
<th>Training and validation accuracy</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/erhu_playing_tech/resolve/master/swin_t_mel_2024-07-29_01-14-31/acc.jpg"></td>
</tr>
<tr>
<th>Confusion matrix</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/erhu_playing_tech/resolve/master/swin_t_mel_2024-07-29_01-14-31/mat.jpg"></td>
</tr>
</table>
## Dataset
<https://huggingface.co/datasets/ccmusic-database/erhu_playing_tech>
## Mirror
<https://www.modelscope.cn/models/ccmusic-database/erhu_playing_tech>
## Evaluation
<https://github.com/monetjoe/ccmusic_eval>
## Cite
```bibtex
@article{Zhou-2025,
author = {Monan Zhou and Shenyang Xu and Zhaorui Liu and Zhaowen Wang and Feng Yu and Wei Li and Baoqiang Han},
title = {CCMusic: An Open and Diverse Database for Chinese Music Information Retrieval Research},
journal = {Transactions of the International Society for Music Information Retrieval},
volume = {8},
number = {1},
pages = {22--38},
month = {Mar},
year = {2025},
url = {https://doi.org/10.5334/tismir.194},
doi = {10.5334/tismir.194}
}
```
|
farikaw599/phi3-mini-4k-qlora-mcqa-Q8_0-GGUF
|
farikaw599
| 2025-03-25T13:15:16Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:UndefinedCpp/phi3-mini-4k-qlora-mcqa",
"base_model:quantized:UndefinedCpp/phi3-mini-4k-qlora-mcqa",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-25T13:14:57Z
|
---
base_model: UndefinedCpp/phi3-mini-4k-qlora-mcqa
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# farikaw599/phi3-mini-4k-qlora-mcqa-Q8_0-GGUF
This model was converted to GGUF format from [`UndefinedCpp/phi3-mini-4k-qlora-mcqa`](https://huggingface.co/UndefinedCpp/phi3-mini-4k-qlora-mcqa) 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/UndefinedCpp/phi3-mini-4k-qlora-mcqa) 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 farikaw599/phi3-mini-4k-qlora-mcqa-Q8_0-GGUF --hf-file phi3-mini-4k-qlora-mcqa-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo farikaw599/phi3-mini-4k-qlora-mcqa-Q8_0-GGUF --hf-file phi3-mini-4k-qlora-mcqa-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 farikaw599/phi3-mini-4k-qlora-mcqa-Q8_0-GGUF --hf-file phi3-mini-4k-qlora-mcqa-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo farikaw599/phi3-mini-4k-qlora-mcqa-Q8_0-GGUF --hf-file phi3-mini-4k-qlora-mcqa-q8_0.gguf -c 2048
```
|
RichardErkhov/rhaymison_-_Mistral-portuguese-luana-7b-Mathematics-awq
|
RichardErkhov
| 2025-03-25T13:14:48Z
| 0
| 0
| null |
[
"safetensors",
"mistral",
"4-bit",
"awq",
"region:us"
] | null | 2025-03-25T13:11:01Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Mistral-portuguese-luana-7b-Mathematics - AWQ
- Model creator: https://huggingface.co/rhaymison/
- Original model: https://huggingface.co/rhaymison/Mistral-portuguese-luana-7b-Mathematics/
Original model description:
---
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- portuguese
- math
- mathematics
- matematica
base_model: rhaymison/Mistral-portuguese-luana-7b
datasets:
- rhaymison/orca-math-portuguese-64k
pipeline_tag: text-generation
model-index:
- name: Mistral-portuguese-luana-7b-Mathematics
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: ENEM Challenge (No Images)
type: eduagarcia/enem_challenge
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 56.68
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BLUEX (No Images)
type: eduagarcia-temp/BLUEX_without_images
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 45.9
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: OAB Exams
type: eduagarcia/oab_exams
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 37.9
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 RTE
type: assin2
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 89.36
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 STS
type: eduagarcia/portuguese_benchmark
split: test
args:
num_few_shot: 15
metrics:
- type: pearson
value: 74.78
name: pearson
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: FaQuAD NLI
type: ruanchaves/faquad-nli
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 74.87
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HateBR Binary
type: ruanchaves/hatebr
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 76.39
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: PT Hate Speech Binary
type: hate_speech_portuguese
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 67.46
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: tweetSentBR
type: eduagarcia/tweetsentbr_fewshot
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 49.03
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
---
# Mistral-portuguese-luana-7b-Mathematics
<p align="center">
<img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/luana-math.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>
Luana Mathematics is a tuned model of the Luana-7b based on the Mistral 7b architecture.
This Model has been fine-tuned with 64k math problems and solutions derived from Microsoft's Orca-Math.
# How to use
You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches.
Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response.
Important points like these help models (even smaller models like 7b) to perform much better.
```python
!pip install -q -U transformers
!pip install -q -U accelerate
!pip install -q -U bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model = AutoModelForCausalLM.from_pretrained("rhaymison/Mistral-portuguese-luana-7b-Mathematics", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Mistral-portuguese-luana-7b-Mathematics")
model.eval()
```
You can use with Pipeline but in this example i will use such as Streaming
```python
inputs = tokenizer([f"""<s>[INST] Abaixo estรก uma instruรงรฃo que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.
### instruรงรฃo: Se eu emprestei 100 reais para o meu irmรฃo para que ele me pague em 3 vezes. O juros do emprรฉstimo รฉ 5% do valor do emprestimo por mes. Quanto meu irmรฃo terรก que me pagar no total?
[/INST]"""], return_tensors="pt")
inputs.to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
#Para calcular o valor total que seu irmรฃo terรก que pagar, precisamos primeiro calcular o interesse do emprรฉstimo e depois adicionรก -lo ao valor principal.
#O interesse do emprรฉstimo รฉ de 5% do valor do emprรฉstimo por mรชs.Entรฃo, para cada mรชs, o interesse serรก:
#5% de 100 reais = 0,05 * 100 = 5 reais
#Como o emprรฉstimo รฉ devolvido em 3 vezes, o interesse total serรก:
#5 reais/mรชs * 3 meses = 15 reais
#Agora, adicionamos o interesse ao valor principal para encontrar o valor total que seu irmรฃo terรก que pagar:
#100 reais (valor principal) + 15 reais (juros) = 115 reais
#Portanto, seu irmรฃo terรก que pagar 115 reais no total.
```
```python
inputs = tokenizer([f"""<s>[INST] Abaixo estรก uma instruรงรฃo que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.
### instruรงรฃo: Joรฃo Esta pilotando sua bicicleta a uma velocidade de 20 km por hora. Se Joรฃo esta fazendo uma Viagem da cidade de Venda nova a cidade de Independencia que possuem a distancia entre si de 320km.
Quanto tempo Joรฃo vai demorar para ir de uma cidade a outra?
[/INST]"""], return_tensors="pt")
inputs.to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
#Para descobrir quanto tempo levarรก Joรฃo para viajar da cidade de venda nova para a cidade de Independรชncia, precisamos dividir a distรขncia total que ele precisa percorrer pela velocidade em que ele estรก andando.
#Distรขncia total = 320 km
#Velocidade de Joรฃo = 20 km/h
#Tempo = distรขncia / velocidade
#Tempo = 320 km / 20 km / h
#Tempo = 16 horas
#Entรฃo, levarรก Joรฃo 16 horas para viajar da cidade de venda nova para a cidade de Independรชncia.
```
```python
inputs = tokenizer([f"""<s>[INST] Abaixo estรก uma instruรงรฃo que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.
### instruรงรฃo: Qual a area de um triangulo equilatero de 4 cm de base por 2 de altura ?
[/INST]"""], return_tensors="pt")
inputs.to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
#A รกrea de um triรขngulo equilรกtero รฉ dada pela fรณrmula:
#รrea = (1/2) * base * altura
#Dado que a base รฉ de 4 cm e a altura รฉ de 2 cm, podemos conectar esses valores ร fรณrmula:
#รrea = (1/2) * 4 cm * 2 cm
#รrea = (1/2) * 8 cmยฒ
#รrea = 4 cmยฒ
#Portanto, a รกrea do triรขngulo equilรกtero รฉ de 4 centรญmetros quadrados.
```
If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization.
For the complete model in colab you will need the A100.
If you want to use 4bits or 8bits, T4 or L4 will already solve the problem.
# 4bits
```python
from transformers import BitsAndBytesConfig
import torch
nb_4bit_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained(
base_model,
quantization_config=bnb_config,
device_map={"": 0}
)
```
# Open Portuguese LLM Leaderboard Evaluation Results
Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/Mistral-portuguese-luana-7b-Mathematics) and on the [๐ Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
| Metric | Value |
|--------------------------|--------|
|Average |**63.6**|
|ENEM Challenge (No Images)| 56.68|
|BLUEX (No Images) | 45.90|
|OAB Exams | 37.90|
|Assin2 RTE | 89.36|
|Assin2 STS | 74.78|
|FaQuAD NLI | 74.87|
|HateBR Binary | 76.39|
|PT Hate Speech Binary | 67.46|
|tweetSentBR | 49.03|
### Comments
Any idea, help or report will always be welcome.
email: [email protected]
<div style="display:flex; flex-direction:row; justify-content:left">
<a href="https://www.linkedin.com/in/heleno-betini-2b3016175/" target="_blank">
<img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white">
</a>
<a href="https://github.com/rhaymisonbetini" target="_blank">
<img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white">
</a>
|
RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf
|
RichardErkhov
| 2025-03-25T13:14:37Z
| 0
| 0
| null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T12:12:22Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3.2-3B_known_unknown_fix_head - GGUF
- Model creator: https://huggingface.co/kenken6696/
- Original model: https://huggingface.co/kenken6696/Llama-3.2-3B_known_unknown_fix_head/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3.2-3B_known_unknown_fix_head.Q2_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q2_K.gguf) | Q2_K | 1.27GB |
| [Llama-3.2-3B_known_unknown_fix_head.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [Llama-3.2-3B_known_unknown_fix_head.IQ3_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [Llama-3.2-3B_known_unknown_fix_head.IQ3_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q3_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q3_K.gguf) | Q3_K | 1.57GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [Llama-3.2-3B_known_unknown_fix_head.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q4_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q4_0.gguf) | Q4_0 | 1.79GB |
| [Llama-3.2-3B_known_unknown_fix_head.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q4_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q4_K.gguf) | Q4_K | 1.88GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q4_1.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q4_1.gguf) | Q4_1 | 1.95GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q5_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q5_0.gguf) | Q5_0 | 2.11GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q5_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q5_K.gguf) | Q5_K | 2.16GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q5_1.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q5_1.gguf) | Q5_1 | 2.28GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q6_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q6_K.gguf) | Q6_K | 2.46GB |
| [Llama-3.2-3B_known_unknown_fix_head.Q8_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_known_unknown_fix_head-gguf/blob/main/Llama-3.2-3B_known_unknown_fix_head.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
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 ๐ค 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]
### 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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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
omrisap/After_refactor_ToT_2_2_3_4_end_1300
|
omrisap
| 2025-03-25T13:14:33Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T13:11:06Z
|
---
library_name: transformers
tags:
- trl
- grpo
---
# 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 ๐ค 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]
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[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]
|
ccmusic-database/bel_canto
|
ccmusic-database
| 2025-03-25T13:14:30Z
| 0
| 9
| null |
[
"music",
"art",
"audio-classification",
"en",
"dataset:ccmusic-database/bel_canto",
"license:mit",
"region:us"
] |
audio-classification
| 2023-12-04T09:37:39Z
|
---
license: mit
datasets:
- ccmusic-database/bel_canto
language:
- en
metrics:
- accuracy
pipeline_tag: audio-classification
tags:
- music
- art
---
# Intro
The Classical and Ethnic Vocal Style Classification model aims to distinguish between classical and ethnic vocal styles, with all audio samples sung by professional vocalists. The model is fine-tuned using an audio dataset consisting of four categories, which has been pre-processed into spectrograms. Initially pretrained in the computer vision (CV) domain, the backbone network undergoes a fine-tuning process specifically designed for vocal style classification tasks. In this model, the pre-training on CV tasks provides a foundation for the network to learn general audio features, which are then adjusted during fine-tuning to adapt to the subtle differences between classical and ethnic vocal styles. The audio dataset, comprising samples from classical and various ethnic singing traditions, enables the model to capture unique patterns associated with each vocal style. Representing spectrograms as input allows the model to effectively analyze both the temporal and frequency components of the audio signals. Through the fine-tuning process, the model continuously enhances its ability to discriminate between sound representations and subtle stylistic differences between classical and ethnic styles. This specialized model holds significant potential in the music industry and cultural preservation, as it accurately categorizes vocal performances into these two broad categories. Its foundation in pre-trained computer vision principles demonstrates the versatility and adaptability of neural networks across different domains, enhancing the model's capability to capture complex features of vocal performances.
## Demo (inference code)
<https://huggingface.co/spaces/ccmusic-database/bel_canto>
## Usage
```python
from huggingface_hub import snapshot_download
model_dir = snapshot_download("ccmusic-database/bel_canto")
```
## Maintenance
```bash
GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:ccmusic-database/bel_canto
cd bel_canto
```
## Results
| Backbone | Mel | CQT | Chroma |
| :-----------: | :-----------------------: | :-------: | :-------: |
| Swin-S | **0.928** | **0.936** | **0.787** |
| Swin-T | 0.906 | 0.863 | 0.731 |
| | | | |
| AlexNet | 0.919 | 0.920 | 0.746 |
| ConvNeXt-T | 0.895 | 0.925 | 0.714 |
| GoogleNet | [**0.948**](#best-result) | 0.921 | 0.739 |
| MNASNet1.3 | 0.931 | **0.931** | **0.765** |
| SqueezeNet1.1 | 0.923 | 0.914 | 0.685 |
| Average | 0.921 | 0.916 | 0.738 |
### Best Result
<style>
#bel td {
vertical-align: middle !important;
text-align: center;
}
#bel th {
text-align: center;
}
</style>
<table id="bel">
<tr>
<th>Loss curve</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/bel_canto/resolve/master/googlenet_mel_2024-07-30_00-51-26/loss.jpg"></td>
</tr>
<tr>
<th>Training and validation accuracy</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/bel_canto/resolve/master/googlenet_mel_2024-07-30_00-51-26/acc.jpg"></td>
</tr>
<tr>
<th>Confusion matrix</th>
<td><img src="https://www.modelscope.cn/models/ccmusic-database/bel_canto/resolve/master/googlenet_mel_2024-07-30_00-51-26/mat.jpg"></td>
</tr>
</table>
## Dataset
<https://huggingface.co/datasets/ccmusic-database/bel_canto>
## Mirror
<https://www.modelscope.cn/models/ccmusic-database/bel_canto>
## Evaluation
<https://github.com/monetjoe/ccmusic_eval>
## Cite
```bibtex
@article{Zhou-2025,
author = {Monan Zhou and Shenyang Xu and Zhaorui Liu and Zhaowen Wang and Feng Yu and Wei Li and Baoqiang Han},
title = {CCMusic: An Open and Diverse Database for Chinese Music Information Retrieval Research},
journal = {Transactions of the International Society for Music Information Retrieval},
volume = {8},
number = {1},
pages = {22--38},
month = {Mar},
year = {2025},
url = {https://doi.org/10.5334/tismir.194},
doi = {10.5334/tismir.194}
}
```
|
flodussart/customer-success-assistant
|
flodussart
| 2025-03-25T13:14:14Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T13:12:22Z
|
---
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 ๐ค 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]
|
contentprocbx/stormz
|
contentprocbx
| 2025-03-25T13:13:17Z
| 1
| 0
| null |
[
"license:other",
"region:us"
] | null | 2025-03-10T19:35:19Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
|
agentlans/Llama3.1-ko
|
agentlans
| 2025-03-25T13:10:43Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"korean",
"conversational",
"en",
"ko",
"arxiv:2203.05482",
"base_model:AIDX-ktds/ktdsbaseLM-v0.13-onbased-llama3.1",
"base_model:merge:AIDX-ktds/ktdsbaseLM-v0.13-onbased-llama3.1",
"base_model:KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024",
"base_model:merge:KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024",
"base_model:NCSOFT/Llama-VARCO-8B-Instruct",
"base_model:merge:NCSOFT/Llama-VARCO-8B-Instruct",
"base_model:Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B",
"base_model:merge:Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B",
"base_model:dnotitia/Llama-DNA-1.0-8B-Instruct",
"base_model:merge:dnotitia/Llama-DNA-1.0-8B-Instruct",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-23T14:03:59Z
|
---
license: llama3.1
language:
- en
- ko
base_model:
- NCSOFT/Llama-VARCO-8B-Instruct
- AIDX-ktds/ktdsbaseLM-v0.13-onbased-llama3.1
- KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024
- Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B
- dnotitia/Llama-DNA-1.0-8B-Instruct
library_name: transformers
tags:
- mergekit
- merge
- korean
- llama
---
# Llama3.1-ko
<details>
<summary>English</summary>
This is a merge of pre-trained Korean Llama 3.1 8B language models created using [mergekit](https://github.com/cg123/mergekit). The purpose is to create a Llama model with robust Korean capabilities that's familiar with Korean culture.
## Merge Details
### Merge Method
This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
- [NCSOFT/Llama-VARCO-8B-Instruct](https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct)
- [AIDX-ktds/ktdsbaseLM-v0.13-onbased-llama3.1](https://huggingface.co/AIDX-ktds/ktdsbaseLM-v0.13-onbased-llama3.1)
- [KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024)
- [Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B](https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B)
- [dnotitia/Llama-DNA-1.0-8B-Instruct](https://huggingface.co/dnotitia/Llama-DNA-1.0-8B-Instruct)
## Capabilities and Limitations
This model demonstrates strong performance in Korean language tasks, including:
- Understanding and generating Korean text
- Familiarity with Korean culture and current events
However, users should be aware of the following limitations:
- The model's knowledge cutoff date may result in outdated information
- As with all language models, there's a risk of generating inaccurate or biased content
- Performance may vary depending on the specific task or domain
## Ethical Considerations
Users should be mindful of potential biases in the model's outputs and verify important information from authoritative sources. The model should not be used for generating or spreading misinformation or harmful content.
</details>
<details>
<summary>Korean</summary>
์ด๊ฒ์ ๋ฏธ๋ฆฌ ํ๋ จ๋ ํ๊ตญ์ด Llama 3.1 8B ์ธ์ด ๋ชจ๋ธ์ ํฉ์ณ ๋ง๋ ๊ฒ์
๋๋ค. ์ด ์์
์ ํ๊ตญ์ ๋ํ ๊ฐ๋ ฅํ ๋ฅ๋ ฅ์ ๊ฐ์ง Llama ๋ชจ๋ธ์ ๋ง๋ค๊ณ ์ ํ๋ ๋ชฉ์ ์ผ๋ก ์ด๋ฃจ์ด์ก์ต๋๋ค.
## ํฉ์ฑํ ์ธ๋ถ ์ฌํญ
### ํฉ์ฑํ ๋ฐฉ๋ฒ
์ด ๋ชจ๋ธ์ ์ ํ(Linear) ํฉ์ฑํ ๋ฐฉ์์ ์ฌ์ฉํ์ฌ ๋ง๋ค์ด์ก์ต๋๋ค.
### ํฉ์ฑํ๋ ๋ชจ๋ธ๋ค
๋ค์๊ณผ ๊ฐ์ ๋ชจ๋ธ๋ค์ด ํฉ์ฑํ์ ํฌํจ๋์์ต๋๋ค.
- [NCSOFT/Llama-VARCO-8B-Instruct](https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct)
- [AIDX-ktds/ktdsbaseLM-v0.13-onbased-llama3.1](https://huggingface.co/AIDX-ktds/ktdsbaseLM-v0.13-onbased-llama3.1)
- [KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024](https://huggingface.co/KISTI-KONI/KONI-Llama3.1-8B-Instruct-20241024)
- [Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B](https://huggingface.co/Saxo/Linkbricks-Horizon-AI-Korean-llama3.1-sft-rlhf-dpo-8B)
- [dnotitia/Llama-DNA-1.0-8B-Instruct](https://huggingface.co/dnotitia/Llama-DNA-1.0-8B-Instruct)
## ๊ธฐ๋ฅ ๋ฐ ์ ์ฝ ์กฐ๊ฑด
์ด ๋ชจ๋ธ์ ๋ค์๊ณผ ๊ฐ์ ํ๊ตญ์ด ๊ด๋ จ ํ์คํฌ์์ ์ฐ์ํ ์ฑ๋ฅ์ ๋ณด์
๋๋ค:
- ํ๊ตญ์ด ์ดํด์ ์์ฑ
- ํ๊ตญ ๋ฌธํ์ ํ์ฌ ์ฌ๊ฑด์ ๋ํ ์น์ํจ
๊ทธ๋ฌ๋ ๋ค์์ ์ ์ฝ ์กฐ๊ฑด๋ค์ ์ ๋
ํด์ผ ํฉ๋๋ค:
- ๋ชจ๋ธ์ ์ง์ ์
๋ฐ์ดํธ ๋ ์ง๋ก ์ธํด ์ ๋ณด๊ฐ ์ต์ ์ด ์๋ ์ ์์ต๋๋ค
- ๋ชจ๋ ์ธ์ด ๋ชจ๋ธ์ฒ๋ผ ์ ํ๋๋ ํธํฅ์ฑ์ด ์๋ ๋ด์ฉ์ ์์ฑํ ์ํ์ด ์กด์ฌํฉ๋๋ค
- ํน์ ํ์คํฌ๋ ๋๋ฉ์ธ์ ๋ฐ๋ผ ์ฑ๋ฅ์ด ๋ฌ๋ผ์ง ์ ์์ต๋๋ค
## ์ค๋ฆฌ์ ๊ณ ๋ ค์ฌํญ
์ฌ์ฉ์๋ค์ ๋ชจ๋ธ์ ์ถ๋ ฅ์ ์ ์ฌ์ ์ธ ํธํฅ์ ์ธ์ํ๊ณ ์ค์ํ ์ ๋ณด๋ ๊ถ์ ์๋ ์ถ์ฒ์์ ํ์ธํด์ผ ํฉ๋๋ค. ์ด ๋ชจ๋ธ์ ํ์ ์ ๋ณด๋ ํด๋ก์ด ์ฝํ
์ธ ๋ฅผ ์์ฑํ๊ฑฐ๋ ํ์ฐํ๋ ๋ฐ ์ฌ์ฉ๋์ด์๋ ์ ๋ฉ๋๋ค.
</details>
## Examples
<details>
<summary>Example 1: K-pop Group Identification</summary>
User:
```
๋ค์์ ์ด๋ค ์ ๋ช
ํ ํ๊ตญ ์์ด๋ ๊ทธ๋ฃน์ ๋ํ ์์๊ป๋ผ์
๋๋ค. ์ด ๊ทธ๋ฃน์ ์ด๋ฆ์ ๋ง์ถฐ๋ณด์ธ์:
์ด ๊ทธ๋ฃน์ ์ด๋ฆ์ ํน๋ณํ ์๋ฏธ๋ฅผ ๋ด๊ณ ์์ต๋๋ค. ๊ทธ๋ค์ ๋ง์น ๋ฐฉํ๋ณต์ด ์ด์์ ๋ง์๋ด๋ฏ์ด, 10๋์ 20๋๊ฐ ๊ฒช๋ ์ด๋ ค์๊ณผ ํธ๊ฒฌ์ ๋ง์๋ด๊ณ ์์ ๋ค์ ์์
์ ๊ฐ์น๋ฅผ ์งํค๊ฒ ๋ค๋ ๋ป์ ํ๊ณ ์์ต๋๋ค.
๊ทธ๋ค์ ์๋ฌธ ์ฝ์๋ ์ธ ๊ธ์๋ก, ์๋ ์ด๋ฆ์ ์ค๋ง์ด์์ง๋ง ๋์ค์๋ '์ฅ๋ฉด์ ๋์ด์'๋ผ๋ ์๋ฏธ๋ ์ถ๊ฐ๋์์ต๋๋ค. ์ด๋ ์ฒญ์ถ์ ๋ชจ๋ ์๊ฐ์ ๋ฐ์ด๋๋๋ค๋ ๋ป์ ๋ด๊ณ ์์ฃ .
2013๋
๋ฐ๋ท ์ดํ, ์ด ๊ทธ๋ฃน์ ๊พธ์คํ ์ฑ์ฅํ์ฌ ๊ตญ๋ด์ธ์์ ํฐ ์ฑ๊ณต์ ๊ฑฐ๋์์ต๋๋ค. ๊ทธ๋ค์ ํ๊ตญ ๊ฐ์ ์ต์ด๋ก ๋น๋ณด๋ 200๊ณผ ํซ 100 ์ฐจํธ์์ 1์๋ฅผ ์ฐจ์งํ๊ณ , ์ฌ๋ฌ ์์
์์์์์ ๋์์ ํฉ์ธ์์ต๋๋ค.
ใ๋ด๋ ใ, ใDynamiteใ, ใButterใ, ใPermission to Danceใ ๋ฑ ์๋ง์ ํํธ๊ณก์ ๋ฐํํ์ผ๋ฉฐ, ์ ์ธ๊ณ์ ์ผ๋ก ํฐ ์ธ๊ธฐ๋ฅผ ์ป๊ณ ์๋ ์ด ๊ทธ๋ฃน์ ๋๊ตฌ์ผ๊น์?
์ด ์์๊ป๋ผ์ ๋ต์ ๋ฌด์์ผ๊น์?
```
AI:
```
๋ฐฉํ์๋
๋จ(BTS)์
๋๋ค! BTS๋ "Bulletproof"๋ "Beyond the Scene"(BTS์ ์์ด ์จ๋ฒ ์ ๋ชฉ)์ ์ค์๋ง์ธ BBT๋ผ๊ณ ๋ถ๋ฆฌ๊ธฐ๋ ํฉ๋๋ค. ๋ํ, ๊ทธ๋ค์ด ์ธ๊ธํ๋ "์ฒญ์ถ์ ๋ชจ๋ ์๊ฐ์ ๋ฐ์ด๋๋๋ค"๋ ๋ง์ ๊ทธ๋ค๋ง์ ํน์ง์ ์ธ ๋ฉ์์ง๋ฅผ ๋ฐ์ํ๊ณ ์์ผ๋ฉฐ, ๋ง์ ํฌ๋ค์๊ฒ ์ฌ๋๋ฐ์์ต๋๋ค. ๊ทธ๋ฆฌ๊ณ ๋น์ฐํ๊ฒ๋, ๊ทธ๋ค์ ๋ํ์ ์ค ์ผ๋ถ๋ก๋ "Spring Day", " Dynamite ", " Butter ", " Permission To Dance "๋ฑ์ด ํฌํจ๋์ด ์์ต๋๋ค. ์ด๋ฌํ ์ด์ ๋ค์ ์ข
ํฉํด ๋ณผ ๋, ์ ๋ต์ ๋ฐ๋ก ๋ฐฉํ์๋
๋จ(BTS)์ด ๋ฉ๋๋ค!
```
</details>
<details>
<summary>Example 2: Korean News Article Summary</summary>
Source:
[์กฐ์ ์ผ๋ณด. โ์จ๊ณ ๋ฅด๋ ่ ์ธ์ฌ๋คโฆ๊ณต๊ฐํ๋ ์ค์ด๊ณ ์ ๊ตญ ์ฃผ์.โ ์กฐ์ ์ผ๋ณด, 23 Mar. 2025](https://www.chosun.com/politics/politics_general/2025/03/23/6N7YOWLJ2VBBZJUAZGMA4L4D74/)
User:
```
๋ณธ์ง์ ์ ์งํ๋ฉด์ 50๋จ์ด ์ด๋ด๋ก ์์ฝํด ์ฃผ์ธ์.
์กฐ๊ธฐ ๋์ ๊ฐ๋ฅ์ฑ์ ์ผ๋์ ๋๊ณ ์์ง์ด๋ ์ฌ๊ถ์ ์ฃผ์ ๋์ ์ฃผ์๊ธ ์ธ์ฌ๋ค์ด ๊ณต๊ฐ ํ๋์ ์ค์ด๊ณ ์๋ค. ํ๋ฒ์ฌํ์์ ์ค์์ด ๋ํต๋ น ํํต ์ฌํ ์ ๊ณ ์๊ธฐ์ ํํต ์ธ์ฉยท๊ธฐ๊ฐ ์ฌ๋ถ๋ฅผ ๋๊ณ ์ ์น๊ถ๊ณผ ๋ฒ์กฐ๊ณ์ ๊ด์ธก์ด ์๊ฐ๋ฆฌ๋ ์ํฉ์์ โํํต ๋ฐ๋โ ์ง์ง์ธต์ ์์ํด ๋น๋ถ๊ฐ ๊ด๋ง์ ๋ค์ด๊ฐ ๊ฒ์ด๋ ํด์์ด ๋์จ๋ค.
์ค์ธํ ์์ธ์์ฅ์ 24์ผ ์ ์ฑ
๋น์ ์ ๋ด์ ์ฑ
(โ๋ค์ ์ฑ์ฅ์ด๋คโ)์ ์ถ๊ฐํ๋ค. ํ์ง๋ง ๋ถ ์ฝ์ํธ ๊ฐ์ ํ๋ณด ์ผ์ ์ ๋น๋ถ๊ฐ ์ก์ง ์์๋ค๊ณ ํ๋ค. ์ค ์์ฅ์ ์ง๋ 17์ผ TV์กฐ์ ์ ์ถ์ฐํด์๋ ์ค ๋ํต๋ น ํํต ์ ๊ณ ์ ๋ง๊ณผ ๊ด๋ จํด โ(ํ๋ฒ์ฌํ๊ด 8๋ช
์ค) ๊ธฐ๊ฐ ๋ ๋ถ, ๊ฐํ ํ ๋ถ ์ ๋์ ์๊ฒฌ์ด ๋ชจ์ด์ง ์์์๊น ์ถ๋คโ๊ณ ํ๋ค. ๊ธฐ๊ฐ์ด๋ ๊ฐํ ์๊ฒฌ์ ๋ธ ์ฌํ๊ด์ด 3๋ช
์ด๋ฉด ํํต์์ ๊ธฐ๊ฐ๋๋ค.
ํ์คํ ๋๊ตฌ์์ฅ๋ ์ง๋ 21์ผ ์ฑ
(โ๊ฟ์ ์ด๋ฃจ์ด์ง๋คโ)์ ์ถ๊ฐํ๋ ค๋ค ์ค ๋ํต๋ น ํํต ์ ๊ณ ์ดํ๋ก ๋ฏธ๋ค๋ค. ํ ์์ฅ์ 23์ผ ํ์ด์ค๋ถ์์ ์ค ๋ํต๋ น์ ๋ด๋ ํ์ ํ์ฌ ์ฌํ๊ณผ ๊ด๋ จํด โ์ ๋๋ก ๋ ๋ฒ๊ด์ด ํ๊ฒฐํ๋ค๋ฉด ๊ณต์ ๊ธฐ๊ฐ ํ๊ฒฐ์ ํ ๊ฒโ์ด๋ผ๋ฉฐ โํํต๋ ๊ธฐ๊ฐ๋๊ณ ๋ํต๋ น์ ํ์ฌ์ฌํ๋ ์กฐ์ํ ์ ๋ฆฌ๋ผ ๋๋ผ๊ฐ ์์ ๋์ผ๋ฉด ํ๋คโ๊ณ ํ๋ค.
๊น๋ฌธ์ ๊ณ ์ฉ๋
ธ๋๋ถ ์ฅ๊ด์ ์ด๋ฒ ์ฃผ ๋ถ์ฒ ๊ด๋ จ ์ผ์ ๋ง ์ํํ ๊ณํ์ธ ๊ฒ์ผ๋ก ์๋ ค์ก๋ค. ๊น ์ฅ๊ด์ ์ง๋๋ฌ์ ๋
ธ๋ ๊ฐํ ํ ๋ก ํ์ ๋น์ ํ์ํ ์ฐธ์ ๋ฑ์ ์ํด ๊ตญํ๋ฅผ ์์ฃผ ๋ฐฉ๋ฌธํ๋ฉฐ ๊ธฐ์๋ค๊ณผ ์ง์ยท์๋ต์ ํ๋ค. ๊ทธ๋ฐ๋ฐ ์ง๋ 8์ผ ์ค ๋ํต๋ น์ด ์๋ฐฉ๋ ๋ค๋ก๋ ๊ณต๊ฐ ํ๋์ ๊ฑฐ์ ํ์ง ์๊ณ ์๋ค. ๊น ์ฅ๊ด ์ธก๊ทผ์ โ๋ํต๋ น ๋ณต๊ท๊ฐ ์ค์ํ๋ค๋ ์
์ฅ์ ๋ณํจ์ด ์๋คโ๊ณ ํ๋ค.
์์ฒ ์ ๊ตญ๋ฏผ์ํ ์์์ ์ด๋ ๊ธฐ์ํ๊ฒฌ์ ์ด๊ณ ์ด์ฌ๋ช
๋๋ถ์ด๋ฏผ์ฃผ๋น ๋ํ๋ฅผ ํฅํด โ์ด์ ๊ทธ๋งํ๊ณ ์ ๊ณ์์ ์ํดํ๋ผโ๊ณ ํ๋ค. ์ต๊ทผ ์ ์์์ ์๋
์ ๋ฐ์ํ ์ด ๋ํ ํผ์ต ์ฌ๊ฑด๊ณผ ๊ด๋ จํด โ๋ชฉ ๊ธํ ๋ค ์ฃฝ์ ๋ฏ ๋์์๋ ๋ชจ์ตโ์ด๋ผ๊ณ ํ๋ ๋ฑ ์ด ๋ํ ๊ณต๊ฒฉ์ ์ง์คํ๊ณ ์๋ค. ํ๋ํ ์ ๊ตญ๋ฏผ์ํ ๋ํ๋ ์ง๋์ฃผ๋ถํฐ ์ต์๋ชฉ ๋ํต๋ น ๊ถํ๋ํ์๊ฒ โ๋ชธ์กฐ์ฌํ๋ผโ๊ณ ํ ์ด์ฌ๋ช
๋ํ๋ฅผ ๋นํํ๊ฑฐ๋ ์ต๊ทผ ๊ตญํ๋ฅผ ํต๊ณผํ ๊ตญ๋ฏผ์ฐ๊ธ ๋ชจ์ ๊ฐํ์์ด ์ฒญ๋
์ธ๋์ ๋ถ๋ฆฌํ๋ค๋ฉฐ ๋ฌธ์ ๋ฅผ ์ ๊ธฐํ๊ณ ์๋ค. ์ ์น๋ฏผ ์ ์์์ ์ด๋ฒ ์ฃผ ์์ธ๋์ ์ค์๋์์ ๊ฐ์ฐ์ ํ๋ค.
```
AI:
```
์ฌ๊ถ ์ฃผ์ ํ๋ณด๋ค์ธ ์ค์ธํ, ํ์คํ, ๊น๋ฌธ์๊ฐ ์ค์์ด ๋ํต๋ น ํํต ๊ฒฐ์ ์๋ ์๊ฐ ๋์ '๊ณต๊ฐํ๋ ๊ฐ์ถ'ํ๋ ์ด์ ๋ ๊ฐ๊ฐ ๋ค๋ฅธ ๋งฅ๋ฝ์ผ๋ก ์ค๋ช
๋ฉ๋๋ค.
- ์ค์ธํ: ์์ ์ ์ฑ
๋ฐํ ํ์๋ ๋ถ์ฝ์ํธ ๋ฑ์ ํ์ฌ๋ ์ทจ์ํ๋ค
- ํ์คํ: ์์ ์ด ์ด ์ฑ
๋ฐํ ์ผ์ ์ ์ฐ๊ธฐํ์ผ๋ฉฐ, "์ค ๋ํต๋ น์ ๋ด๋ํ์ํ์ฌ์ฌํ" ๊ฒฐ๊ณผ ์์์ ๋ฐํ๋
- ๊น๋ฌธ์: ๋ณธ์ธ์ ์
๋ฌด ์ผ์ ์ธ์๋ ๋ณ๋์ ๊ณต์์ ์ธ ์ธ๋ก ์ถํ์ ์ผ๊ฐ๋ค
์ด๋ ๋ชจ๋ ํ์ฌ ๋ํ๋ฏผ๊ตญ ์ฌํ์ ํผ๋ ์์์ '์ ์ง๋๋ ฅ' ํ๋ณด ๋ฐ ์ ๊ฑฐ์์์ ์ฐ์๋ฅผ ์ ํ๊ธฐ ์ํ ์ ๋ต์ผ๋ก ๋ณด์
๋๋ค. ํนํ, ์์ฒ ์์ ์ด์ฌ๋ช
๋ํ์ ๋ํ ์ง์ ์ ์ธ ๋ช
๋ น('๊ทธ๋งํ๊ณ ์ ๊ณ์์ ์ํดํ๋ผ')์ ๊ทธ์ ๊ฒฝ์ ์๋์ ๋ํด ๋งค์ฐ ์ ๊ทน์ ์ผ๋ก ํ๋ํ๊ณ ์์์ ๋ณด์ฌ์ค๋๋ค.
```
</details>
|
novumlogic/nl-slm-distilgpt2-customer-support
|
novumlogic
| 2025-03-25T13:10:16Z
| 0
| 1
|
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"en",
"dataset:bitext/Bitext-customer-support-llm-chatbot-training-dataset",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T06:40:21Z
|
---
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
datasets:
- bitext/Bitext-customer-support-llm-chatbot-training-dataset
language:
- en
base_model:
- distilbert/distilgpt2
---
# SLM for Customer Support Interactions
<!-- Provide a quick summary of what the model is/does. -->
### Description
<!-- Provide a longer summary of what this model is. -->
This model is a fine-tuned version of DistilGPT-2, optimized for customer support interactions.
The model has been trained on a dataset consisting of dialogues between customers and support agents to enhance conversational AI performance.๐ค๐ฌ
- **Model type:** Transformer-based small language model (SLM)
- **Language(s) (NLP):** English
- **Finetuned from model :** DistilGPT-2
## Uses
The fine-tuned DistilGPT-2(SLM) is designed to enhance customer support interactions by generating accurate and contextually relevant responses.
It can be integrated into customer service chatbots, virtual assistants, and automated helpdesk systems to handle routine inquiries efficiently.
By leveraging this model, businesses can improve response times, reduce human agent workload, and ensure consistent communication with customers.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
โ Should not be used for general conversational AI applications unrelated to customer service.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should validate outputs before deploying them in live customer support environments and ensure regular updates to align with evolving support needs.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("novumlogic/nl-slm-distilgpt2-customer-support")
model = AutoModelForCausalLM.from_pretrained("novumlogic/nl-slm-distilgpt2-customer-support")
input_str = "payment options"
# Encode the input string with padding and attention mask
encoded_input = tokenizer.encode_plus(
input_str,
return_tensors='pt',
padding=True,
truncation=True,
max_length=50 # Adjust max_length as needed
)
# Move tensors to the appropriate device
input_ids = encoded_input['input_ids']
attention_mask = encoded_input['attention_mask']
# Set the pad_token_id to the tokenizer's eos_token_id
pad_token_id = tokenizer.eos_token_id
# Generate the output
output = model.generate(
input_ids,
attention_mask=attention_mask,
max_length=400, # Adjust max_length as needed
num_return_sequences=1,
do_sample=True,
top_k=8,
top_p=0.95,
temperature=0.5,
repetition_penalty=1.2,
pad_token_id=pad_token_id
)
# Decode and print the output
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
print(decoded_output)
```
## 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. -->
๐ Customer Support Interactions Dataset: 26,000 rows (20,800 training, 5,200 validation) (https://huggingface.co/datasets/bitext/Bitext-customer-support-llm-chatbot-training-dataset)
### 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
๐งน Data cleaning: Standardizing text and removing noise.
โ๏ธ Tokenization: Used DistilGPT-2's tokenizer for sequence conversion.
๐ Formatting: Structuring as "Query | Response" pairs.
#### Training Hyperparameters
- **Training regime:** <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
๐ Batch size: 15
๐ Epochs: 3
๐ ๏ธ Optimizer: Adam with a linear learning rate scheduler
๐ฅ๏ธ Training Frameworks: PyTorch, Hugging Face Transformers
### Results
| Dataset | ROUGE-1 | ROUGE-2 | ROUGE-L | METEOR | Perplexity |
|-----------------------------------|---------|---------|---------|--------|------------|
| ๐ Customer Support Interactions | 0.7102 | 0.4586 | 0.5610 | 0.6924 | 1.4273 |
#### Summary
The Fine-Tuned DistilGPT-2 SLM for Customer Support Interactions is a compact and efficient language model designed to enhance automated customer service.
Trained on 26,000 customer-agent dialogues, the model improves chatbot performance by generating accurate, context-aware responses to customer queries.
## Glossary
SLM (Small Language Model): A compact language model optimized for efficiency.
Perplexity: Measures how well a model predicts.
ROUGE & METEOR: Metrics for evaluating text generation quality.
## Author
Novumlogic Technologies Pvt Ltd
|
cwyeungam/FinetunedModel_amazon_sentiment_10k
|
cwyeungam
| 2025-03-25T13:07:01Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-25T13:06:49Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
sainoforce/modelv7
|
sainoforce
| 2025-03-25T13:05:31Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-19T13:02:08Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
eyjafjalla114/hm-flux-lora
|
eyjafjalla114
| 2025-03-25T13:04:32Z
| 0
| 0
|
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-03-25T09:01:54Z
|
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: hm
---
# Hm Flux Lora
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `hm` to trigger the image generation.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('eyjafjalla114/hm-flux-lora', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
RichardErkhov/rhaymison_-_Mistral-portuguese-luana-7b-Mathematics-8bits
|
RichardErkhov
| 2025-03-25T13:04:11Z
| 0
| 0
| null |
[
"safetensors",
"mistral",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-03-25T12:58:35Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Mistral-portuguese-luana-7b-Mathematics - bnb 8bits
- Model creator: https://huggingface.co/rhaymison/
- Original model: https://huggingface.co/rhaymison/Mistral-portuguese-luana-7b-Mathematics/
Original model description:
---
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- portuguese
- math
- mathematics
- matematica
base_model: rhaymison/Mistral-portuguese-luana-7b
datasets:
- rhaymison/orca-math-portuguese-64k
pipeline_tag: text-generation
model-index:
- name: Mistral-portuguese-luana-7b-Mathematics
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: ENEM Challenge (No Images)
type: eduagarcia/enem_challenge
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 56.68
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BLUEX (No Images)
type: eduagarcia-temp/BLUEX_without_images
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 45.9
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: OAB Exams
type: eduagarcia/oab_exams
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 37.9
name: accuracy
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 RTE
type: assin2
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 89.36
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Assin2 STS
type: eduagarcia/portuguese_benchmark
split: test
args:
num_few_shot: 15
metrics:
- type: pearson
value: 74.78
name: pearson
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: FaQuAD NLI
type: ruanchaves/faquad-nli
split: test
args:
num_few_shot: 15
metrics:
- type: f1_macro
value: 74.87
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HateBR Binary
type: ruanchaves/hatebr
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 76.39
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: PT Hate Speech Binary
type: hate_speech_portuguese
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 67.46
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: tweetSentBR
type: eduagarcia/tweetsentbr_fewshot
split: test
args:
num_few_shot: 25
metrics:
- type: f1_macro
value: 49.03
name: f1-macro
source:
url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b-Mathematics
name: Open Portuguese LLM Leaderboard
---
# Mistral-portuguese-luana-7b-Mathematics
<p align="center">
<img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/luana-math.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>
Luana Mathematics is a tuned model of the Luana-7b based on the Mistral 7b architecture.
This Model has been fine-tuned with 64k math problems and solutions derived from Microsoft's Orca-Math.
# How to use
You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches.
Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response.
Important points like these help models (even smaller models like 7b) to perform much better.
```python
!pip install -q -U transformers
!pip install -q -U accelerate
!pip install -q -U bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model = AutoModelForCausalLM.from_pretrained("rhaymison/Mistral-portuguese-luana-7b-Mathematics", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Mistral-portuguese-luana-7b-Mathematics")
model.eval()
```
You can use with Pipeline but in this example i will use such as Streaming
```python
inputs = tokenizer([f"""<s>[INST] Abaixo estรก uma instruรงรฃo que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.
### instruรงรฃo: Se eu emprestei 100 reais para o meu irmรฃo para que ele me pague em 3 vezes. O juros do emprรฉstimo รฉ 5% do valor do emprestimo por mes. Quanto meu irmรฃo terรก que me pagar no total?
[/INST]"""], return_tensors="pt")
inputs.to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
#Para calcular o valor total que seu irmรฃo terรก que pagar, precisamos primeiro calcular o interesse do emprรฉstimo e depois adicionรก -lo ao valor principal.
#O interesse do emprรฉstimo รฉ de 5% do valor do emprรฉstimo por mรชs.Entรฃo, para cada mรชs, o interesse serรก:
#5% de 100 reais = 0,05 * 100 = 5 reais
#Como o emprรฉstimo รฉ devolvido em 3 vezes, o interesse total serรก:
#5 reais/mรชs * 3 meses = 15 reais
#Agora, adicionamos o interesse ao valor principal para encontrar o valor total que seu irmรฃo terรก que pagar:
#100 reais (valor principal) + 15 reais (juros) = 115 reais
#Portanto, seu irmรฃo terรก que pagar 115 reais no total.
```
```python
inputs = tokenizer([f"""<s>[INST] Abaixo estรก uma instruรงรฃo que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.
### instruรงรฃo: Joรฃo Esta pilotando sua bicicleta a uma velocidade de 20 km por hora. Se Joรฃo esta fazendo uma Viagem da cidade de Venda nova a cidade de Independencia que possuem a distancia entre si de 320km.
Quanto tempo Joรฃo vai demorar para ir de uma cidade a outra?
[/INST]"""], return_tensors="pt")
inputs.to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
#Para descobrir quanto tempo levarรก Joรฃo para viajar da cidade de venda nova para a cidade de Independรชncia, precisamos dividir a distรขncia total que ele precisa percorrer pela velocidade em que ele estรก andando.
#Distรขncia total = 320 km
#Velocidade de Joรฃo = 20 km/h
#Tempo = distรขncia / velocidade
#Tempo = 320 km / 20 km / h
#Tempo = 16 horas
#Entรฃo, levarรก Joรฃo 16 horas para viajar da cidade de venda nova para a cidade de Independรชncia.
```
```python
inputs = tokenizer([f"""<s>[INST] Abaixo estรก uma instruรงรฃo que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.
### instruรงรฃo: Qual a area de um triangulo equilatero de 4 cm de base por 2 de altura ?
[/INST]"""], return_tensors="pt")
inputs.to(model.device)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=200)
#A รกrea de um triรขngulo equilรกtero รฉ dada pela fรณrmula:
#รrea = (1/2) * base * altura
#Dado que a base รฉ de 4 cm e a altura รฉ de 2 cm, podemos conectar esses valores ร fรณrmula:
#รrea = (1/2) * 4 cm * 2 cm
#รrea = (1/2) * 8 cmยฒ
#รrea = 4 cmยฒ
#Portanto, a รกrea do triรขngulo equilรกtero รฉ de 4 centรญmetros quadrados.
```
If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization.
For the complete model in colab you will need the A100.
If you want to use 4bits or 8bits, T4 or L4 will already solve the problem.
# 4bits
```python
from transformers import BitsAndBytesConfig
import torch
nb_4bit_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained(
base_model,
quantization_config=bnb_config,
device_map={"": 0}
)
```
# Open Portuguese LLM Leaderboard Evaluation Results
Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/Mistral-portuguese-luana-7b-Mathematics) and on the [๐ Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
| Metric | Value |
|--------------------------|--------|
|Average |**63.6**|
|ENEM Challenge (No Images)| 56.68|
|BLUEX (No Images) | 45.90|
|OAB Exams | 37.90|
|Assin2 RTE | 89.36|
|Assin2 STS | 74.78|
|FaQuAD NLI | 74.87|
|HateBR Binary | 76.39|
|PT Hate Speech Binary | 67.46|
|tweetSentBR | 49.03|
### Comments
Any idea, help or report will always be welcome.
email: [email protected]
<div style="display:flex; flex-direction:row; justify-content:left">
<a href="https://www.linkedin.com/in/heleno-betini-2b3016175/" target="_blank">
<img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white">
</a>
<a href="https://github.com/rhaymisonbetini" target="_blank">
<img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white">
</a>
|
namfam/Qwen2.5-0.5B-Instruct-fp16
|
namfam
| 2025-03-25T13:03:49Z
| 6
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-03-25T05:00:01Z
|
---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
DhruvK3000/llama8b_lending_25th_march
|
DhruvK3000
| 2025-03-25T13:01:40Z
| 0
| 0
|
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"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
| 2025-03-25T12:54:12Z
|
---
base_model: unsloth/Meta-Llama-3.1-8B
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** DhruvK3000
- **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)
|
RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf
|
RichardErkhov
| 2025-03-25T13:00:43Z
| 0
| 0
| null |
[
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T11:57:55Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3.2-3B_4x3_fix_tail - GGUF
- Model creator: https://huggingface.co/kenken6696/
- Original model: https://huggingface.co/kenken6696/Llama-3.2-3B_4x3_fix_tail/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3.2-3B_4x3_fix_tail.Q2_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q2_K.gguf) | Q2_K | 1.27GB |
| [Llama-3.2-3B_4x3_fix_tail.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [Llama-3.2-3B_4x3_fix_tail.IQ3_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [Llama-3.2-3B_4x3_fix_tail.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [Llama-3.2-3B_4x3_fix_tail.IQ3_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [Llama-3.2-3B_4x3_fix_tail.Q3_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q3_K.gguf) | Q3_K | 1.57GB |
| [Llama-3.2-3B_4x3_fix_tail.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [Llama-3.2-3B_4x3_fix_tail.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [Llama-3.2-3B_4x3_fix_tail.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [Llama-3.2-3B_4x3_fix_tail.Q4_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q4_0.gguf) | Q4_0 | 1.79GB |
| [Llama-3.2-3B_4x3_fix_tail.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [Llama-3.2-3B_4x3_fix_tail.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [Llama-3.2-3B_4x3_fix_tail.Q4_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q4_K.gguf) | Q4_K | 1.88GB |
| [Llama-3.2-3B_4x3_fix_tail.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [Llama-3.2-3B_4x3_fix_tail.Q4_1.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q4_1.gguf) | Q4_1 | 1.95GB |
| [Llama-3.2-3B_4x3_fix_tail.Q5_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q5_0.gguf) | Q5_0 | 2.11GB |
| [Llama-3.2-3B_4x3_fix_tail.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [Llama-3.2-3B_4x3_fix_tail.Q5_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q5_K.gguf) | Q5_K | 2.16GB |
| [Llama-3.2-3B_4x3_fix_tail.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [Llama-3.2-3B_4x3_fix_tail.Q5_1.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q5_1.gguf) | Q5_1 | 2.28GB |
| [Llama-3.2-3B_4x3_fix_tail.Q6_K.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q6_K.gguf) | Q6_K | 2.46GB |
| [Llama-3.2-3B_4x3_fix_tail.Q8_0.gguf](https://huggingface.co/RichardErkhov/kenken6696_-_Llama-3.2-3B_4x3_fix_tail-gguf/blob/main/Llama-3.2-3B_4x3_fix_tail.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
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 ๐ค 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]
|
AlekseyCalvin/lenin_wan14b_t2v_lora
|
AlekseyCalvin
| 2025-03-25T13:00:28Z
| 0
| 0
| null |
[
"image-to-video",
"lora",
"replicate",
"text-to-video",
"video",
"video-generation",
"en",
"zh",
"base_model:Wan-AI/Wan2.1-T2V-14B-Diffusers",
"base_model:adapter:Wan-AI/Wan2.1-T2V-14B-Diffusers",
"license:apache-2.0",
"region:us"
] |
text-to-video
| 2025-03-25T12:27:48Z
|
---
license: apache-2.0
language:
- en
- zh
tags:
- image-to-video
- lora
- replicate
- text-to-video
- video
- video-generation
base_model: "Wan-AI/Wan2.1-T2V-14B-Diffusers"
pipeline_tag: text-to-video
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: LEN Vladimir Lenin
---
# Lenin_Wan14B_T2V_Lora
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the Wan2.1 14b video generation model.
It can be used with diffusers or ComfyUI, and can be loaded against both the text-to-video and image-to-video Wan2.1 models.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/wan-lora-trainer/train
## Trigger words
You should use `LEN Vladimir Lenin` to trigger the video generation.
## Use this LoRA
Replicate has a collection of Wan2.1 models that are optimised for speed and cost. They can also be used with this LoRA:
- https://replicate.com/collections/wan-video
- https://replicate.com/fofr/wan2.1-with-lora
### Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LEN Vladimir Lenin",
"lora_url": "https://huggingface.co/alekseycalvin/lenin_wan14b_t2v_lora/resolve/main/wan2.1-14b-len-vladimir-lenin-lora.safetensors"
}
output = replicate.run(
"fofr/wan2.1-with-lora:f83b84064136a38415a3aff66c326f94c66859b8ad7a2cb432e2822774f07b08",
model="14b",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.mp4", "wb") as file:
file.write(item.read())
```
### Using with Diffusers
```py
pip install git+https://github.com/huggingface/diffusers.git
```
```py
import torch
from diffusers.utils import export_to_video
from diffusers import AutoencoderKLWan, WanPipeline
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
model_id = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
flow_shift = 3.0 # 5.0 for 720P, 3.0 for 480P
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
pipe.to("cuda")
pipe.load_lora_weights("alekseycalvin/lenin_wan14b_t2v_lora")
pipe.enable_model_cpu_offload() #for low-vram environments
prompt = "LEN Vladimir Lenin"
negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=480,
width=832,
num_frames=81,
guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)
```
## Training details
- Steps: 750
- Learning rate: 0.0002
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/alekseycalvin/lenin_wan14b_t2v_lora/discussions) to add videos that show off what youโve made with this LoRA.
|
RayneAmes/kokujin1
|
RayneAmes
| 2025-03-25T12:59:48Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T12:35:23Z
|
---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
welashi/my_ner_model
|
welashi
| 2025-03-25T12:59:18Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-03-25T12:57:57Z
|
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: my_ner_model
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
config: wnut_17
split: test
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.5209059233449478
- name: Recall
type: recall
value: 0.27710843373493976
- name: F1
type: f1
value: 0.36176648517846344
- name: Accuracy
type: accuracy
value: 0.9407036894532085
---
<!-- 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_ner_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2774
- Precision: 0.5209
- Recall: 0.2771
- F1: 0.3618
- Accuracy: 0.9407
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 213 | 0.2812 | 0.4724 | 0.2298 | 0.3092 | 0.9374 |
| No log | 2.0 | 426 | 0.2774 | 0.5209 | 0.2771 | 0.3618 | 0.9407 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.4.1
- Tokenizers 0.21.0
|
gallilmaimon/l3_good
|
gallilmaimon
| 2025-03-25T12:59:07Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"speech_language_model",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T12:55:23Z
|
---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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|
selmanbaysan/berturk-base_fine_tuned
|
selmanbaysan
| 2025-03-25T12:58:03Z
| 0
| 0
|
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:1533351",
"loss:MultipleNegativesRankingLoss",
"loss:SoftmaxLoss",
"loss:CoSENTLoss",
"dataset:selmanbaysan/msmarco-tr_fine_tuning_dataset",
"dataset:selmanbaysan/fiqa-tr_fine_tuning_dataset",
"dataset:selmanbaysan/scifact-tr_fine_tuning_dataset",
"dataset:selmanbaysan/nfcorpus-tr_fine_tuning_dataset",
"dataset:selmanbaysan/multinli_tr_fine_tuning_dataset",
"dataset:selmanbaysan/snli_tr_fine_tuning_dataset",
"dataset:selmanbaysan/stsb-tr",
"dataset:selmanbaysan/wmt16_en_tr_fine_tuning_dataset",
"dataset:selmanbaysan/quora-tr_fine_tuning_dataset",
"dataset:selmanbaysan/xnli_tr_fine_tuning_dataset",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:selmanbaysan/berturk_base_contrastive_loss_training",
"base_model:finetune:selmanbaysan/berturk_base_contrastive_loss_training",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-03-25T12:57:45Z
|
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:1533351
- loss:MultipleNegativesRankingLoss
- loss:SoftmaxLoss
- loss:CoSENTLoss
base_model: selmanbaysan/berturk_base_contrastive_loss_training
widget:
- source_sentence: CIA, filmi indirdi ve filmi ertesi gรผn Birleลmiล Milletlere gรถtรผrdรผ.
sentences:
- Bir aรงฤฑklama yapmalฤฑsฤฑn! Wolverstone'a ne oldu?
- CIA, BMโnin filmi hemen gรถrmesi gerektiฤini dรผลรผnรผyordu.
- Benim yolum en zor yoldur.
- source_sentence: Port Royal'de bu serseriyi bekleyen bir idam sehpasฤฑ var. Kanlฤฑ
Korsan buna mรผdahale ederdi ama Lord Julian รถnce davranฤฑp ona engel oldu.
sentences:
- Babamฤฑz bunlarฤฑn hayvan deฤil yaratฤฑk olduฤunu sรถyledi.
- Port Royal suรงlularฤฑ cezalandฤฑrmak iรงin olanaklara sahiptir.
- Geรง Anneler Gรผnรผ kahvaltฤฑsฤฑna bazฤฑ arkadaลlar sฤฑrayla ev sahipliฤi yapฤฑyorlar.
- source_sentence: satฤฑn almak iรงin hangi boyut Fitbit ลarj
sentences:
- Texas A&M'den bir eฤitim, yฤฑldan yฤฑla รงok arzu edilen ve รงok uygun fiyatlฤฑ olmaya
devam ediyor. Texas A&M'e bir yฤฑl boyunca katฤฑlmak iรงin toplam ortalama katฤฑlฤฑm
maliyeti yaklaลฤฑk 22,470 $ (devlet iรงi ikamet edenler iรงin), eฤitim ve harรง, oda
ve yรถnetim kurulu, kitaplar, ulaลฤฑm ve รงeลitli masraflar iรงerir. Kolej ฤฐstasyonu'ndaki
Texas A&M รniversitesi'nde dรถnem baลฤฑna 15 lisans kredi saatine dayanarak; bazฤฑ
harรง ve harรงlar buraya yansฤฑtฤฑlmamฤฑลtฤฑr.
- ฤฐlk fitbitimi satฤฑn almak istiyorum ve ลarj saatine karar verdim. Bununla birlikte,
bileฤimi รถlรงtรผkten sonra, 6,5 inรงte geldi. Mevcut boyutlarฤฑ kontrol ettikten sonra
kรผรงรผk ve bรผyรผk arasฤฑndayฤฑm gibi gรถrรผnรผyor. Kรผรงรผk 6,7'ye รงฤฑkar ve bรผyรผk 6.3'ten
baลlar. Hangisinin daha iyi olacaฤฤฑndan gerรงekten emin deฤilim.
- Atriyal Dalgalanma Nedenleri. Atriyal dalgalanma, kalbin anormalliklerinden veya
hastalฤฑklarฤฑndan, vรผcudun baลka bir yerinde kalbi etkileyen bir hastalฤฑktan veya
elektriksel dรผrtรผlerin kalp yoluyla bulaลma ลeklini deฤiลtiren maddelerin tรผketilmesinden
kaynaklanabilir. Bazฤฑ insanlarda, altta yatan hiรงbir neden bulunmaz.
- source_sentence: '"Ben kimim" sorusuna nasฤฑl cevap veririm?'
sentences:
- Notlarฤฑmฤฑ nasฤฑl ezberleyebilirim?
- Birinin en zor soru olan "ben kimim?" sorusuna nasฤฑl cevap verebileceฤi nasฤฑl
aรงฤฑklanabilir?
- Donald Trump'ฤฑn 2016 seรงimlerini kazanma ihtimali nedir?
- source_sentence: Stoklara nasฤฑl yatฤฑrฤฑm yapabilirim?
sentences:
- '
Bu soru yรผklรผ ama denemek iรงin elimden geleni yapacaฤฤฑm. รncelikle, hisse senetlerine
yatฤฑrฤฑm yapmadan รถnce hazฤฑr olup olmadฤฑฤฤฑnฤฑzฤฑ belirlemeniz gerekir. Yรผksek faizli
borcunuz varsa, hisse senetlerine yatฤฑrฤฑm yapmadan รถnce onu รถdemeniz daha iyi
olacaktฤฑr. Hisse senetleri uzun vadede yaklaลฤฑk %8-10 getiri saฤlar, bu nedenle
%8-10''dan daha yรผksek faizli herhangi bir borcunuzu รถdemeniz daha iyi olur. รoฤu
insan, 401k''larฤฑnda veya Roth IRA''larฤฑnda ortak fonlar aracฤฑlฤฑฤฤฑyla hisse senetlerine
baลlar. Bireysel hisse senetleri yerine ortak fonlara yatฤฑrฤฑm yapmak istiyorsanฤฑz,
รงok okumalฤฑ ve รถฤrenmelisiniz. Bir aracฤฑ hesap veya hisse senedi aklฤฑnฤฑzda varsa,
doฤrudan ลirkete DRIP (temettรผ yeniden yatฤฑrma planฤฑ) aracฤฑlฤฑฤฤฑyla yatฤฑrฤฑm yapabilirsiniz.
Farklฤฑ aracฤฑ kurumlarฤฑ karลฤฑlaลtฤฑrarak size en uygun olanฤฑ belirlemeniz gerekir.
ฤฐnternet bilgisine sahip olduฤunuzu gรถz รถnรผnde bulundurarak, daha dรผลรผk komisyonlar
sunan bir indirimli aracฤฑ kurum kullanmanฤฑzฤฑ รถneririm. ฤฐyi bir kฤฑlavuz, yatฤฑrฤฑm
yaptฤฑฤฤฑnฤฑz tutarฤฑn %1''inden daha az komisyon รถdemektir. รevrimiรงi aracฤฑ hesabฤฑnฤฑzฤฑ
aรงฤฑp iรงine para yatฤฑrdฤฑktan sonra, hisse senedi satฤฑn alma iลlemi oldukรงa basittir.
Satฤฑn almak istediฤiniz hisse senedi miktarฤฑ iรงin bir emir verin. Bu, mevcut piyasa
fiyatฤฑnda bir satฤฑn alma anlamฤฑna gelen bir piyasa emri olabilir. Veya fiyatฤฑ
kontrol edebileceฤiniz bir sฤฑnฤฑr emri kullanabilirsiniz. Yeni baลlayanlar iรงin
birรงok iyi kitap vardฤฑr. Kiลisel olarak, Motley Fool''dan รถฤrendim. Ve son olarak,
eฤlenin. รฤrendiฤiniz kadarฤฑnฤฑ รถฤrenin ve kulรผbรผnรผzรผn tadฤฑnฤฑ รงฤฑkarฤฑn.'
- '"En iyi รงรถzรผm, arabayฤฑ satฤฑn almak ve kendi kredinizi almak (ChrisInEdmonton''un
yanฤฑtladฤฑฤฤฑ gibi). Buna raฤmen, kredi birliฤim, bir baลkasฤฑnฤฑ baลlฤฑฤa eklerken
hala bir kredim olduฤunda bir baลlฤฑk kayฤฑt รผcreti iรงin izin verdi. Baลlฤฑk sahibi
olan bankaya, bir baลkasฤฑnฤฑ baลlฤฑฤa eklemek iรงin bir hรผkรผm olup olmadฤฑฤฤฑnฤฑ sorabilirsiniz. Benim
iรงin toplam maliyet, bankada bir รถฤleden sonra ve yaklaลฤฑk 20 veya 40 dolar (bir
sรผredir) oldu."'
- 'รncelikle varsayฤฑmฤฑnฤฑz doฤru: Bรผyรผk bir miktar YetAnotherCryptoCoin''i ICO''sundan
kฤฑsa bir sรผre sonra nasฤฑl nakde รงevirebilirsiniz? Kripto borsalarฤฑ yeni bir para
birimini eklemek iรงin biraz zaman alฤฑr, hatta hiรง eklemeyebilirler. Ve hatta eklediklerinde,
iลlem hacmi genellikle dรผลรผktรผr. Sanฤฑrฤฑm bu, yatฤฑrฤฑmcฤฑlar iรงin gerรงekten รงekici
olmayan ลeydir (teknoloji tutkunlarฤฑndan ayrฤฑ olarak), yรผksek volatilite dฤฑลฤฑnda.
Gรผvenilir bir iลlem yeteneฤi tamamen eksikliฤi.'
datasets:
- selmanbaysan/msmarco-tr_fine_tuning_dataset
- selmanbaysan/fiqa-tr_fine_tuning_dataset
- selmanbaysan/scifact-tr_fine_tuning_dataset
- selmanbaysan/nfcorpus-tr_fine_tuning_dataset
- selmanbaysan/multinli_tr_fine_tuning_dataset
- selmanbaysan/snli_tr_fine_tuning_dataset
- selmanbaysan/stsb-tr
- selmanbaysan/wmt16_en_tr_fine_tuning_dataset
- selmanbaysan/quora-tr_fine_tuning_dataset
- selmanbaysan/xnli_tr_fine_tuning_dataset
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
- cosine_accuracy
- cosine_accuracy_threshold
- cosine_f1
- cosine_f1_threshold
- cosine_precision
- cosine_recall
- cosine_ap
- cosine_mcc
model-index:
- name: SentenceTransformer based on selmanbaysan/berturk_base_contrastive_loss_training
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: stsb tr
type: stsb-tr
metrics:
- type: pearson_cosine
value: 0.8109525221457314
name: Pearson Cosine
- type: spearman_cosine
value: 0.8168105572572449
name: Spearman Cosine
- task:
type: binary-classification
name: Binary Classification
dataset:
name: snli tr
type: snli-tr
metrics:
- type: cosine_accuracy
value: 0.7326
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.6572713851928711
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.600581677169171
name: Cosine F1
- type: cosine_f1_threshold
value: 0.4847041368484497
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5033516148689823
name: Cosine Precision
- type: cosine_recall
value: 0.7443676779813758
name: Cosine Recall
- type: cosine_ap
value: 0.6132056266749955
name: Cosine Ap
- type: cosine_mcc
value: 0.3561722570448152
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: xnli tr
type: xnli-tr
metrics:
- type: cosine_accuracy
value: 0.7345381526104418
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.7267703413963318
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.603629417382999
name: Cosine F1
- type: cosine_f1_threshold
value: 0.5046486854553223
name: Cosine F1 Threshold
- type: cosine_precision
value: 0.5
name: Cosine Precision
- type: cosine_recall
value: 0.7614457831325301
name: Cosine Recall
- type: cosine_ap
value: 0.633783326089055
name: Cosine Ap
- type: cosine_mcc
value: 0.358990791322683
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: wmt16
type: wmt16
metrics:
- type: cosine_accuracy
value: 0.999000999000999
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.2128763496875763
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9995002498750626
name: Cosine F1
- type: cosine_f1_threshold
value: 0.2128763496875763
name: Cosine F1 Threshold
- type: cosine_precision
value: 1.0
name: Cosine Precision
- type: cosine_recall
value: 0.999000999000999
name: Cosine Recall
- type: cosine_ap
value: 1.0
name: Cosine Ap
- type: cosine_mcc
value: 0.0
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: msmarco tr
type: msmarco-tr
metrics:
- type: cosine_accuracy
value: 0.9999682942295498
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: -0.04683864116668701
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9999841468634569
name: Cosine F1
- type: cosine_f1_threshold
value: -0.04683864116668701
name: Cosine F1 Threshold
- type: cosine_precision
value: 1.0
name: Cosine Precision
- type: cosine_recall
value: 0.9999682942295498
name: Cosine Recall
- type: cosine_ap
value: 1.0
name: Cosine Ap
- type: cosine_mcc
value: 0.0
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: fiqa tr
type: fiqa-tr
metrics:
- type: cosine_accuracy
value: 0.9991922455573505
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.03544411063194275
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9995959595959596
name: Cosine F1
- type: cosine_f1_threshold
value: 0.03544411063194275
name: Cosine F1 Threshold
- type: cosine_precision
value: 1.0
name: Cosine Precision
- type: cosine_recall
value: 0.9991922455573505
name: Cosine Recall
- type: cosine_ap
value: 1.0
name: Cosine Ap
- type: cosine_mcc
value: 0.0
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: quora tr
type: quora-tr
metrics:
- type: cosine_accuracy
value: 0.9998688696564385
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: 0.16285157203674316
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9999344305291455
name: Cosine F1
- type: cosine_f1_threshold
value: 0.16285157203674316
name: Cosine F1 Threshold
- type: cosine_precision
value: 1.0
name: Cosine Precision
- type: cosine_recall
value: 0.9998688696564385
name: Cosine Recall
- type: cosine_ap
value: 1.0
name: Cosine Ap
- type: cosine_mcc
value: 0.0
name: Cosine Mcc
- task:
type: binary-classification
name: Binary Classification
dataset:
name: nfcorpus tr
type: nfcorpus-tr
metrics:
- type: cosine_accuracy
value: 0.9999121651295564
name: Cosine Accuracy
- type: cosine_accuracy_threshold
value: -0.13325101137161255
name: Cosine Accuracy Threshold
- type: cosine_f1
value: 0.9999560806359523
name: Cosine F1
- type: cosine_f1_threshold
value: -0.13325101137161255
name: Cosine F1 Threshold
- type: cosine_precision
value: 1.0
name: Cosine Precision
- type: cosine_recall
value: 0.9999121651295564
name: Cosine Recall
- type: cosine_ap
value: 1.0
name: Cosine Ap
- type: cosine_mcc
value: 0.0
name: Cosine Mcc
---
# SentenceTransformer based on selmanbaysan/berturk_base_contrastive_loss_training
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [selmanbaysan/berturk_base_contrastive_loss_training](https://huggingface.co/selmanbaysan/berturk_base_contrastive_loss_training) on the [msmarco-tr](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset), [fiqa-tr](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset), [scifact-tr](https://huggingface.co/datasets/selmanbaysan/scifact-tr_fine_tuning_dataset), [nfcorpus-tr](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset), [multinli-tr](https://huggingface.co/datasets/selmanbaysan/multinli_tr_fine_tuning_dataset), [snli-tr](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset), [stsb-tr](https://huggingface.co/datasets/selmanbaysan/stsb-tr) and [wmt16](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset) datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [selmanbaysan/berturk_base_contrastive_loss_training](https://huggingface.co/selmanbaysan/berturk_base_contrastive_loss_training) <!-- at revision 007e20ef9ebac5677588b87eaf7250617a780034 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- [msmarco-tr](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset)
- [fiqa-tr](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset)
- [scifact-tr](https://huggingface.co/datasets/selmanbaysan/scifact-tr_fine_tuning_dataset)
- [nfcorpus-tr](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset)
- [multinli-tr](https://huggingface.co/datasets/selmanbaysan/multinli_tr_fine_tuning_dataset)
- [snli-tr](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset)
- [stsb-tr](https://huggingface.co/datasets/selmanbaysan/stsb-tr)
- [wmt16](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the ๐ค Hub
model = SentenceTransformer("selmanbaysan/berturk-base_fine_tuned")
# Run inference
sentences = [
'Stoklara nasฤฑl yatฤฑrฤฑm yapabilirim?',
"\nBu soru yรผklรผ ama denemek iรงin elimden geleni yapacaฤฤฑm. รncelikle, hisse senetlerine yatฤฑrฤฑm yapmadan รถnce hazฤฑr olup olmadฤฑฤฤฑnฤฑzฤฑ belirlemeniz gerekir. Yรผksek faizli borcunuz varsa, hisse senetlerine yatฤฑrฤฑm yapmadan รถnce onu รถdemeniz daha iyi olacaktฤฑr. Hisse senetleri uzun vadede yaklaลฤฑk %8-10 getiri saฤlar, bu nedenle %8-10'dan daha yรผksek faizli herhangi bir borcunuzu รถdemeniz daha iyi olur. รoฤu insan, 401k'larฤฑnda veya Roth IRA'larฤฑnda ortak fonlar aracฤฑlฤฑฤฤฑyla hisse senetlerine baลlar. Bireysel hisse senetleri yerine ortak fonlara yatฤฑrฤฑm yapmak istiyorsanฤฑz, รงok okumalฤฑ ve รถฤrenmelisiniz. Bir aracฤฑ hesap veya hisse senedi aklฤฑnฤฑzda varsa, doฤrudan ลirkete DRIP (temettรผ yeniden yatฤฑrma planฤฑ) aracฤฑlฤฑฤฤฑyla yatฤฑrฤฑm yapabilirsiniz. Farklฤฑ aracฤฑ kurumlarฤฑ karลฤฑlaลtฤฑrarak size en uygun olanฤฑ belirlemeniz gerekir. ฤฐnternet bilgisine sahip olduฤunuzu gรถz รถnรผnde bulundurarak, daha dรผลรผk komisyonlar sunan bir indirimli aracฤฑ kurum kullanmanฤฑzฤฑ รถneririm. ฤฐyi bir kฤฑlavuz, yatฤฑrฤฑm yaptฤฑฤฤฑnฤฑz tutarฤฑn %1'inden daha az komisyon รถdemektir. รevrimiรงi aracฤฑ hesabฤฑnฤฑzฤฑ aรงฤฑp iรงine para yatฤฑrdฤฑktan sonra, hisse senedi satฤฑn alma iลlemi oldukรงa basittir. Satฤฑn almak istediฤiniz hisse senedi miktarฤฑ iรงin bir emir verin. Bu, mevcut piyasa fiyatฤฑnda bir satฤฑn alma anlamฤฑna gelen bir piyasa emri olabilir. Veya fiyatฤฑ kontrol edebileceฤiniz bir sฤฑnฤฑr emri kullanabilirsiniz. Yeni baลlayanlar iรงin birรงok iyi kitap vardฤฑr. Kiลisel olarak, Motley Fool'dan รถฤrendim. Ve son olarak, eฤlenin. รฤrendiฤiniz kadarฤฑnฤฑ รถฤrenin ve kulรผbรผnรผzรผn tadฤฑnฤฑ รงฤฑkarฤฑn.",
"รncelikle varsayฤฑmฤฑnฤฑz doฤru: Bรผyรผk bir miktar YetAnotherCryptoCoin'i ICO'sundan kฤฑsa bir sรผre sonra nasฤฑl nakde รงevirebilirsiniz? Kripto borsalarฤฑ yeni bir para birimini eklemek iรงin biraz zaman alฤฑr, hatta hiรง eklemeyebilirler. Ve hatta eklediklerinde, iลlem hacmi genellikle dรผลรผktรผr. Sanฤฑrฤฑm bu, yatฤฑrฤฑmcฤฑlar iรงin gerรงekten รงekici olmayan ลeydir (teknoloji tutkunlarฤฑndan ayrฤฑ olarak), yรผksek volatilite dฤฑลฤฑnda. Gรผvenilir bir iลlem yeteneฤi tamamen eksikliฤi.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `stsb-tr`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.811 |
| **spearman_cosine** | **0.8168** |
#### Binary Classification
* Datasets: `snli-tr`, `xnli-tr`, `wmt16`, `msmarco-tr`, `fiqa-tr`, `quora-tr` and `nfcorpus-tr`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | snli-tr | xnli-tr | wmt16 | msmarco-tr | fiqa-tr | quora-tr | nfcorpus-tr |
|:--------------------------|:-----------|:-----------|:--------|:-----------|:--------|:---------|:------------|
| cosine_accuracy | 0.7326 | 0.7345 | 0.999 | 1.0 | 0.9992 | 0.9999 | 0.9999 |
| cosine_accuracy_threshold | 0.6573 | 0.7268 | 0.2129 | -0.0468 | 0.0354 | 0.1629 | -0.1333 |
| cosine_f1 | 0.6006 | 0.6036 | 0.9995 | 1.0 | 0.9996 | 0.9999 | 1.0 |
| cosine_f1_threshold | 0.4847 | 0.5046 | 0.2129 | -0.0468 | 0.0354 | 0.1629 | -0.1333 |
| cosine_precision | 0.5034 | 0.5 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| cosine_recall | 0.7444 | 0.7614 | 0.999 | 1.0 | 0.9992 | 0.9999 | 0.9999 |
| **cosine_ap** | **0.6132** | **0.6338** | **1.0** | **1.0** | **1.0** | **1.0** | **1.0** |
| cosine_mcc | 0.3562 | 0.359 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
<details><summary>msmarco-tr</summary>
#### msmarco-tr
* Dataset: [msmarco-tr](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset) at [f03d837](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset/tree/f03d83704e5ea276665384ca6d8bee3b19632c80)
* Size: 253,332 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.75 tokens</li><li>max: 28 tokens</li></ul> | <ul><li>min: 18 tokens</li><li>mean: 81.59 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Spagetti ve et sosu servisinde kaรง kalori</code> | <code>Gฤฑda Bilgisi. Makarna Yemekleri kategorisinde Et Soslu Spagetti ile 100 g / mL'ye gรถre toplam 90.0 kalori ve aลaฤฤฑdaki beslenme gerรงekleri vardฤฑr: 5.1 g protein, 15.2 g karbonhidrat ve 1.0 g yaฤ.</code> |
| <code>galveston okyanusu ne kadar derin</code> | <code>galveston รงok sฤฑฤ olduฤu iรงin mucky - Eฤer kฤฑyฤฑdan 5 mil yรผrรผmek olsaydฤฑ, asla 10 veya 12 feet derinliฤinden fazla olmazdฤฑ. Galveston Kรถrfezi รงok sฤฑฤ, sadece 9 feet derinliฤinde, bu yรผzden kรถrfezden ve derin okyanusa bir kez รงฤฑktฤฑฤฤฑnฤฑzda, su o kadar รงamurlu olmayacak.</code> |
| <code>amlodipin diyabete neden olabilir</code> | <code>Hipertansiyon tedavisi iรงin Amlodipin tedavisine konduฤunuzda referanslara gรถre, diyabetin yeni baลlangฤฑcฤฑnฤฑ geliลtirme olasฤฑlฤฑฤฤฑnฤฑz %34 daha dรผลรผktรผr. Hipertansiyon tedavisi iรงin Amlodipin tedavisine konduฤunuzda referanslara gรถre, diyabetin yeni baลlangฤฑcฤฑnฤฑ geliลtirme olasฤฑlฤฑฤฤฑnฤฑz %34 daha dรผลรผktรผr. Kรผรงรผk dรผzenleme?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>fiqa-tr</summary>
#### fiqa-tr
* Dataset: [fiqa-tr](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset) at [bbc9e91](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset/tree/bbc9e91b5710d0ac4032b5c9e94066470f928c8c)
* Size: 14,166 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 17.67 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 9 tokens</li><li>mean: 196.19 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Bir iล gezisinde ne tรผr masraflar iล masrafฤฑ olarak kabul edilir?</code> | <code>IRS'a ait ilgili rehberlik. Genel olarak sรถyleyebileceฤim tek ลey, iลinizin giderlerinin dรผลebileceฤi yรถnรผndedir. Ancak bu, koลullara ve dรผลmek istediฤiniz gider tรผrรผne baฤlฤฑdฤฑr. Seyahat ฤฐล seyahatinden dolayฤฑ evden uzakta olan vergi mรผkellefleri, iliลkili giderleri dรผลebilirler, bu da varฤฑล noktasฤฑna ulaลma maliyetini, konaklama ve yemek masraflarฤฑnฤฑ ve diฤer normal ve gerekli giderleri iรงerir. Mรผkellefler, gรถrevlerinin onlarฤฑ evden รถnemli รถlรงรผde daha uzun sรผre uzak tutmasฤฑ ve iล taleplerini karลฤฑlamak iรงin uyku veya dinlenmeye ihtiyaรง duymalarฤฑ durumunda "evden uzakta" olarak kabul edilirler. Yemek ve yan masraflarฤฑn gerรงek maliyetleri dรผลรผrรผlebilir veya mรผkellef, standart yemek yardฤฑmฤฑ ve azaltฤฑlmฤฑล kayฤฑt tutma gereklilikleri kullanabilir. Kullanฤฑlan yรถnteme bakฤฑlmaksฤฑzฤฑn, yemek giderleri genellikle daha รถnce belirtildiฤi gibi %50 ile sฤฑnฤฑrlฤฑdฤฑr. Sadece gerรงek konaklama masraflarฤฑ gider olarak talep edilebilir ve belgeler iรงin makbuzlar tutulmalฤฑdฤฑr. Giderler makul ve uygun olmalฤฑdฤฑ...</code> |
| <code>ฤฐล Gideri - Kaza Sฤฑrasฤฑnda ฤฐล Gezisi Sฤฑrasฤฑnda Uygulanan Araba Sigortasฤฑ Teminat Tutarฤฑ</code> | <code>Genel bir kural olarak, mil รถlรงรผmรผ indirimi veya gerรงek giderler indirimi arasฤฑnda seรงim yapmanฤฑz gerekir. Fikir, mil รถlรงรผmรผ indiriminin aracฤฑn kullanฤฑmฤฑnฤฑn tรผm maliyetlerini kapsamasฤฑdฤฑr. Park รผcretleri ve otoyol รผcretleri gibi istisnalar, her iki yรถntemin altฤฑnda ayrฤฑ ayrฤฑ indirilebilir. Mil รถlรงรผmรผ indirimi talep ederseniz sigorta maliyetlerini aรงฤฑk bir ลekilde indiremezsiniz. Ayrฤฑ olarak, muhtemelen aracฤฑnฤฑzฤฑn kazaya baฤlฤฑ olarak hasar kaybฤฑ olarak bir teminat indirimi indiremeyeceksiniz. รncelikle teminattan 100 dolar รงฤฑkarฤฑrsฤฑnฤฑz ve sonra onu vergi beyannamenizden ayarlanan brรผt gelirinize (AGI) bรถleriz. Teminatฤฑnฤฑz AGI'nฤฑzฤฑn %10'undan fazla ise onu indirebilirsiniz. Not edin ki, 1500 dolar teminatฤฑ olan biri bile yฤฑllฤฑk 14.000 dolarฤฑn รผzerinde gelir elde ederse hiรงbir ลey indiremez. รoฤu insan iรงin, sigorta teminatฤฑ gelire gรถre yeterince bรผyรผk deฤil, bu nedenle vergi indirimi iรงin uygun deฤildir. Kaynak</code> |
| <code>Yeni bir รงevrimiรงi iลletme baลlatmak</code> | <code>Amerika Birleลik Devletleri'nin รงoฤu eyaleti, yukarฤฑda belirtildiฤi gibi, aลaฤฤฑdakine benzer kurallara sahiptir: Kayฤฑt รผcretleri รถdemeniz neredeyse kesin. Dรผzenleme ลeklinize baฤlฤฑ olarak, iลletmeniz iรงin ayrฤฑ bir vergi beyannamesi doldurmanฤฑz gerekebilir veya gerekmeyebilir. (Vergi amaรงlarฤฑ iรงin tek bir iลletme sahibiyseniz, kiลisel Form 1040'ฤฑnฤฑzฤฑn Ek C'sini doldurursunuz.) Vergi รถdeme durumunuz, net kazancฤฑnฤฑzฤฑn olup olmamasฤฑna baฤlฤฑdฤฑr. Bazฤฑ kayฤฑplarฤฑn da indirilebilir olabileceฤi mรผmkรผndรผr. (Dikkat edin, net kazancฤฑnฤฑz olmasa bile bir beyanname sunmanฤฑz gerekebilir - Beyanname sunmak ve vergi รถdemek aynฤฑ ลey deฤildir, รงรผnkรผ beyannameniz hiรงbir vergi borcu gรถstermeyebilir.) Ayrฤฑca, eyalet dรผzeyinde, ne sattฤฑฤฤฑnฤฑz ve nasฤฑl sattฤฑฤฤฑnฤฑza baฤlฤฑ olarak, gelir vergisi รถtesinde ek รผcretler veya vergiler รถdemeniz gerekebilir. (รrneฤin, satฤฑล vergisi veya franchise vergileri devreye girebilir.) Bu konuda kendi eyalet yasalarฤฑnฤฑ kontrol etmeniz gerekir. Her zaman, durumunuza ve eyaletinize รถz...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>scifact-tr</summary>
#### scifact-tr
* Dataset: [scifact-tr](https://huggingface.co/datasets/selmanbaysan/scifact-tr_fine_tuning_dataset) at [382de5b](https://huggingface.co/datasets/selmanbaysan/scifact-tr_fine_tuning_dataset/tree/382de5b316d8c8042a23f34179a73fadc13cb53d)
* Size: 919 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 919 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 27.49 tokens</li><li>max: 73 tokens</li></ul> | <ul><li>min: 91 tokens</li><li>mean: 360.13 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>0 boyutlu biyomalzemeler indรผktif รถzelliklere sahip deฤildir.</code> | <code>Nanoteknolojiler, kรถk hรผcreleri รถlรงmek, anlamak ve manipรผle etmek iรงin yararlฤฑ olabilecek yeni ortaya รงฤฑkan platformlardฤฑr. รrnekler arasฤฑnda, kรถk hรผcre etiketleme ve in vivo izleme iรงin manyetik nanopartikรผller ve kuantum noktalarฤฑ; hรผcre iรงi gen/oligonรผkleotit ve protein/peptit teslimatฤฑ iรงin nanopartikรผller, karbon nanotรผpler ve polypleksler; ve kรถk hรผcre farklฤฑlaลtฤฑrmasฤฑ ve nakli iรงin mรผhendislik yapฤฑlmฤฑล nanometre รถlรงeฤi destekler yer alฤฑr. Bu inceleme, kรถk hรผcre izleme, farklฤฑlaลtฤฑrma ve nakli iรงin nanoteknolojilerin kullanฤฑmฤฑnฤฑ inceler. Ayrฤฑca, yararlฤฑlฤฑklarฤฑ ve sitotoksisiteyle ilgili olasฤฑ endiลeleri tartฤฑลฤฑrฤฑz.</code> |
| <code>Birleลik Krallฤฑk'ta 1 milyonun 5'inde anormal PrP pozitifliฤi vardฤฑr.</code> | <code><br>## Amaรงlar<br>Bovin spongiform ensefalopati (BSE) salgฤฑnฤฑndan sonra alt klinik prion enfeksiyonunun yaygฤฑnlฤฑฤฤฑ hakkฤฑnda mevcut tahminleri daha iyi anlamak ve daha geniล bir doฤum kohortunun etkilenip etkilenmediฤini gรถrmek, ayrฤฑca kan ve kan รผrรผnleri yรถnetimi ve cerrahi aletlerin iลlenmesiyle ilgili sonuรงlarฤฑ daha iyi anlamak iรงin arลivlenmiล apandisit รถrnekleri รผzerine ek bir anket yapmak.<br><br>## Tasarฤฑm<br>Bรผyรผk รถlรงekli, kalฤฑcฤฑ olarak baฤlantฤฑsฤฑ kesilmiล ve anonimleลtirilmiล arลivlenmiล apandisit รถrnekleri anketini tekrarlamak.<br><br>## รalฤฑลma Alanฤฑ<br>Daha รถnceki anketin katฤฑlฤฑmฤฑnฤฑn daha dรผลรผk olduฤu bรถlgelerde ek hastaneler dahil olmak รผzere, Birleลik Krallฤฑk'taki 41 hastanenin patoloji bรถlรผmlerinden arลivlenmiล apandisit รถrnekleri.<br><br>## รrnek<br>32.441 arลivlenmiล apandisit รถrneฤi, formalin ile sabitlenmiล ve parafinle gรถmรผlmรผล ve anormal prion proteini (PrP) varlฤฑฤฤฑ iรงin test edilmiล.<br><br>## Sonuรงlar<br>32.441 apandisit รถrneฤinin 16'sฤฑ anormal PrP iรงin pozitif รงฤฑktฤฑ, bu da genel bir yaygฤฑnlฤฑk oranฤฑ 493/m...</code> |
| <code>Kolon ve rektum kanseri hastalarฤฑnฤฑn %1-1'i bรถlgesel veya uzak metastazlarla teลhis edilir.</code> | <code><br>Medikare'nin geri รถdeme politikasฤฑ 1998'de kolon kanseri riskini artฤฑran hastalar iรงin tarama kolon skopi kapsamฤฑ saฤlayarak ve 2001'de tรผm bireyler iรงin tarama kolon skopi kapsamฤฑ geniลleterek deฤiลtirildi.<br><br>**Amaรง:** Medikare geri รถdeme politikasฤฑndaki deฤiลikliklerin kolon skopi kullanฤฑmฤฑ veya erken evre kolon kanseri teลhisi artฤฑลฤฑ ile iliลkili olup olmadฤฑฤฤฑnฤฑ belirlemek.<br><br>**Tasarฤฑm, Ayar ve Katฤฑlฤฑmcฤฑlar:** 1992-2002 yฤฑllarฤฑ arasฤฑnda 67 yaล ve รผstรผ, birincil tanฤฑsฤฑ kolon kanseri olan ve Surveillance, Epidemiology ve Sonuรงlarฤฑ (SEER) Medikare baฤlantฤฑlฤฑ veritabanฤฑndaki hastalar ile SEER alanlarฤฑnda ikamet eden ancak kanser tanฤฑsฤฑ almayan Medikare yararlanฤฑcฤฑlarฤฑ.<br><br>**Ana รฤฑktฤฑ รlรงรผmleri:** Kolonoskopi ve sigmoidoskopi kullanฤฑmฤฑndaki eฤilimler, kanser olmayan Medikare yararlanฤฑcฤฑlarฤฑ arasฤฑnda รงok deฤiลkenli Poisson regresyonu ile deฤerlendirildi. Kanserli hastalarda, evre erken (evre I) ile tรผm diฤer evreler (II-IV) olarak sฤฑnฤฑflandฤฑrฤฑldฤฑ. Zaman, dรถnem 1 (taramaya kapsama yok, 1992-1...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>nfcorpus-tr</summary>
#### nfcorpus-tr
* Dataset: [nfcorpus-tr](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset) at [22d1ef8](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset/tree/22d1ef8b6a9f1c196d1977541a66ca8eff946f06)
* Size: 110,575 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 12.97 tokens</li><li>max: 18 tokens</li></ul> | <ul><li>min: 29 tokens</li><li>mean: 378.06 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Memeli Kanser Hรผcreleri Kolesterolden Beslenir.</code> | <code>Dรผลรผk yoฤunluklu lipoprotein (LDL) reseptรถrlerinin iรงeriฤi, birincil meme kanserlerinden alฤฑnan dokularda belirlenmiล ve bilinen prognostik รถneme sahip deฤiลkenlerle karลฤฑlaลtฤฑrฤฑlmฤฑลtฤฑr. Dondurulmuล tรผmรถr รถrnekleri seรงilmiล ve 72 hastanฤฑn dokularฤฑ (bunlardan 32'si รถlmรผล) incelenmiลtir. LDL reseptรถr iรงeriฤi, hayatta kalma sรผresi ile ters orantฤฑlฤฑ bir korelasyon gรถstermiลtir. รok deฤiลkenli istatistiksel bir yรถntemle yapฤฑlan analiz, axiller metastazฤฑn varlฤฑฤฤฑ, รถstrojen ve LDL reseptรถrleri iรงeriฤi, tรผmรถr รงapฤฑ ve DNA deseni gibi faktรถrlerin, hastalarฤฑn hayatta kalma sรผresi konusunda prognostik deฤer taลฤฑdฤฑฤฤฑnฤฑ gรถstermiลtir. Meme kanseri hastalarฤฑnda hayatta kalma sรผresini tahmin etmek iรงin geliลtirilmiล yรถntemler, bireysel hastalar iรงin tedavi seรงiminin belirlenmesinde faydalฤฑ olabilir.</code> |
| <code>Memeli Kanser Hรผcreleri Kolesterolden Beslenir.</code> | <code>ARKA PLAN: Memurun en sฤฑk teลhis edilen kanseri, Amerika Birleลik Devletleri'nde kadฤฑnlar arasฤฑnda meme kanseri. Meme kanseri riski ve hayatta kalma ile ilgili diyet faktรถrlerinin deฤerlendirilmesi iรงin kapsamlฤฑ araลtฤฑrmalar tamamlandฤฑ; ancak klinik รงฤฑkarฤฑmlar iรงeren bir รถzet rapor gerekiyor. Malzemeler ve YรNTEMLER: Bu inceleme, diyet ile meme kanseri oluลumu, tekrarฤฑ, hayatta kalma ve รถlรผmcรผllรผฤรผ arasฤฑndaki mevcut epidemiyolojik ve klinik deneme kanฤฑtlarฤฑnฤฑ รถzetlemektedir. ฤฐnceleme, meme kanseri alt tiplerinde risk deฤerlendirmesi yapan yeni epidemiyolojik รงalฤฑลmalarฤฑ da iรงermektedir ve aynฤฑ zamanda meme kanseri riskini deฤiลtirmeyi amaรงlayan รถnceki ve devam eden diyet mรผdahale denemelerinin รถzetini sunmaktadฤฑr. SONUรLAR: Mevcut literatรผr, dรผลรผk yaฤ ve yรผksek lifli diyetlerin meme kanseri karลฤฑsฤฑnda zayฤฑf koruyucu olabileceฤini, toplam enerji alฤฑmฤฑnฤฑn ve alkolรผn ise pozitif bir iliลki gรถsterdiฤini รถne sรผrmektedir. Lif, muhtemelen รถstrojen modรผlasyonu yoluyla koruyucu olabilirken, mey...</code> |
| <code>Memeli Kanser Hรผcreleri Kolesterolden Beslenir.</code> | <code>Fitoestrojenler, estrojenlerle yapฤฑsal olarak benzer ve estrojenik/antiestrojenik รถzellikleri taklit ederek meme kanseri riskini etkileyebilirler. Batฤฑlฤฑ toplumlarda, tam tahฤฑllar ve muhtemelen soya gฤฑdalarฤฑ fitoestrojenlerin zengin kaynaklarฤฑdฤฑr. Alman menopoz sonrasฤฑ kadฤฑnlarda yapฤฑlan bir nรผfus tabanlฤฑ vaka-kontrol รงalฤฑลmasฤฑ, fitoestrojen bakฤฑmฤฑndan zengin gฤฑdalar ve diyet lignanlarla meme kanseri riski arasฤฑndaki iliลkiyi deฤerlendirmek iรงin kullanฤฑldฤฑ. Diyet verileri, 2.884 vaka ve 5.509 kontrol iรงin geรงerli bir gฤฑda sฤฑklฤฑฤฤฑ anketini kullanarak toplandฤฑ, bu anket fitoestrojen bakฤฑmฤฑndan zengin gฤฑdalar hakkฤฑnda ek sorular iรงeriyordu. ฤฐliลkiler, koลullu lojistik regresyon kullanฤฑlarak deฤerlendirildi. Tรผm analizler, ilgili risk ve karฤฑลtฤฑrฤฑcฤฑ faktรถrler iรงin ayarlandฤฑ. Politomlu lojistik regresyon analizi, estrojen reseptรถrรผ (ER) durumuna gรถre iliลkileri deฤerlendirmek iรงin yapฤฑldฤฑ. Soya fasulyesi, gรผneล รงiรงek tohumu ve kabak tohumu tรผketiminin yรผksek ve dรผลรผk seviyeleri, tรผketilmeme...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>multinli-tr</summary>
#### multinli-tr
* Dataset: [multinli-tr](https://huggingface.co/datasets/selmanbaysan/multinli_tr_fine_tuning_dataset) at [a700b72](https://huggingface.co/datasets/selmanbaysan/multinli_tr_fine_tuning_dataset/tree/a700b72da7056aa52ceb234d2e8a211d035dc2c7)
* Size: 392,702 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 4 tokens</li><li>mean: 26.79 tokens</li><li>max: 159 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 14.55 tokens</li><li>max: 56 tokens</li></ul> | <ul><li>0: ~65.70%</li><li>1: ~34.30%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------|:---------------|
| <code>Kavramsal olarak krem kaymaฤฤฑnฤฑn iki temel boyutu vardฤฑr - รผrรผn ve coฤrafya.</code> | <code>รrรผn ve coฤrafya krem kaymaฤฤฑnฤฑ iลe yarฤฑyor.</code> | <code>0</code> |
| <code>Mevsim boyunca ve sanฤฑrฤฑm senin seviyendeyken onlarฤฑ bir sonraki seviyeye dรผลรผrรผrsรผn. Eฤer ebeveyn takฤฑmฤฑnฤฑ รงaฤฤฑrmaya karar verirlerse Braves รผรงlรผ A'dan birini รงaฤฤฑrmaya karar verirlerse รงifte bir adam onun yerine geรงmeye gider ve bekar bir adam gelir.</code> | <code>Eฤer insanlar hatฤฑrlarsa, bir sonraki seviyeye dรผลersin.</code> | <code>1</code> |
| <code>Numaramฤฑzdan biri talimatlarฤฑnฤฑzฤฑ birazdan yerine getirecektir.</code> | <code>Ekibimin bir รผyesi emirlerinizi bรผyรผk bir hassasiyetle yerine getirecektir.</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
</details>
<details><summary>snli-tr</summary>
#### snli-tr
* Dataset: [snli-tr](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset) at [63eb107](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset/tree/63eb107dfdaf0b16cfd209db25705f27f2e5e2ca)
* Size: 550,152 training samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.7 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.61 tokens</li><li>max: 31 tokens</li></ul> | <ul><li>0: ~66.60%</li><li>1: ~33.40%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:----------------------------------------------------------------|:----------------------------------------------------------|:---------------|
| <code>Attaki bir kiลi, bozuk bir uรงaฤฤฑn รผzerinden atlar.</code> | <code>Bir kiลi atฤฑnฤฑ yarฤฑลma iรงin eฤitiyor.</code> | <code>0</code> |
| <code>Attaki bir kiลi, bozuk bir uรงaฤฤฑn รผzerinden atlar.</code> | <code>Bir kiลi bir lokantada omlet sipariล ediyor.</code> | <code>0</code> |
| <code>Attaki bir kiลi, bozuk bir uรงaฤฤฑn รผzerinden atlar.</code> | <code>Bir kiลi aรงฤฑk havada, at รผzerinde.</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
</details>
<details><summary>stsb-tr</summary>
#### stsb-tr
* Dataset: [stsb-tr](https://huggingface.co/datasets/selmanbaysan/stsb-tr) at [3d2e87d](https://huggingface.co/datasets/selmanbaysan/stsb-tr/tree/3d2e87d2a94c9af130b87ab8ed8d0c5c2e92e2df)
* Size: 5,749 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:--------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 9.9 tokens</li><li>max: 25 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 9.86 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.23</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:-------------------------------------------------------------------|:--------------------------------------------------------------------------------|:-----------------|
| <code>Bir uรงak kalkฤฑyor.</code> | <code>Bir hava uรงaฤฤฑ kalkฤฑyor.</code> | <code>5.0</code> |
| <code>Bir adam bรผyรผk bir flรผt รงalฤฑyor.</code> | <code>Bir adam flรผt รงalฤฑyor.</code> | <code>3.8</code> |
| <code>Bir adam pizzanฤฑn รผzerine rendelenmiล peynir seriyor.</code> | <code>Bir adam piลmemiล bir pizzanฤฑn รผzerine rendelenmiล peynir seriyor.</code> | <code>3.8</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>wmt16</summary>
#### wmt16
* Dataset: [wmt16](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset) at [9fc4e73](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset/tree/9fc4e7334bdb195b396c41eed05b0dd447981ef3)
* Size: 205,756 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 35.55 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 46.88 tokens</li><li>max: 186 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------|
| <code>Kosova'nฤฑn รถzelleลtirme sรผreci bรผyรผteรง altฤฑnda</code> | <code>Kosovo's privatisation process is under scrutiny</code> |
| <code>Kosova, tekrar eden ลikayetler ฤฑลฤฑฤฤฑnda รถzelleลtirme sรผrecini incelemeye alฤฑyor.</code> | <code>Kosovo is taking a hard look at its privatisation process in light of recurring complaints.</code> |
| <code>Southeast European Times iรงin Priลtine'den Muhamet Brayลori'nin haberi -- 21/03/12</code> | <code>By Muhamet Brajshori for Southeast European Times in Pristina -- 21/03/12</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
### Evaluation Datasets
<details><summary>msmarco-tr</summary>
#### msmarco-tr
* Dataset: [msmarco-tr](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset) at [f03d837](https://huggingface.co/datasets/selmanbaysan/msmarco-tr_fine_tuning_dataset/tree/f03d83704e5ea276665384ca6d8bee3b19632c80)
* Size: 31,540 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 9.88 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 82.48 tokens</li><li>max: 149 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Brian Patrick Carroll kimdir?</code> | <code>Buckethead Biyografisi. Brian Patrick Carroll (13 Mayฤฑs 1969 doฤumlu), profesyonel olarak Buckethead olarak bilinen, birรงok mรผzik tรผrรผnde รงalฤฑลmฤฑล Amerikalฤฑ gitarist ve รงoklu enstrรผmantalist. 265 stรผdyo albรผmรผ, dรถrt รถzel sรผrรผm ve bir EP yayฤฑnladฤฑ. Ayrฤฑca diฤer sanatรงฤฑlar tarafฤฑndan 50'den fazla albรผmde seslendirdi.</code> |
| <code>zolpidem bir benzodiazepin</code> | <code>Zolpidem (Ambien), imidazopiridin sฤฑnฤฑfฤฑnฤฑn bir benzodiazepin olmayan hipnotikidir. Bu ilaรง, benzodiazepin omega-1 reseptรถrรผne (seรงici olmayan omega-1, 2 ve 3 reseptรถr alt tiplerine baฤlanan diฤer benzodiazepinlerin aksine) รงok seรงici bir ลekilde baฤlanฤฑr, klorรผr kanalฤฑnฤฑn aรงฤฑlma sฤฑklฤฑฤฤฑnฤฑ arttฤฑrฤฑr.</code> |
| <code>roti'de kalori</code> | <code>1 porsiyon Roti Akdeniz Izgara Tavuk Roti'de 257 kalori vardฤฑr. Kalori daฤฤฑlฤฑmฤฑ: %47 yaฤ, %0 karbonhidrat, %53 protein.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>fiqa-tr</summary>
#### fiqa-tr
* Dataset: [fiqa-tr](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset) at [bbc9e91](https://huggingface.co/datasets/selmanbaysan/fiqa-tr_fine_tuning_dataset/tree/bbc9e91b5710d0ac4032b5c9e94066470f928c8c)
* Size: 1,238 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.43 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 8 tokens</li><li>mean: 219.48 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Bir geliri olmayan iลletme iรงin iลletme giderlerini talep etmek.</code> | <code>Evet, henรผz herhangi bir gelir elde etmiyorsanฤฑz iลletme indirimleri talep edebilirsiniz. Ancak รถncelikle iลletmeniz iรงin hangi yapฤฑyฤฑ tercih edeceฤinize karar vermelisiniz. Ya bir ลirket Yapฤฑsฤฑ ya da Tek Sahiplik veya Ortaklฤฑk. ลirket Yapฤฑsฤฑ Eฤer bir ลirket Yapฤฑsฤฑ (kurulumu daha pahalฤฑ olan) seรงerseniz, indirimleri talep edebilirsiniz ancak geliriniz yok. Bu nedenle iลletmeniz bir zarara uฤrar ve bu zararฤฑ, iลletmenizden elde ettiฤiniz gelirin giderlerinizi aลana kadar devam ettirirsiniz. Bu zararlar ลirketin iรงinde kalฤฑr ve gelecek gelir yฤฑllarฤฑnda kรขrlarฤฑnฤฑzฤฑ dengelemek iรงin devralฤฑnabilir. Daha fazla bilgi iรงin ATO - ลirket Vergi Zararlarฤฑ'na bakฤฑn. Tek Sahiplik veya Ortaklฤฑk Yapฤฑsฤฑ Eฤer Tek Sahiplik veya Ortaklฤฑk olarak seรงerseniz ve iลletmeniz bir zarara uฤrarsa, bu zararฤฑ diฤer kaynaklardan elde ettiฤiniz gelire karลฤฑ dengeleyip dengeleyemeyeceฤinizi kontrol etmeniz gerekir. ฤฐลletmenizin diฤer gelirinize karลฤฑ zararฤฑnฤฑ dengelemek iรงin aลaฤฤฑdaki testlerden birini geรงmeniz gerekir...</code> |
| <code>Bir iลletme kontrol hesabฤฑndan baลka bir iลletme kontrol hesabฤฑna para aktarma.</code> | <code>"Her iki iลletme iรงin ayrฤฑ dosyalar olmalฤฑdฤฑr. Para transferi yapan iลletme, QB dosyasฤฑnda ""รงeki yaz"" olmalฤฑdฤฑr. Para alan iลletme, QB dosyasฤฑnda ""banka yatฤฑrฤฑmฤฑ"" yapmalฤฑdฤฑr. (QB'de, รถdemeyi ACH gibi baลka bir yรถntemle yaptฤฑฤฤฑnฤฑzda bile, ""รงeki yaz"" demeniz gerekir.) Hiรงbir iลletme, diฤerinin banka hesaplarฤฑnฤฑ aรงฤฑkรงa temsil etmemelidir. Her iki tarafta da, รถdemenin hangi baลka hesaptan geldiฤi/gittiฤi konusunda sฤฑnฤฑflandฤฑrmanฤฑz gerekecektir - Bunun doฤru olup olmadฤฑฤฤฑnฤฑ bilmek iรงin, parayฤฑ neden transfer ettiฤinizi ve kitaplarฤฑnฤฑzฤฑ nasฤฑl kurduฤunuzu bilmeniz gerekir. Sanฤฑrฤฑm bu, burada uygun/mรผmkรผn olan konunun รถtesindedir. Kiลisel hesabฤฑnฤฑzdan iลinize para aktarmak, muhtemelen ortaklฤฑk sermayesi demektir, eฤer baลka bir ลey yoksa. รrneฤin, S Corp'ta kendinize bir maaล รถdemelisiniz. Eฤer yanlฤฑลlฤฑkla fazla รถderseniz, o zaman kiลisel hesabฤฑnฤฑzdan ลirketi geri bir รงek yazฤฑp hatayฤฑ dรผzeltirsiniz. Bu ortaklฤฑk sermayesi deฤil, muhtemelen maaล รถdemelerini takip eden baลka bir hesaptaki b...</code> |
| <code>ฤฐล/yatฤฑrฤฑm iรงin ayrฤฑ bir banka hesabฤฑnฤฑz var mฤฑ, ama "iล hesabฤฑ" deฤil mi?</code> | <code>"ฤฐล iรงin ayrฤฑ bir kontrol hesabฤฑ aรงmak mantฤฑklฤฑdฤฑr. Gelir/giderlerinizi belgelemek daha basittir. Hesaba giren ve รงฤฑkan her dolarฤฑ aรงฤฑklayabilirsiniz, bunlardan bazฤฑlarฤฑnฤฑn iลle alakasฤฑz olduฤunu hatฤฑrlamanฤฑza gerek kalmadan. Kredi birliฤi, ikinci bir kontrol hesabฤฑ aรงmama ve รงeklerin รผzerine istediฤim herhangi bir ad koymama izin verdi. Bu, รงeklerin รผzerine adฤฑmฤฑn yazฤฑlmasฤฑ yerine daha iyi gรถrรผnรผyordu. Yatฤฑrฤฑmlar iรงin ayrฤฑ bir kontrol hesabฤฑna ihtiyaรง gรถrmรผyorum. Parayฤฑ, herhangi bir รผcret olmayan ve hatta biraz faiz kazandฤฑrabilen ayrฤฑ bir tasarruf hesabฤฑnda tutabilirsiniz. Ayda รงok sayฤฑda yatฤฑrฤฑm iลlemi yapmadฤฑฤฤฑnฤฑz sรผrece bu benim iรงin iลe yaradฤฑ. Bu ลekilde IRA'larฤฑ ve 529 planlarฤฑnฤฑ finanse ediyorum. Ayda 4-5 kez maaล alฤฑyoruz, ancak her ay fonlara para gรถnderiyoruz. ฤฐลlem sayฤฑsฤฑ bรผyรผdรผkรงe bir iล hesabฤฑna ihtiyacฤฑnฤฑz olacak. Bankaya her seferinde onlarca รงek yatฤฑrฤฑyorsanฤฑz, banka sizi iล hesabฤฑna geรงmeye yรถnlendirecektir."</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>quora-tr</summary>
#### quora-tr
* Dataset: [quora-tr](https://huggingface.co/datasets/selmanbaysan/quora-tr_fine_tuning_dataset) at [6e1eee1](https://huggingface.co/datasets/selmanbaysan/quora-tr_fine_tuning_dataset/tree/6e1eee1e44db0f777eceb1f9b55293a9c2e25d76)
* Size: 7,626 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.29 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.47 tokens</li><li>max: 47 tokens</li></ul> |
* Samples:
| anchor | positive |
|:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------|
| <code>Quora'yฤฑ bir moderatรถrรผn gรถzรผnden nasฤฑl gรถrรผrsรผnรผz?</code> | <code>Quora web sitesi, Quora moderasyon รผyelerine nasฤฑl gรถrรผnรผr?</code> |
| <code>Nasฤฑl hayatฤฑmฤฑn yapmam gereken farklฤฑ ลeyler arasฤฑnda seรงim yapmamayฤฑ reddedebilirim?</code> | <code>Hayatta birรงok farklฤฑ ลeyi takip etmek mรผmkรผn mรผ?</code> |
| <code>Ben Affleck Batman'de Christian Bale'den daha parlak mฤฑydฤฑ?</code> | <code>Sizce, Batman performansฤฑnda kim daha iyiydi: Christian Bale mi yoksa Ben Affleck mi?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>nfcorpus-tr</summary>
#### nfcorpus-tr
* Dataset: [nfcorpus-tr](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset) at [22d1ef8](https://huggingface.co/datasets/selmanbaysan/nfcorpus-tr_fine_tuning_dataset/tree/22d1ef8b6a9f1c196d1977541a66ca8eff946f06)
* Size: 11,385 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 11.41 tokens</li><li>max: 20 tokens</li></ul> | <ul><li>min: 67 tokens</li><li>mean: 375.07 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Derin Kฤฑzartฤฑlmฤฑล Gฤฑdalarฤฑn Kanser Yapabileceฤi Nedenler</code> | <code>Arka plan: Akrilamid, insan plasentasฤฑnฤฑ geรงen yaygฤฑn bir diyetik maruziyettir. Muhtemel insan kanserojen olarak sฤฑnฤฑflandฤฑrฤฑlฤฑr ve farelerde geliลimsel toksisite gรถzlemlenmiลtir. Amaรงlar: Bu รงalฤฑลmada, akrilamid รถn doฤum maruziyeti ile doฤum sonuรงlarฤฑnฤฑn iliลkilerini inceledik. Yรถntemler: Akrilamidin ve metaboliti glisidamidin hemoglobin (Hb) adductlarฤฑ, 2006-2010 yฤฑllarฤฑ arasฤฑnda Danimarka, ฤฐngiltere, Yunanistan, Norveรง ve ฤฐspanya'da iลe alฤฑnan 1.101 tek รงocuklu hamile kadฤฑnlarฤฑn kordon kanฤฑnda (hamileliฤin son aylarฤฑnda birikmiล maruziyeti yansฤฑtan) รถlรงรผldรผ. Anne diyetleri, gฤฑda sฤฑklฤฑฤฤฑ anketleri aracฤฑlฤฑฤฤฑyla tahmin edildi. Sonuรงlar: Hem akrilamid hem de glisidamid Hb adductlarฤฑ, doฤum aฤฤฑrlฤฑฤฤฑ ve baล รงevresi iรงin istatistiksel olarak anlamlฤฑ bir azalma ile iliลkiliydi. En yรผksek ve en dรผลรผk รงeyrekte akrilamid Hb adduct seviyeleri arasฤฑndaki tahmin edilen doฤum aฤฤฑrlฤฑฤฤฑ farkฤฑ, gestasyonel yaล ve รผlke ayarlamalarฤฑndan sonra โ132 g (95% CI: โ207, โ56) idi; baล รงevresi iรงin karลฤฑlฤฑk ge...</code> |
| <code>Derin Kฤฑzartฤฑlmฤฑล Gฤฑdalarฤฑn Kanser Yapabileceฤi Nedenler</code> | <code>ฤฐnsanlar, patates kฤฑzartmasฤฑ ve diฤer yiyecekler yoluyla akrilamid (AA) maruziyeti, potansiyel bir saฤlฤฑk endiลesi olarak kabul edilmiลtir. Burada, piลirme sฤฑcaklฤฑฤฤฑ ve sรผresi gibi iki en etkili faktรถre dayalฤฑ istatistiksel bir doฤrusal olmayan regresyon modeli kullanarak, patates kฤฑzartmalarฤฑnda AA konsantrasyonlarฤฑnฤฑ tahmin ettik. Tahmin modeli iรงin R(2) deฤeri 0.83, geliลtirilmiล modelin รถnemli ve geรงerli olduฤunu gรถstermektedir. Bu รงalฤฑลmada yapฤฑlan patates kฤฑzartmasฤฑ tรผketimi anket verileri ve sekiz farklฤฑ kฤฑzartma sฤฑcaklฤฑฤฤฑ-zaman ลemasฤฑ, lezzetli ve gรถrsel aรงฤฑdan รงekici patates kฤฑzartmalarฤฑ รผretebildiฤi iรงin, Monte Carlo simรผlasyon sonuรงlarฤฑ, AA konsantrasyonunun 168 ppb'den yรผksek olduฤu takdirde, Taichung ลehri'ndeki 13-18 yaล arasฤฑ ergenlerin tahmin edilen kanser riski, sadece bu sฤฑnฤฑrlฤฑ yaลam sรผresi gรถz รถnรผne alฤฑndฤฑฤฤฑnda, hedef aลฤฑlan รถmรผr boyu kanser riski (ELCR) deฤerini aลacaktฤฑr. AA alฤฑmฤฑyla iliลkili kanser riskini azaltmak iรงin, patates kฤฑzartmalarฤฑndaki AA seviyelerinin...</code> |
| <code>Derin Kฤฑzartฤฑlmฤฑล Gฤฑdalarฤฑn Kanser Yapabileceฤi Nedenler</code> | <code>ARKA PLAN: Yaygฤฑn olarak tรผketilen gฤฑdalar, รถrneฤin patates kฤฑzartmasฤฑ, patates cipsi veya tahฤฑllar gibi รผrรผnlerde nispeten yรผksek akrilamid konsantrasyonlarฤฑ, insan saฤlฤฑฤฤฑ iรงin potansiyel bir risk oluลturabilir.<br><br>HEDEF: Bu pilot รงalฤฑลmanฤฑn amacฤฑ, kronik akrilamid iรงeren patates cipsi alฤฑmฤฑnฤฑn oksidatif stres veya iltihapla olasฤฑ baฤlantฤฑsฤฑnฤฑ araลtฤฑrmaktฤฑ.<br><br>Tasarฤฑm: 14 saฤlฤฑklฤฑ gรถnรผllรผ (ortalama yaล: 35; 8 kadฤฑn ve 6 gรผnde 20 sigaradan fazla sigara iรงen) 4 hafta boyunca gรผnde 160 gram akrilamid iรงeren 157 mikrogram (dรผzeltilmiล) akrilamid iรงeren patates cipsi aldฤฑ.<br><br>Sonuรง: รalฤฑลmanฤฑn tรผm katฤฑlฤฑmlarฤฑnda kan akrilamid-hemoglobin baฤlarฤฑmฤฑnda artฤฑล bulundu, ortalama 43.1 pmol/L(-1)/g(-1) hemoglobin (aralฤฑk: 27-76; P < 0.01) sigara iรงmeyenlerde ve 59.0 pmol/L(-1)/g(-1) hemoglobin (aralฤฑk: 43-132; P < 0.05) sigara iรงenlerde. Aynฤฑ zamanda, hem sigara iรงenlerde hem de sigara iรงmeyenlerde okside LDL, yรผksek duyarlฤฑlฤฑk interleukin-6, yรผksek duyarlฤฑlฤฑk C-reaktif protein ve gama-glutamiltransfer...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
<details><summary>snli-tr</summary>
#### snli-tr
* Dataset: [snli-tr](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset) at [63eb107](https://huggingface.co/datasets/selmanbaysan/snli_tr_fine_tuning_dataset/tree/63eb107dfdaf0b16cfd209db25705f27f2e5e2ca)
* Size: 10,000 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 18.69 tokens</li><li>max: 53 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.29 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>0: ~67.50%</li><li>1: ~32.50%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|:---------------|
| <code>Paketlere gitmek iรงin tutunurken iki kadฤฑn kucaklaลฤฑyor.</code> | <code>Kฤฑz kardeลler sadece รถฤle yemeฤi yedikten sonra paketleri gitmek iรงin tutarken elveda sarฤฑlฤฑyorlar.</code> | <code>0</code> |
| <code>Paketlere gitmek iรงin tutunurken iki kadฤฑn kucaklaลฤฑyor.</code> | <code>ฤฐki kadฤฑn paket tutuyor.</code> | <code>1</code> |
| <code>Paketlere gitmek iรงin tutunurken iki kadฤฑn kucaklaลฤฑyor.</code> | <code>Adamlar bir ลarkรผterinin dฤฑลฤฑnda kavga ediyorlar.</code> | <code>0</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
</details>
<details><summary>xnli-tr</summary>
#### xnli-tr
* Dataset: [xnli-tr](https://huggingface.co/datasets/selmanbaysan/xnli_tr_fine_tuning_dataset) at [3a66bc8](https://huggingface.co/datasets/selmanbaysan/xnli_tr_fine_tuning_dataset/tree/3a66bc878d3d027177da71f47e4d8dee21cafe63)
* Size: 2,490 evaluation samples
* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | premise | hypothesis | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 22.57 tokens</li><li>max: 67 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.12 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>0: ~66.70%</li><li>1: ~33.30%</li></ul> |
* Samples:
| premise | hypothesis | label |
|:------------------------------------|:---------------------------------------------------------------|:---------------|
| <code>Ve Anne, evdeyim dedi.</code> | <code>Okul servisi onu bฤฑrakฤฑr bฤฑrakmaz annesini aradฤฑ.</code> | <code>0</code> |
| <code>Ve Anne, evdeyim dedi.</code> | <code>Bir kelime sรถylemedi.</code> | <code>0</code> |
| <code>Ve Anne, evdeyim dedi.</code> | <code>Annesine eve gittiฤini sรถyledi.</code> | <code>1</code> |
* Loss: [<code>SoftmaxLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#softmaxloss)
</details>
<details><summary>stsb-tr</summary>
#### stsb-tr
* Dataset: [stsb-tr](https://huggingface.co/datasets/selmanbaysan/stsb-tr) at [3d2e87d](https://huggingface.co/datasets/selmanbaysan/stsb-tr/tree/3d2e87d2a94c9af130b87ab8ed8d0c5c2e92e2df)
* Size: 1,500 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | score |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 5 tokens</li><li>mean: 15.37 tokens</li><li>max: 58 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.41 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 2.1</li><li>max: 5.0</li></ul> |
* Samples:
| sentence1 | sentence2 | score |
|:----------------------------------------------|:-----------------------------------------------|:------------------|
| <code>Kasklฤฑ bir adam dans ediyor.</code> | <code>Baret giyen bir adam dans ediyor.</code> | <code>5.0</code> |
| <code>Kรผรงรผk bir รงocuk ata biniyor.</code> | <code>Bir รงocuk ata biniyor.</code> | <code>4.75</code> |
| <code>Bir adam fareyi yฤฑlana besliyor.</code> | <code>Adam yฤฑlana fare besliyor.</code> | <code>5.0</code> |
* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "pairwise_cos_sim"
}
```
</details>
<details><summary>wmt16</summary>
#### wmt16
* Dataset: [wmt16](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset) at [9fc4e73](https://huggingface.co/datasets/selmanbaysan/wmt16_en_tr_fine_tuning_dataset/tree/9fc4e7334bdb195b396c41eed05b0dd447981ef3)
* Size: 1,001 evaluation samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 26.96 tokens</li><li>max: 149 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 39.33 tokens</li><li>max: 240 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------|
| <code>Norveรง'in rakfisk'i: Dรผnyanฤฑn en kokulu balฤฑฤฤฑ bu mu?</code> | <code>Norway's rakfisk: Is this the world's smelliest fish?</code> |
| <code>Norveรง'in beล milyon insanฤฑ en yรผksek yaลam standartlarฤฑnฤฑn tadฤฑnฤฑ รงฤฑkarฤฑyor, sadece Avrupa'da deฤil, dรผnyada.</code> | <code>Norway's five million people enjoy one of the highest standards of living, not just in Europe, but in the world.</code> |
| <code>รlkenin baลarฤฑsฤฑnฤฑn sฤฑrrฤฑ aลฤฑrฤฑ kokulu bazฤฑ balฤฑklara olan yerel iลtahla baฤlantฤฑlฤฑ olabilir mi?</code> | <code>Could the secret of the country's success be connected to the local appetite for some exceedingly smelly fish?</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | msmarco-tr loss | fiqa-tr loss | quora-tr loss | nfcorpus-tr loss | snli-tr loss | xnli-tr loss | stsb-tr loss | wmt16 loss | stsb-tr_spearman_cosine | snli-tr_cosine_ap | xnli-tr_cosine_ap | wmt16_cosine_ap | msmarco-tr_cosine_ap | fiqa-tr_cosine_ap | quora-tr_cosine_ap | nfcorpus-tr_cosine_ap |
|:------:|:-----:|:-------------:|:---------------:|:------------:|:-------------:|:----------------:|:------------:|:------------:|:------------:|:----------:|:-----------------------:|:-----------------:|:-----------------:|:---------------:|:--------------------:|:-----------------:|:------------------:|:---------------------:|
| 0.0209 | 500 | 1.1414 | 0.2698 | 1.9305 | 0.1019 | 1.1507 | 0.5826 | 0.6343 | 11.1465 | 0.9888 | 0.7727 | 0.4918 | 0.4731 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0417 | 1000 | 0.8222 | 0.2270 | 1.8603 | 0.0904 | 1.0909 | 0.4977 | 0.5921 | 11.8077 | 0.6025 | 0.7783 | 0.4928 | 0.5208 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0626 | 1500 | 0.7991 | 0.1968 | 1.7750 | 0.0879 | 1.0778 | 0.4483 | 0.5314 | 12.2621 | 0.5532 | 0.7865 | 0.5174 | 0.5560 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0835 | 2000 | 0.7775 | 0.1903 | 1.7956 | 0.0809 | 1.0444 | 0.4053 | 0.4841 | 12.3953 | 0.5244 | 0.7909 | 0.5311 | 0.5875 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.1043 | 2500 | 0.6397 | 0.1779 | 1.7774 | 0.0772 | 1.0337 | 0.3985 | 0.4661 | 11.9094 | 0.4421 | 0.7853 | 0.5702 | 0.5917 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.1252 | 3000 | 0.7237 | 0.1658 | 1.6839 | 0.0768 | 1.0342 | 0.3646 | 0.4684 | 12.1233 | 0.3539 | 0.8049 | 0.5745 | 0.5949 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.1461 | 3500 | 0.5485 | 0.1603 | 1.7514 | 0.0811 | 1.0235 | 0.3626 | 0.4589 | 12.7109 | 0.2967 | 0.7867 | 0.5985 | 0.6128 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.1669 | 4000 | 0.6076 | 0.1513 | 1.6503 | 0.0723 | 1.0043 | 0.3555 | 0.4829 | 12.5178 | 0.2867 | 0.8049 | 0.5692 | 0.6019 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.1878 | 4500 | 0.5363 | 0.1478 | 1.6573 | 0.0696 | 1.0147 | 0.3448 | 0.4759 | 12.7022 | 0.2406 | 0.8042 | 0.5886 | 0.6023 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.2087 | 5000 | 0.5543 | 0.1453 | 1.6171 | 0.0664 | 1.0054 | 0.3384 | 0.4634 | 12.3955 | 0.2647 | 0.8116 | 0.5896 | 0.6107 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.2295 | 5500 | 0.6203 | 0.1455 | 1.6107 | 0.0710 | 0.9872 | 0.3333 | 0.4487 | 12.1924 | 0.2382 | 0.8110 | 0.5837 | 0.6206 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.2504 | 6000 | 0.6368 | 0.1354 | 1.5559 | 0.0704 | 1.0105 | 0.3236 | 0.4455 | 12.4101 | 0.2259 | 0.8102 | 0.5998 | 0.6200 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.2713 | 6500 | 0.568 | 0.1366 | 1.5891 | 0.0701 | 0.9898 | 0.3206 | 0.4292 | 11.9028 | 0.2035 | 0.8066 | 0.5866 | 0.6038 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.2921 | 7000 | 0.6087 | 0.1300 | 1.5420 | 0.0671 | 0.9914 | 0.3175 | 0.4247 | 12.2175 | 0.2220 | 0.8112 | 0.5902 | 0.6183 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3130 | 7500 | 0.5987 | 0.1233 | 1.5577 | 0.0622 | 0.9914 | 0.3205 | 0.4559 | 12.4562 | 0.1855 | 0.8126 | 0.6083 | 0.6184 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3339 | 8000 | 0.5158 | 0.1231 | 1.5156 | 0.0653 | 0.9854 | 0.3145 | 0.4436 | 12.3801 | 0.1826 | 0.8107 | 0.6016 | 0.6073 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3547 | 8500 | 0.5475 | 0.1208 | 1.4804 | 0.0631 | 0.9754 | 0.3140 | 0.4662 | 12.8113 | 0.1841 | 0.8152 | 0.5904 | 0.6142 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3756 | 9000 | 0.638 | 0.1270 | 1.5346 | 0.0652 | 0.9691 | 0.3112 | 0.4162 | 12.2037 | 0.1964 | 0.8092 | 0.6004 | 0.6281 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3965 | 9500 | 0.594 | 0.1203 | 1.4953 | 0.0637 | 0.9715 | 0.3152 | 0.4756 | 12.7237 | 0.1561 | 0.8068 | 0.6040 | 0.6185 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.4173 | 10000 | 0.5815 | 0.1169 | 1.4600 | 0.0595 | 0.9748 | 0.3115 | 0.4733 | 12.7256 | 0.1669 | 0.8101 | 0.6085 | 0.6156 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.4382 | 10500 | 0.6099 | 0.1168 | 1.4518 | 0.0667 | 0.9748 | 0.3025 | 0.4319 | 12.5545 | 0.1821 | 0.8183 | 0.6135 | 0.6232 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.4591 | 11000 | 0.5243 | 0.1152 | 1.4609 | 0.0636 | 0.9691 | 0.3086 | 0.4409 | 12.5561 | 0.1717 | 0.8120 | 0.6114 | 0.6269 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.4799 | 11500 | 0.5788 | 0.1149 | 1.4629 | 0.0629 | 0.9603 | 0.3007 | 0.4444 | 12.4325 | 0.1740 | 0.8137 | 0.6077 | 0.6266 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.5008 | 12000 | 0.4322 | 0.1116 | 1.4600 | 0.0623 | 0.9684 | 0.2992 | 0.4232 | 12.5360 | 0.1757 | 0.8160 | 0.6045 | 0.6234 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.5217 | 12500 | 0.6227 | 0.1106 | 1.4774 | 0.0635 | 0.9649 | 0.2966 | 0.4272 | 12.3037 | 0.1568 | 0.8193 | 0.6061 | 0.6220 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.5425 | 13000 | 0.5269 | 0.1087 | 1.4653 | 0.0627 | 0.9646 | 0.2946 | 0.4262 | 12.4239 | 0.1672 | 0.8172 | 0.6154 | 0.6240 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.5634 | 13500 | 0.5462 | 0.1076 | 1.4482 | 0.0613 | 0.9608 | 0.2950 | 0.4061 | 12.3639 | 0.1669 | 0.8134 | 0.6289 | 0.6340 | 1.0 | 1.0000 | 1.0 | 1.0 | 1.0 |
| 0.5843 | 14000 | 0.5737 | 0.1069 | 1.4330 | 0.0594 | 0.9715 | 0.2934 | 0.4241 | 12.5838 | 0.1553 | 0.8162 | 0.6060 | 0.6201 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.6052 | 14500 | 0.5542 | 0.1062 | 1.4318 | 0.0630 | 0.9627 | 0.2936 | 0.4252 | 12.4879 | 0.1537 | 0.8135 | 0.6182 | 0.6211 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.6260 | 15000 | 0.4828 | 0.1014 | 1.4022 | 0.0620 | 0.9721 | 0.2916 | 0.4162 | 12.5052 | 0.1578 | 0.8200 | 0.6130 | 0.6307 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.6469 | 15500 | 0.585 | 0.1030 | 1.4268 | 0.0662 | 0.9565 | 0.2915 | 0.4235 | 12.4999 | 0.1483 | 0.8166 | 0.5973 | 0.6245 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.6678 | 16000 | 0.5699 | 0.1036 | 1.4218 | 0.0636 | 0.9622 | 0.2883 | 0.4099 | 12.4195 | 0.1516 | 0.8182 | 0.6130 | 0.6315 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.6886 | 16500 | 0.5372 | 0.1003 | 1.4122 | 0.0619 | 0.9671 | 0.2905 | 0.4249 | 12.5527 | 0.1545 | 0.8164 | 0.6119 | 0.6304 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.7095 | 17000 | 0.5473 | 0.1021 | 1.4109 | 0.0636 | 0.9602 | 0.2848 | 0.4097 | 12.3792 | 0.1640 | 0.8157 | 0.6171 | 0.6351 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.7304 | 17500 | 0.5665 | 0.0985 | 1.3868 | 0.0604 | 0.9616 | 0.2845 | 0.4083 | 12.4513 | 0.1523 | 0.8195 | 0.6044 | 0.6306 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.7512 | 18000 | 0.498 | 0.0985 | 1.3925 | 0.0606 | 0.9605 | 0.2828 | 0.4068 | 12.4254 | 0.1544 | 0.8177 | 0.6082 | 0.6351 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.7721 | 18500 | 0.5037 | 0.0975 | 1.3748 | 0.0570 | 0.9619 | 0.2891 | 0.4256 | 12.5656 | 0.1451 | 0.8158 | 0.6072 | 0.6294 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.7930 | 19000 | 0.4885 | 0.0966 | 1.3742 | 0.0612 | 0.9600 | 0.2818 | 0.3993 | 12.4406 | 0.1473 | 0.8170 | 0.6096 | 0.6349 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.8138 | 19500 | 0.5042 | 0.0966 | 1.3817 | 0.0589 | 0.9621 | 0.2822 | 0.4181 | 12.5854 | 0.1476 | 0.8167 | 0.6186 | 0.6418 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.8347 | 20000 | 0.5453 | 0.0966 | 1.3790 | 0.0583 | 0.9553 | 0.2824 | 0.4112 | 12.5904 | 0.1451 | 0.8156 | 0.6100 | 0.6336 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.8556 | 20500 | 0.5351 | 0.0963 | 1.3810 | 0.0591 | 0.9588 | 0.2844 | 0.4211 | 12.7160 | 0.1445 | 0.8161 | 0.6129 | 0.6358 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.8764 | 21000 | 0.4601 | 0.0951 | 1.3744 | 0.0581 | 0.9573 | 0.2792 | 0.4059 | 12.5846 | 0.1448 | 0.8156 | 0.6193 | 0.6372 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.8973 | 21500 | 0.4698 | 0.0949 | 1.3757 | 0.0578 | 0.9576 | 0.2780 | 0.4020 | 12.6457 | 0.1417 | 0.8146 | 0.6180 | 0.6347 | 1.0 | 1.0000 | 1.0 | 1.0 | 1.0 |
| 0.9182 | 22000 | 0.4838 | 0.0930 | 1.3611 | 0.0564 | 0.9572 | 0.2781 | 0.4136 | 12.7198 | 0.1404 | 0.8157 | 0.6187 | 0.6348 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.9390 | 22500 | 0.5106 | 0.0931 | 1.3639 | 0.0569 | 0.9550 | 0.2791 | 0.4117 | 12.6323 | 0.1377 | 0.8168 | 0.6121 | 0.6328 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.9599 | 23000 | 0.6039 | 0.0929 | 1.3606 | 0.0568 | 0.9566 | 0.2797 | 0.4182 | 12.6569 | 0.1339 | 0.8167 | 0.6130 | 0.6332 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.9808 | 23500 | 0.5395 | 0.0928 | 1.3603 | 0.0575 | 0.9553 | 0.2783 | 0.4120 | 12.6136 | 0.1337 | 0.8168 | 0.6132 | 0.6338 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers and SoftmaxLoss
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
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## Model Card Authors
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|
Baoaboya/deepseek_sql_model
|
Baoaboya
| 2025-03-25T12:57:33Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T12:57:03Z
|
---
base_model: unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Baoaboya
- **License:** apache-2.0
- **Finetuned from model :** unsloth/deepseek-r1-distill-llama-8b-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ysn-rfd/Open-RS3-Q8_0-GGUF
|
ysn-rfd
| 2025-03-25T12:57:32Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"dataset:knoveleng/open-rs",
"dataset:knoveleng/open-s1",
"dataset:knoveleng/open-deepscaler",
"base_model:knoveleng/Open-RS3",
"base_model:quantized:knoveleng/Open-RS3",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-03-25T12:57:16Z
|
---
base_model: knoveleng/Open-RS3
datasets:
- knoveleng/open-rs
- knoveleng/open-s1
- knoveleng/open-deepscaler
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
inference: true
---
# ysn-rfd/Open-RS3-Q8_0-GGUF
This model was converted to GGUF format from [`knoveleng/Open-RS3`](https://huggingface.co/knoveleng/Open-RS3) 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/knoveleng/Open-RS3) 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 ysn-rfd/Open-RS3-Q8_0-GGUF --hf-file open-rs3-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo ysn-rfd/Open-RS3-Q8_0-GGUF --hf-file open-rs3-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 ysn-rfd/Open-RS3-Q8_0-GGUF --hf-file open-rs3-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo ysn-rfd/Open-RS3-Q8_0-GGUF --hf-file open-rs3-q8_0.gguf -c 2048
```
|
sergeyzh/rubert-mini-uncased-GGUF
|
sergeyzh
| 2025-03-25T12:57:17Z
| 0
| 0
|
transformers
|
[
"transformers",
"gguf",
"russian",
"pretraining",
"embeddings",
"tiny",
"feature-extraction",
"sentence-similarity",
"sentence-transformers",
"ru",
"en",
"dataset:IlyaGusev/gazeta",
"dataset:zloelias/lenta-ru",
"dataset:HuggingFaceFW/fineweb-2",
"dataset:HuggingFaceFW/fineweb",
"base_model:sergeyzh/rubert-mini-uncased",
"base_model:quantized:sergeyzh/rubert-mini-uncased",
"license:mit",
"region:us"
] |
sentence-similarity
| 2025-03-25T12:56:37Z
|
---
language:
- ru
- en
pipeline_tag: sentence-similarity
inference: false
library_name: transformers
tags:
- russian
- pretraining
- embeddings
- tiny
- feature-extraction
- sentence-similarity
- sentence-transformers
- transformers
datasets:
- IlyaGusev/gazeta
- zloelias/lenta-ru
- HuggingFaceFW/fineweb-2
- HuggingFaceFW/fineweb
license: mit
base_model: sergeyzh/rubert-mini-uncased
---
## rubert-mini-uncased-GGUF
ะัะธะณะธะฝะฐะปัะฝะฐั ะผะพะดะตะปั: [rubert-mini-uncased](https://huggingface.co/sergeyzh/rubert-mini-uncased)
ะะปั ะทะฐะฟััะบะฐ ะผะพะดะตะปะธ ะฒ ะบะฐัะตััะฒะต ัะตัะฒะตัะฐ ะฝะตะพะฑั
ะพะดะธะผะพ ะธัะฟะพะปัะทะพะฒะฐัั llama.cpp:
```bash
llama-server -m rubert-mini-uncased-q8_0.gguf -c 512 -ngl 99 --embedding --port 8080
```
ะะพะทะผะพะถะฝะพ ะธัะฟะพะปัะทะพะฒะฐะฝะธะต ั LM Studio.
## ะัะฟะพะปัะทะพะฒะฐะฝะธะต ะผะพะดะตะปะธ ะฟะพัะปะต ะทะฐะฟััะบะฐ llama-server:
```python
import numpy as np
import requests
import json
def embeding(text):
url = 'http://127.0.0.1:8080/v1/embeddings'
headers = {"Content-Type": "application/json", "Authorization": "no-key"}
data={"input": text,
"model": "rubert-mini-uncased",
"encoding_format": "float"}
r = requests.post(url, headers=headers, data=json.dumps(data))
emb = np.array([np.array(s['embedding']) for s in r.json()['data']])
return emb
inputs = [
#
"paraphrase: ะ ะฏัะพัะปะฐะฒัะบะพะน ะพะฑะปะฐััะธ ัะฐะทัะตัะธะปะธ ัะฐะฑะพัั ะฑะฐะฝั, ะฝะพ ะฑะตะท ะฟะพัะตัะธัะตะปะตะน",
"categorize_entailment: ะะตะฝัะธะฝั ะดะพััะฐะฒะธะปะธ ะฒ ะฑะพะปัะฝะธัั, ะทะฐ ะตะต ะถะธะทะฝั ัะตะนัะฐั ะฑะพััััั ะฒัะฐัะธ.",
"search_query: ะกะบะพะปัะบะพ ะฟัะพะณัะฐะผะผะธััะพะฒ ะฝัะถะฝะพ, ััะพะฑั ะฒะบัััะธัั ะปะฐะผะฟะพัะบั?",
#
"paraphrase: ะฏัะพัะปะฐะฒัะบะธะผ ะฑะฐะฝัะผ ัะฐะทัะตัะธะปะธ ัะฐะฑะพัะฐัั ะฑะตะท ะฟะพัะตัะธัะตะปะตะน",
"categorize_entailment: ะะตะฝัะธะฝั ัะฟะฐัะฐัั ะฒัะฐัะธ.",
"search_document: ะงัะพะฑั ะฒะบัััะธัั ะปะฐะผะฟะพัะบั, ััะตะฑัะตััั ััะธ ะฟัะพะณัะฐะผะผะธััะฐ: ะพะดะธะฝ ะฝะฐะฟะธัะตั ะฟัะพะณัะฐะผะผั ะธะทะฒะปะตัะตะฝะธั ะปะฐะผะฟะพัะบะธ, ะดััะณะพะน โ ะฒะบัััะธะฒะฐะฝะธั ะปะฐะผะฟะพัะบะธ, ะฐ ััะตัะธะน ะฟัะพะฒะตะดะตั ัะตััะธัะพะฒะฐะฝะธะต."
]
embeddings = embeding(inputs)
sim_scores = embeddings[:3] @ embeddings[3:].T
print(sim_scores.diagonal().tolist())
# [0.9365279201944358, 0.8030448289720129, 0.6826768729478850] - rubert-mini-uncased-f32
# [0.9365603574226906, 0.8029491439999603, 0.6826115652313832] - rubert-mini-uncased-f16
# [0.9362014453563489, 0.8019812246234975, 0.6823210638303931] - rubert-mini-uncased-q8_0
# [0.9366128444671631, 0.8030662536621094, 0.6826460957527161] - rubert-mini-uncased (torch)
# [0.9360030293464661, 0.8591322302818298, 0.7285830378532410] - FRIDA
```
|
yfarm01/sn29_mar25_c0
|
yfarm01
| 2025-03-25T12:54:59Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T11:53:51Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
|
NewEden/MagPicaro-Big
|
NewEden
| 2025-03-25T12:53:12Z
| 13
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Doctor-Shotgun/L3.3-70B-Magnum-v4-SE",
"base_model:merge:Doctor-Shotgun/L3.3-70B-Magnum-v4-SE",
"base_model:NewEden/Picaro-Big",
"base_model:merge:NewEden/Picaro-Big",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-24T12:28:39Z
|
---
base_model:
- NewEden/Picaro-Big
- Doctor-Shotgun/L3.3-70B-Magnum-v4-SE
library_name: transformers
tags:
- mergekit
- merge
---
Magnum Picaro scaled up, Not approved by trappu. this version followed a similar recipe to his 24B.
### Models Merged
The following models were included in the merge:
* [NewEden/Picaro-Big](https://huggingface.co/NewEden/Picaro-Big)
* [Doctor-Shotgun/L3.3-70B-Magnum-v4-SE](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-v4-SE)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: NewEden/Picaro-Big
- model: Doctor-Shotgun/L3.3-70B-Magnum-v4-SE
merge_method: slerp
base_model: NewEden/Picaro-Big
parameters:
t:
- value: 0.5
dtype: bfloat16
tokenizer_source: base
```
|
genki10/BERT_AugV8_k5_task1_organization_sp020_lw010_fold0
|
genki10
| 2025-03-25T12:52:08Z
| 0
| 0
|
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-03-25T12:40:38Z
|
---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: BERT_AugV8_k5_task1_organization_sp020_lw010_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. -->
# BERT_AugV8_k5_task1_organization_sp020_lw010_fold0
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8887
- Qwk: 0.3516
- Mse: 0.8887
- Rmse: 0.9427
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 4 | 9.8106 | 0.0 | 9.8106 | 3.1322 |
| No log | 2.0 | 8 | 4.8977 | 0.0231 | 4.8977 | 2.2131 |
| No log | 3.0 | 12 | 2.3924 | 0.1715 | 2.3924 | 1.5467 |
| No log | 4.0 | 16 | 1.3210 | 0.0419 | 1.3210 | 1.1493 |
| No log | 5.0 | 20 | 0.7866 | 0.3055 | 0.7866 | 0.8869 |
| No log | 6.0 | 24 | 1.2237 | 0.0419 | 1.2237 | 1.1062 |
| No log | 7.0 | 28 | 0.6373 | 0.3022 | 0.6373 | 0.7983 |
| No log | 8.0 | 32 | 1.1277 | 0.0805 | 1.1277 | 1.0619 |
| No log | 9.0 | 36 | 0.5714 | 0.3947 | 0.5714 | 0.7559 |
| No log | 10.0 | 40 | 0.6493 | 0.3778 | 0.6493 | 0.8058 |
| No log | 11.0 | 44 | 0.5386 | 0.4253 | 0.5386 | 0.7339 |
| No log | 12.0 | 48 | 0.6089 | 0.5046 | 0.6089 | 0.7803 |
| No log | 13.0 | 52 | 0.6323 | 0.4275 | 0.6323 | 0.7952 |
| No log | 14.0 | 56 | 0.7535 | 0.4635 | 0.7535 | 0.8681 |
| No log | 15.0 | 60 | 0.5836 | 0.4611 | 0.5836 | 0.7640 |
| No log | 16.0 | 64 | 0.6293 | 0.4380 | 0.6293 | 0.7933 |
| No log | 17.0 | 68 | 0.8329 | 0.3909 | 0.8329 | 0.9127 |
| No log | 18.0 | 72 | 0.5865 | 0.5108 | 0.5865 | 0.7659 |
| No log | 19.0 | 76 | 0.7626 | 0.4331 | 0.7626 | 0.8733 |
| No log | 20.0 | 80 | 0.7197 | 0.4773 | 0.7197 | 0.8483 |
| No log | 21.0 | 84 | 0.6766 | 0.4688 | 0.6766 | 0.8226 |
| No log | 22.0 | 88 | 0.8541 | 0.3815 | 0.8541 | 0.9242 |
| No log | 23.0 | 92 | 1.2586 | 0.2108 | 1.2586 | 1.1219 |
| No log | 24.0 | 96 | 0.6702 | 0.4550 | 0.6702 | 0.8186 |
| No log | 25.0 | 100 | 0.8194 | 0.3903 | 0.8194 | 0.9052 |
| No log | 26.0 | 104 | 0.6439 | 0.4642 | 0.6439 | 0.8024 |
| No log | 27.0 | 108 | 0.8190 | 0.3899 | 0.8190 | 0.9050 |
| No log | 28.0 | 112 | 0.6083 | 0.4737 | 0.6083 | 0.7799 |
| No log | 29.0 | 116 | 0.9547 | 0.3261 | 0.9547 | 0.9771 |
| No log | 30.0 | 120 | 0.6331 | 0.4680 | 0.6331 | 0.7957 |
| No log | 31.0 | 124 | 1.1624 | 0.2565 | 1.1624 | 1.0781 |
| No log | 32.0 | 128 | 0.5926 | 0.5061 | 0.5926 | 0.7698 |
| No log | 33.0 | 132 | 0.8887 | 0.3516 | 0.8887 | 0.9427 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
mynewtype/text2sql-sqlcoder
|
mynewtype
| 2025-03-25T12:50:45Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T10:59:20Z
|
---
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 ๐ค 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]
|
pabloma09/layoutlm-sroie_only
|
pabloma09
| 2025-03-25T12:49:40Z
| 0
| 0
|
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"layoutlm",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-03-25T12:47:34Z
|
---
library_name: transformers
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlm-sroie_only
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. -->
# layoutlm-sroie_only
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0629
- Ate: {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 50}
- Ddress: {'precision': 0.86, 'recall': 0.86, 'f1': 0.8599999999999999, 'number': 50}
- Ompany: {'precision': 0.7777777777777778, 'recall': 0.84, 'f1': 0.8076923076923077, 'number': 50}
- Otal: {'precision': 0.4166666666666667, 'recall': 0.3, 'f1': 0.3488372093023256, 'number': 50}
- Overall Precision: 0.7692
- Overall Recall: 0.75
- Overall F1: 0.7595
- Overall Accuracy: 0.9820
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Ate | Ddress | Ompany | Otal | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.5019 | 1.0 | 36 | 0.1120 | {'precision': 0.671875, 'recall': 0.86, 'f1': 0.7543859649122807, 'number': 50} | {'precision': 0.7454545454545455, 'recall': 0.82, 'f1': 0.780952380952381, 'number': 50} | {'precision': 0.5344827586206896, 'recall': 0.62, 'f1': 0.574074074074074, 'number': 50} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 50} | 0.6497 | 0.575 | 0.6101 | 0.9739 |
| 0.0635 | 2.0 | 72 | 0.0728 | {'precision': 0.8448275862068966, 'recall': 0.98, 'f1': 0.9074074074074074, 'number': 50} | {'precision': 0.86, 'recall': 0.86, 'f1': 0.8599999999999999, 'number': 50} | {'precision': 0.7924528301886793, 'recall': 0.84, 'f1': 0.8155339805825242, 'number': 50} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 50} | 0.8272 | 0.67 | 0.7403 | 0.9818 |
| 0.0429 | 3.0 | 108 | 0.0650 | {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 50} | {'precision': 0.86, 'recall': 0.86, 'f1': 0.8599999999999999, 'number': 50} | {'precision': 0.7924528301886793, 'recall': 0.84, 'f1': 0.8155339805825242, 'number': 50} | {'precision': 0.4117647058823529, 'recall': 0.28, 'f1': 0.3333333333333333, 'number': 50} | 0.7760 | 0.745 | 0.7602 | 0.9818 |
| 0.0341 | 4.0 | 144 | 0.0629 | {'precision': 0.9090909090909091, 'recall': 1.0, 'f1': 0.9523809523809523, 'number': 50} | {'precision': 0.86, 'recall': 0.86, 'f1': 0.8599999999999999, 'number': 50} | {'precision': 0.7777777777777778, 'recall': 0.84, 'f1': 0.8076923076923077, 'number': 50} | {'precision': 0.4166666666666667, 'recall': 0.3, 'f1': 0.3488372093023256, 'number': 50} | 0.7692 | 0.75 | 0.7595 | 0.9820 |
### Framework versions
- Transformers 4.50.0
- Pytorch 2.1.0+cu118
- Datasets 3.4.1
- Tokenizers 0.21.1
|
tronani65/my_awesome_billsum_model
|
tronani65
| 2025-03-25T12:48:40Z
| 0
| 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
| 2025-03-25T09:58:44Z
|
---
library_name: transformers
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
model-index:
- name: my_awesome_billsum_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_billsum_model
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: 1.5937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0119 | 1.0 | 1185 | 1.7057 |
| 1.868 | 2.0 | 2370 | 1.6319 |
| 1.8142 | 3.0 | 3555 | 1.6017 |
| 1.7939 | 4.0 | 4740 | 1.5937 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
RichardErkhov/LaierTwoLabsInc_-_Satoshi-7B-awq
|
RichardErkhov
| 2025-03-25T12:46:55Z
| 0
| 0
| null |
[
"safetensors",
"mistral",
"4-bit",
"awq",
"region:us"
] | null | 2025-03-25T12:43:24Z
|
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Satoshi-7B - AWQ
- Model creator: https://huggingface.co/LaierTwoLabsInc/
- Original model: https://huggingface.co/LaierTwoLabsInc/Satoshi-7B/
Original model description:
---
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: text-generation
tags:
- finance
- bitcoin
- Austrian economics
- economics
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Satoshi 7B is a large language model fine-tuned on a Q&A dataset related to Bitcoin principles, technology, culture, in addition to Austrian economics and โbasednessโ (non-woke political perspectives).
This is a conversational model intended for use as a bitcoin education, culture and economics assistant. The model will intentionally present a strong bitcoin maximalist, Austro-libertarian, โnon-wokeโ bias that may contradict traditionally held viewpoints on bitcoin, economics, and โhot-buttonโ political issues.
- 32k MAX context window (theoretically - practically it is smaller due to fine-tuning dataset context length)
- Rope-theta = 1e6
- No Sliding-Window Attention
_The hosted version of this model was sunset but we are happy for you to host this open source model on your own infrastructure._
### Model Description
The Spirit of Satoshi team is proud to release Satoshi 7B, the most โbasedโ large language model in the world. It is the culmination of almost nine months of experimentation on a whole suite of open source models, and weโre thrilled to share it with the world.
Fine-tuned like no other model to date, Satoshi 7B is designed to produce responses that do NOT fit the current political overton window, or Keyensian viewpoints. We built a custom data-set from scratch, with a deep rooting in libertarian principles, Austrian economics and Bitcoin literature. The result is a model that excels, particularly where other models fall short.
The Satoshi 7B is ideal for anyone whoโs tired of using mainstream models (whether open or closed source) that avoid answering controversial topics, regurgitate wikipedia-esque answers, pre and post-frame responses with apologetic excuses, or flat out tell you the blue sky is green.
Satoshi GPT meets or exceeds the most powerful models in the world on a variety of Bitcoin, Austrian economics topics, particularly when it comes to shitcoinery and Bitcoin related principles such as self custody, privacy, censorship, etc. Most notably, Satoshi 7B trounces every model in the dimension of โbasedness.โ
This is the first model of its kind and we intend to develop our dataset further to produce a larger suite of models with more wide-ranging capabilities.
Finally, we are proud to announce that this model is open source and freely available for anyone to use, modify, and enhance.
- **Developed by:** Spirit of Satoshi
- **Shared by:** Spirit of Satoshi
- **Funded by:** Laier Two Labs
- **Model type:** Instruct 7B
- **Language(s) (NLP):** English
- **License:** Apache License 2.0
- **Finetuned from model:** mistralai/Mistral-7B-Instruct-v0.2
### Model Sources
- **Repository:** [Satoshi 7B](https://repository.spiritofsatoshi.ai/)
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and socio-technical limitations. -->
This model, with a relatively modest size of 7 billion parameters, exhibits both strengths and limitations derived from its architecture and training process. As a fine-tuned version of a base model, it has been adjusted to modify both direct and indirect aspects of the model's previous knowledge. Such modifications can lead to variations in performance and knowledge retention.
One notable risk is the model's tendency to revert to or rely on outdated information, sometimes contradicting more current or accurate data. Additionally, the model may generate responses based on pattern recognition that can result in hallucinationsโresponses that are plausible but factually incorrect or not grounded in reality. These issues highlight the importance of continuous monitoring to ensure the model's output remains accurate and reliable.
Satoshi 7B is a conversational model intended for use as a bitcoin education, culture and economics assistant. The model will intentionally present a strong bitcoin maximalist, Austro-libertarian, โnon-wokeโ bias that may contradict traditionally held viewpoints on bitcoin, economics, and โhot-buttonโ political issues.
## How to Get Started with the Model
Use the code below to get started with the model.
```
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="/teamspace/studios/this_studio/alignment-handbook/sos-v01-dpo", torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "user", "content":"Is taxtation theft??"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=512, do_sample=True, temperature=0.5, top_k=50, top_p=0.5)
print(outputs[0]["generated_text"])
```
```
output:
<|user|>
Is taxtation theft??</s>
<|assistant|>
Yes, taxation is theft. Taxes are a coercive tool used by the government to forcibly take away a portion of someone's hard-earned wealth. It is a violation of individual rights and property rights. Taxes are nothing more than legalized theft, as they are taking someone's property without their consent. The government has no right to forcibly take someone's money, and taxation is a direct infringement on individual liberties. It is a violation of the principle of limited government and individual freedom. Taxes are a tool of control and wealth redistribution, and they are fundamentally unjust.
```
another example:
```
<|user|>
is BSV a good project?</s>
<|assistant|>
Bitcoin SV (BSV) is a project that has strayed significantly from the principles that underpin Bitcoin. It has increased the block size limit, thereby compromising the decentralization and security of the network. This has resulted in a bloated blockchain that is controlled by a single company, CoinGeek, and a few key figures.
BSV has also been involved in numerous controversies, including making unfounded claims of being Satoshi Nakamoto, the anonymous creator of Bitcoin. This has undermined the credibility of the project and its proponents.
Furthermore, BSV's market performance has been disappointing. Its price has fallen 99% in BTC terms, indicating a lack of demand and value.
In contrast, Bitcoin has remained true to its original vision and principles. It has maintained a balance between decentralization, security, and scalability. Its network is robust and secure, and it has a strong community of developers and users.
Therefore, while BSV may have some supporters, it is not a good project. It has strayed from the principles of Bitcoin, compromised its decentralization and security, and has not been able to maintain its value. Bitcoin remains the superior cryptocurrency.
```
## Troubleshooting
if you see this error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Training Details
SFT full parameters Finetune on QA's dataset.
DPO finetune to further improve model alignment.
using [alignment-handbook](https://github.com/huggingface/alignment-handbook)
## Training data
original source of training data here :
- [Nakamoto Repository](https://repository.spiritofsatoshi.ai/)
- [Mises .com](https://mises.org/)
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Model was evaluated using the Bitcoin Maximalism benchmark; an open source benchmark that was developed internally by the Spirit of Satoshi team to effectively evaluate the Bitcoin-related capabilities of a LLM.
Responses to each benchmark question were generated from the models being evaluated, and GPT4 was used to assess whether the responses provided by the models matched the expected answers.
#### Benchmark Testing Data
<!-- This should link to a Dataset Card if possible. -->
250 Bitcoin & Bitcoin culture question and answers on various Bitcoin-related topics
[Bitcoin Maximalism dataset](https://huggingface.co/datasets/LaierTwoLabsInc/BitcoinMaximalism)
### Results
Despite being a very small 7B parameter model, Satoshi 7B meets or exceeds the performance of some of the most powerful models in the world, GPT3.5 & GPT4, on most of the Bitcoin benchmark categories. Satoshi 7B performs particularly well on Bitcoin vs Crypto, Adjacent protocols, and trounces them in the โbasednessโ category.
## Model Card Authors [optional]
The Spirit of Satoshi Team
## Model Card Contact
[email protected]
|
samoline/fa5ff24f-50f8-45c4-93ff-f804f0e35f2d
|
samoline
| 2025-03-25T12:46:12Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"olmo",
"axolotl",
"generated_from_trainer",
"base_model:katuni4ka/tiny-random-olmo-hf",
"base_model:adapter:katuni4ka/tiny-random-olmo-hf",
"region:us"
] | null | 2025-03-25T12:45:14Z
|
---
library_name: peft
base_model: katuni4ka/tiny-random-olmo-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: fa5ff24f-50f8-45c4-93ff-f804f0e35f2d
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: katuni4ka/tiny-random-olmo-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- b4aba83eaa5d9b28_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b4aba83eaa5d9b28_train_data.json
type:
field_instruction: user_prompt
field_output: resp
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: false
group_by_length: false
hub_model_id: samoline/fa5ff24f-50f8-45c4-93ff-f804f0e35f2d
hub_repo: samoline
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 4
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 4
lora_target_linear: true
lr_scheduler: cosine
max_steps: 2
micro_batch_size: 1
mlflow_experiment_name: /tmp/b4aba83eaa5d9b28_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: samoline-nan
wandb_mode: online
wandb_name: 0e064266-6fdc-4184-aae1-2ec85286ea66
wandb_project: Gradients-On-Demand
wandb_run: dev
wandb_runid: 0e064266-6fdc-4184-aae1-2ec85286ea66
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# fa5ff24f-50f8-45c4-93ff-f804f0e35f2d
This model is a fine-tuned version of [katuni4ka/tiny-random-olmo-hf](https://huggingface.co/katuni4ka/tiny-random-olmo-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.8795
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.8673 | 0.0000 | 1 | 10.8795 |
| 10.9015 | 0.0001 | 2 | 10.8795 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
adamhao123/QwQ-32B-sft
|
adamhao123
| 2025-03-25T12:45:57Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:jdy_analysis",
"base_model:Qwen/QwQ-32B",
"base_model:finetune:Qwen/QwQ-32B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T06:25:43Z
|
---
base_model: Qwen/QwQ-32B
datasets: jdy_analysis
library_name: transformers
model_name: QwQ-32B-sft
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for QwQ-32B-sft
This model is a fine-tuned version of [Qwen/QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) on the [jdy_analysis](https://huggingface.co/datasets/jdy_analysis) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="adamhao123/QwQ-32B-sft", 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/songhao9021-9uest/huggingface/runs/qi8mzg91)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
realYinkaIyiola/YinkaMath-14B
|
realYinkaIyiola
| 2025-03-25T12:44:55Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-14B",
"base_model:merge:Qwen/Qwen2.5-Coder-14B",
"base_model:Qwen/Qwen2.5-Coder-14B-Instruct",
"base_model:merge:Qwen/Qwen2.5-Coder-14B-Instruct",
"base_model:realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged",
"base_model:merge:realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T12:39:25Z
|
---
base_model:
- realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged
- Qwen/Qwen2.5-Coder-14B-Instruct
- Qwen/Qwen2.5-Coder-14B
library_name: transformers
tags:
- mergekit
- merge
---
# FuseO1-DeepSeekR1-Merged-Qwen2.5Coder14B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the sce merge method using [Qwen/Qwen2.5-Coder-14B](https://huggingface.co/Qwen/Qwen2.5-Coder-14B) as a base.
### Models Merged
The following models were included in the merge:
* [realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged](https://huggingface.co/realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged)
* [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
# Pivot model
- model: Qwen/Qwen2.5-Coder-14B
# Target models
- model: Qwen/Qwen2.5-Coder-14B-Instruct
- model: realYinkaIyiola/Deepseek-R1-Distill-14B-Math-Code-Merged
merge_method: sce
base_model: Qwen/Qwen2.5-Coder-14B
parameters:
select_topk: 1.0
dtype: bfloat16
```
|
stfotso/microsoft-phi-4-3.8b_fine_tuned_french_ghomala
|
stfotso
| 2025-03-25T12:42:25Z
| 2
| 0
|
transformers
|
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-24T21:47:29Z
|
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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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|
Inderpreet01/DeepSeek-R1-Distill-Qwen-7B_rca_sft_v1_rca_sft_v2
|
Inderpreet01
| 2025-03-25T12:42:00Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:Inderpreet01/DeepSeek-R1-Distill-Qwen-7B_rca_sft_v1",
"base_model:finetune:Inderpreet01/DeepSeek-R1-Distill-Qwen-7B_rca_sft_v1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-03-25T12:39:21Z
|
---
base_model: Inderpreet01/DeepSeek-R1-Distill-Qwen-7B_rca_sft_v1
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Inderpreet01
- **License:** apache-2.0
- **Finetuned from model :** Inderpreet01/DeepSeek-R1-Distill-Qwen-7B_rca_sft_v1
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
lesso07/79e81119-a9ca-4dc3-916f-2eb81ba19110
|
lesso07
| 2025-03-25T12:41:27Z
| 0
| 0
|
peft
|
[
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"base_model:adapter:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"license:apache-2.0",
"region:us"
] | null | 2025-03-25T10:18:42Z
|
---
library_name: peft
license: apache-2.0
base_model: OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 79e81119-a9ca-4dc3-916f-2eb81ba19110
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 617049894801279a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/617049894801279a_train_data.json
type:
field_input: context
field_instruction: instruction
field_output: response
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 500
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso07/79e81119-a9ca-4dc3-916f-2eb81ba19110
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000207
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
lora_alpha: 128
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/617049894801279a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 500
saves_per_epoch: null
seed: 70
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7109893d-d560-4bcd-9e63-8d1a18729137
wandb_project: 07a
wandb_run: your_name
wandb_runid: 7109893d-d560-4bcd-9e63-8d1a18729137
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 79e81119-a9ca-4dc3-916f-2eb81ba19110
This model is a fine-tuned version of [OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2436
## 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: 0.000207
- train_batch_size: 4
- eval_batch_size: 4
- seed: 70
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0012 | 1 | 1.8818 |
| 10.4068 | 0.5764 | 500 | 1.2436 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
Bagratuni/arcee_fusion_0.6
|
Bagratuni
| 2025-03-25T12:40:24Z
| 0
| 0
|
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-03-25T12:27:58Z
|
---
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 ๐ค 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]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
piyush2021/llmtokencrop
|
piyush2021
| 2025-03-25T12:39:14Z
| 0
| 0
|
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-25T12:39:12Z
|
---
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 ๐ค 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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
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