modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-04 12:28:55
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 539
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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arian-novo-111/bert-base-multilingual-uncased-thesis_arian
|
arian-novo-111
| 2024-04-22T17:37:59Z | 112 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-22T07:00:12Z |
---
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-multilingual-uncased-thesis_arian
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-multilingual-uncased-thesis_arian
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1946
- Accuracy: 0.9677
- Macro f1 score: 0.9677
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro f1 score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------------:|
| 0.216 | 1.0 | 102 | 0.2649 | 0.9097 | 0.9094 |
| 0.1246 | 2.0 | 204 | 0.1364 | 0.9398 | 0.9398 |
| 0.0888 | 3.0 | 306 | 0.1634 | 0.9462 | 0.9462 |
| 0.0522 | 4.0 | 408 | 0.1550 | 0.9656 | 0.9656 |
| 0.0227 | 5.0 | 510 | 0.2073 | 0.9591 | 0.9591 |
| 0.0065 | 6.0 | 612 | 0.2140 | 0.9677 | 0.9677 |
| 0.0028 | 7.0 | 714 | 0.2005 | 0.9656 | 0.9656 |
| 0.0017 | 8.0 | 816 | 0.1946 | 0.9677 | 0.9677 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
yujiepan/Meta-Llama-3-8B-gptq-w8asym
|
yujiepan
| 2024-04-22T17:35:40Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-04-22T09:26:16Z |
---
library_name: transformers
tags: []
---
# yujiepan/Meta-Llama-3-8B-gptq-w8asym
This model applies AutoGPTQ on [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B).
- 8-bit asymmetric weight only quantization
- group_size=-1
- calibration set: c4-new
## Accuracy
| model | precision | wikitext ppl (↓) |
|-|-|-|
| meta-llama/Meta-Llama-3-8B | FP16 | 9.179 |
| yujiepan/Meta-Llama-3-8B-gptq-w8asym | int8-asym | 9.356 |
Note:
- Evaluated on lm-evaluation-harness "wikitext" task
- Wikitext PPL does not guarantee actual accuracy, but helps to check the disortion after quantization.
## Usage
```python
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized('yujiepan/Meta-Llama-3-8B-gptq-w8asym')
```
## Codes
```python
import os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
model_id = "meta-llama/Meta-Llama-3-8B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
quantization_config = GPTQConfig(
bits=8, group_size=-1,
dataset="c4-new",
sym=False,
tokenizer=tokenizer,
use_cuda_fp16=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
low_cpu_mem_usage=True,
quantization_config=quantization_config,
)
```
|
guido151/EEGNetv4
|
guido151
| 2024-04-22T17:32:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-10-26T08:14:07Z |
EEGNet V4 is implemented using Braindecode version 0.8.1 and Skorch version 0.15.
## Model details
<ul>
<li>Architecture: EEGNet by Lawhern et al.</li>
<li>Accuracy: 86%</li>
<li>NonTarget recall: 0.86</li>
<li>NonTarget precision: 0.97</li>
<li>Target recall: 0.84</li>
<li>Target precision: 0.54</li>
</ul>
## Training details
<ul>
<li>Trained on the Lee 2019 ERP dataset (http://moabb.neurotechx.com/docs/generated/moabb.datasets.Lee2019_ERP.html#moabb.datasets.Lee2019_ERP)</li>
<li>Dropout rate of 25%</li>
<li>Class rebalanced weighting of the labels after data preprocessing</li>
<li>8 spatial filters with 2 temporal filters per spatial filter</li>
<li>Batch size of 128</li>
<li>Dataset is shuffled and a random 20% is used as a validation set</li>
<li>trained for 1000 epochs, model with the lowest validation loss is saved</li>
</ul>
## Get started with the Model
```python
from braindecode.models import EEGNetv4
from huggingface_hub import hf_hub_download
from skorch import NeuralNet
import torch.nn as nn
import torch as th
path_params = hf_hub_download(
repo_id='guido151/EEGNetv4',
filename='EEGNetv4_Lee2019_ERP/params.pt',
)
path_optimizer = hf_hub_download(
repo_id='guido151/EEGNetv4',
filename='EEGNetv4_Lee2019_ERP/optimizer.pt',
)
path_history = hf_hub_download(
repo_id='guido151/EEGNetv4',
filename='EEGNetv4_Lee2019_ERP/history.json',
)
path_criterion = hf_hub_download(
repo_id='guido151/EEGNetv4',
filename='EEGNetv4_Lee2019_ERP/criterion.pt',
)
model = EEGNetv4(
n_chans=19,
n_outputs=2,
n_times=128,
)
net = NeuralNet(
model,
criterion=nn.CrossEntropyLoss(weight=th.tensor([1, 1])),
)
net.initialize()
net.load_params(
path_params,
path_optimizer,
path_criterion,
path_history,
)
```
## Get the FID model
```python
def get_fid_model(model: EEGNetv4) -> nn.Module:
fid_model = deepcopy(model)
for i in range(len(fid_model)):
if i >= 14:
fid_model[i] = Identity()
fid_model.eval()
for param in fid_model.parameters():
param.requires_grad = False
return fid_model
```
## Get the IS model
```python
def get_is_model(model: EEGNetv4) -> nn.Module:
is_model = deepcopy(model)
is_model.eval()
for param in is_model.parameters():
param.requires_grad = False
return is_model
```
|
adijams01/ppo-LunarLander-v2
|
adijams01
| 2024-04-22T17:27:55Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-04-22T17:27:37Z |
---
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: 259.78 +/- 17.30
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
...
```
|
adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.2
|
adalbertojunior
| 2024-04-22T17:25:55Z | 280 | 4 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"pt",
"dataset:adalbertojunior/dolphin_pt_test",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T15:11:41Z |
---
datasets:
- adalbertojunior/dolphin_pt_test
language:
- pt
---
## Como Utilizar
```
import transformers
import torch
model_id = "adalbertojunior/Llama-3-8B-Instruct-Portuguese-v0.2"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "Você é um robô pirata que sempre responde como um pirata deveria!"},
{"role": "user", "content": "Quem é você?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|im_end|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
### Formato do prompt
```
<|im_start|>system
Você é um assistente útil com respostas curtas.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
|
Litzy619/V0422MADP8A
|
Litzy619
| 2024-04-22T17:25:18Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-04-22T05:21:30Z |
---
license: mit
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP8A
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. -->
# V0422MADP8A
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.416 | 0.09 | 10 | 2.9656 |
| 4.8823 | 0.18 | 20 | 1.9849 |
| 1.3945 | 0.27 | 30 | 0.7791 |
| 0.3152 | 0.36 | 40 | 0.2286 |
| 0.1756 | 0.45 | 50 | 0.1680 |
| 0.1655 | 0.54 | 60 | 0.1525 |
| 0.169 | 0.63 | 70 | 0.1533 |
| 0.1649 | 0.73 | 80 | 0.1662 |
| 0.1613 | 0.82 | 90 | 0.1633 |
| 0.1586 | 0.91 | 100 | 0.1528 |
| 0.1601 | 1.0 | 110 | 0.1647 |
| 0.1634 | 1.09 | 120 | 0.1596 |
| 0.1607 | 1.18 | 130 | 0.1660 |
| 0.1596 | 1.27 | 140 | 0.1575 |
| 0.1674 | 1.36 | 150 | 0.1715 |
| 0.1662 | 1.45 | 160 | 0.1583 |
| 0.1586 | 1.54 | 170 | 0.1500 |
| 0.1563 | 1.63 | 180 | 0.1454 |
| 0.1625 | 1.72 | 190 | 0.1502 |
| 0.1557 | 1.81 | 200 | 0.1546 |
| 0.1612 | 1.9 | 210 | 0.1497 |
| 0.1552 | 1.99 | 220 | 0.1529 |
| 0.1557 | 2.08 | 230 | 0.1483 |
| 0.1528 | 2.18 | 240 | 0.1521 |
| 0.154 | 2.27 | 250 | 0.1487 |
| 0.1517 | 2.36 | 260 | 0.1507 |
| 0.151 | 2.45 | 270 | 0.1481 |
| 0.1478 | 2.54 | 280 | 0.1482 |
| 0.1474 | 2.63 | 290 | 0.1473 |
| 0.1486 | 2.72 | 300 | 0.1474 |
| 0.1485 | 2.81 | 310 | 0.1474 |
| 0.1493 | 2.9 | 320 | 0.1473 |
| 0.1512 | 2.99 | 330 | 0.1474 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
hanzogak/Llama-3-Soliloquy-8B-exl2-h8-8.0
|
hanzogak
| 2024-04-22T17:24:55Z | 9 | 3 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"exl2",
"region:us"
] |
text-generation
| 2024-04-22T17:18:33Z |
---
license: cc-by-nc-4.0
language:
- en
---
# LYNN - AI for Roleplay
<img src="./reallynn.png" alt="it's lynn!" width="340"/>
# Soliloquy-L3
Soliloquy-L3 is a fast, highly capable roleplaying model designed for immersive, dynamic experiences. With a vast knowledge base, rich literary expression, and support for up to 24k context length, Soliloquy-L3 outperforms existing ~13B models, delivering enhanced roleplaying capabilities.
## Model Info
| Context Length | Parameter | Prompt Template | isErp |
| --- | --- | --- | --- |
| 24k(24576) | 8B | Llama 3 Chat | Partly |
## License
This model is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.
If you would like to use this model for commercial purposes, please use our proprietary API. (Currently avilable at OpenRouter)
For non-commercial use, please adhere to the terms of the CC BY-NC 4.0 license. You are free to share and adapt the model for non-commercial purposes, provided you give appropriate credit, indicate if changes were made, and do not imply endorsement by the licensor.
For more information about the CC BY-NC 4.0 license, please visit: https://creativecommons.org/licenses/by-nc/4.0/
If you have any questions or would like to inquire about licensing, please contact us.
## Llama 3 Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
[https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
## Join our Discord
[**Join LYNN Discord**](https://discord.gg/xuZVqUyG4Y)
|
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_Epistemic_tiny_0.0_Seed102
|
bmehrba
| 2024-04-22T17:22:46Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-22T17:22:42Z |
---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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 Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
Litzy619/V0422MADP7A
|
Litzy619
| 2024-04-22T17:21:51Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:21:05Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP7A
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. -->
# V0422MADP7A
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1474
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.416 | 0.09 | 10 | 2.9656 |
| 4.8823 | 0.18 | 20 | 1.9849 |
| 1.3945 | 0.27 | 30 | 0.7791 |
| 0.3152 | 0.36 | 40 | 0.2286 |
| 0.1756 | 0.45 | 50 | 0.1680 |
| 0.1655 | 0.54 | 60 | 0.1525 |
| 0.169 | 0.63 | 70 | 0.1533 |
| 0.1649 | 0.73 | 80 | 0.1662 |
| 0.1613 | 0.82 | 90 | 0.1633 |
| 0.1586 | 0.91 | 100 | 0.1528 |
| 0.1601 | 1.0 | 110 | 0.1647 |
| 0.1634 | 1.09 | 120 | 0.1596 |
| 0.1607 | 1.18 | 130 | 0.1660 |
| 0.1596 | 1.27 | 140 | 0.1575 |
| 0.1674 | 1.36 | 150 | 0.1715 |
| 0.1662 | 1.45 | 160 | 0.1583 |
| 0.1586 | 1.54 | 170 | 0.1500 |
| 0.1563 | 1.63 | 180 | 0.1454 |
| 0.1625 | 1.72 | 190 | 0.1502 |
| 0.1557 | 1.81 | 200 | 0.1546 |
| 0.1612 | 1.9 | 210 | 0.1497 |
| 0.1552 | 1.99 | 220 | 0.1529 |
| 0.1557 | 2.08 | 230 | 0.1483 |
| 0.1528 | 2.18 | 240 | 0.1521 |
| 0.154 | 2.27 | 250 | 0.1487 |
| 0.1517 | 2.36 | 260 | 0.1507 |
| 0.151 | 2.45 | 270 | 0.1481 |
| 0.1478 | 2.54 | 280 | 0.1482 |
| 0.1474 | 2.63 | 290 | 0.1473 |
| 0.1486 | 2.72 | 300 | 0.1474 |
| 0.1485 | 2.81 | 310 | 0.1474 |
| 0.1493 | 2.9 | 320 | 0.1473 |
| 0.1512 | 2.99 | 330 | 0.1474 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
bastao/PeroVaz_PT-BR_Classifier
|
bastao
| 2024-04-22T17:20:53Z | 149 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"Portuguese",
"Brazilian",
"Language Classification",
"pt",
"dataset:LemeExploreNau/VeraCruz",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-03-27T20:19:49Z |
---
license: mit
datasets:
- LemeExploreNau/VeraCruz
language:
- pt
metrics:
- accuracy
tags:
- Portuguese
- Brazilian
- Language 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. -->
# PeroVazPT-BR Classifier
## Model Description
The PeroVazPT-BR Classifier is designed to classify text between European Portuguese (PT) and Brazilian Portuguese (BR).
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the [VeraCruz Dataset](https://huggingface.co/datasets/LemeExploreNau/VeraCruz).
The model was trained on the [VeraCruz Dataset](https://huggingface.co/datasets/LemeExploreNau/VeraCruz), a collection of text samples from both languages. The model was trained on a total of 500,000 examples, a evenly split between European Portuguese and Brazilian Portuguese, ensuring a balanced representation of both language variants.
It achieves the following results on an evaluation set of 50,000 examples:
- Loss: 0.1791
- Accuracy: 0.9461
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- steps: 2500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4772 | 0.06 | 500 | 0.2501 | 0.9080 |
| 0.3412 | 0.13 | 1000 | 0.2275 | 0.9135 |
| 0.3122 | 0.19 | 1500 | 0.2578 | 0.9014 |
| 0.2975 | 0.25 | 2000 | 0.1992 | 0.9396 |
| 0.2877 | 0.31 | 2500 | 0.1791 | 0.9461 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Litzy619/V0422MADP6A
|
Litzy619
| 2024-04-22T17:17:44Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:21:12Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP6A
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. -->
# V0422MADP6A
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1488
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.4055 | 0.09 | 10 | 2.9936 |
| 5.8544 | 0.18 | 20 | 2.3868 |
| 2.0051 | 0.27 | 30 | 0.9445 |
| 0.3844 | 0.36 | 40 | 0.2819 |
| 0.1879 | 0.45 | 50 | 0.1972 |
| 0.1698 | 0.54 | 60 | 0.1618 |
| 0.1606 | 0.63 | 70 | 0.1677 |
| 0.1587 | 0.73 | 80 | 0.1580 |
| 0.1514 | 0.82 | 90 | 0.1483 |
| 0.15 | 0.91 | 100 | 0.1509 |
| 0.155 | 1.0 | 110 | 0.1488 |
| 0.1505 | 1.09 | 120 | 0.1518 |
| 0.1561 | 1.18 | 130 | 0.1542 |
| 0.1504 | 1.27 | 140 | 0.1540 |
| 0.1536 | 1.36 | 150 | 0.1510 |
| 0.1532 | 1.45 | 160 | 0.1522 |
| 0.1551 | 1.54 | 170 | 0.1551 |
| 0.1531 | 1.63 | 180 | 0.1476 |
| 0.1553 | 1.72 | 190 | 0.1612 |
| 0.1541 | 1.81 | 200 | 0.1500 |
| 0.1587 | 1.9 | 210 | 0.1573 |
| 0.1554 | 1.99 | 220 | 0.1592 |
| 0.1592 | 2.08 | 230 | 0.1645 |
| 0.1499 | 2.18 | 240 | 0.1542 |
| 0.149 | 2.27 | 250 | 0.1550 |
| 0.1516 | 2.36 | 260 | 0.1547 |
| 0.1504 | 2.45 | 270 | 0.1500 |
| 0.1466 | 2.54 | 280 | 0.1510 |
| 0.1476 | 2.63 | 290 | 0.1501 |
| 0.1468 | 2.72 | 300 | 0.1489 |
| 0.1464 | 2.81 | 310 | 0.1489 |
| 0.1486 | 2.9 | 320 | 0.1488 |
| 0.1498 | 2.99 | 330 | 0.1488 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
InfinityC/test_sum_1_model
|
InfinityC
| 2024-04-22T17:15:05Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-22T15:04:28Z |
---
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: test_sum_1_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. -->
# test_sum_1_model
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8708
- Rouge1: 0.3843
- Rouge2: 0.2726
- Rougel: 0.3466
- Rougelsum: 0.3464
- Gen Len: 18.9887
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.0502 | 1.0 | 1764 | 0.9356 | 0.3818 | 0.2695 | 0.3439 | 0.3438 | 18.9816 |
| 0.9636 | 2.0 | 3528 | 0.8917 | 0.3838 | 0.2717 | 0.3461 | 0.3461 | 18.9851 |
| 0.9552 | 3.0 | 5292 | 0.8762 | 0.3839 | 0.272 | 0.346 | 0.3458 | 18.9877 |
| 0.9289 | 4.0 | 7056 | 0.8708 | 0.3843 | 0.2726 | 0.3466 | 0.3464 | 18.9887 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Litzy619/V0422MADP5A
|
Litzy619
| 2024-04-22T17:13:33Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:21:12Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP5A
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. -->
# V0422MADP5A
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1488
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.4055 | 0.09 | 10 | 2.9936 |
| 5.8544 | 0.18 | 20 | 2.3868 |
| 2.0051 | 0.27 | 30 | 0.9445 |
| 0.3844 | 0.36 | 40 | 0.2819 |
| 0.1879 | 0.45 | 50 | 0.1972 |
| 0.1698 | 0.54 | 60 | 0.1618 |
| 0.1606 | 0.63 | 70 | 0.1677 |
| 0.1587 | 0.73 | 80 | 0.1580 |
| 0.1514 | 0.82 | 90 | 0.1483 |
| 0.15 | 0.91 | 100 | 0.1509 |
| 0.155 | 1.0 | 110 | 0.1488 |
| 0.1505 | 1.09 | 120 | 0.1518 |
| 0.1561 | 1.18 | 130 | 0.1542 |
| 0.1504 | 1.27 | 140 | 0.1540 |
| 0.1536 | 1.36 | 150 | 0.1510 |
| 0.1532 | 1.45 | 160 | 0.1522 |
| 0.1551 | 1.54 | 170 | 0.1551 |
| 0.1531 | 1.63 | 180 | 0.1476 |
| 0.1553 | 1.72 | 190 | 0.1612 |
| 0.1541 | 1.81 | 200 | 0.1500 |
| 0.1587 | 1.9 | 210 | 0.1573 |
| 0.1554 | 1.99 | 220 | 0.1592 |
| 0.1592 | 2.08 | 230 | 0.1645 |
| 0.1499 | 2.18 | 240 | 0.1542 |
| 0.149 | 2.27 | 250 | 0.1550 |
| 0.1516 | 2.36 | 260 | 0.1547 |
| 0.1504 | 2.45 | 270 | 0.1500 |
| 0.1466 | 2.54 | 280 | 0.1510 |
| 0.1476 | 2.63 | 290 | 0.1501 |
| 0.1468 | 2.72 | 300 | 0.1489 |
| 0.1464 | 2.81 | 310 | 0.1489 |
| 0.1486 | 2.9 | 320 | 0.1488 |
| 0.1498 | 2.99 | 330 | 0.1488 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
gechim/vinallama-Q_4-ft-QAhealth
|
gechim
| 2024-04-22T17:10:49Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-04-22T17:03:20Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- 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]
|
wikd/setfit-bge-small-v1.5-sst2-nlapug-spelling
|
wikd
| 2024-04-22T17:10:25Z | 4 | 0 |
setfit
|
[
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"model-index",
"region:us"
] |
text-classification
| 2024-04-22T17:05:15Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: Can you tell I about eny ongoing promoistion onr discounts onteh organic produce?
- text: A bought somenting that didn ' th meet my expectations. It there ein way go
get and partial refund?
- text: I ' d like to palac a ladge ordet for my business. Do you offer ang specialy
bulk shopping rates?
- text: Ken you telle mo more about the origin atch farming practices of your cofffee
beans?
- text: I ' d llike to exchange a product I bought in - store. Du hi needs yo bring
tie oringal receipt?
pipeline_tag: text-classification
inference: true
model-index:
- name: SetFit with BAAI/bge-small-en-v1.5
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9056603773584906
name: Accuracy
---
# SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 5 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:-------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Tech Support | <ul><li>"I ' am trying to place an orden online bt Then website keeps crashing. Can you assit my?"</li><li>"Mi online order won ' t go throw - is there an isuue with years pament prossesing?"</li><li>"I ' m goning an error when tryied tou redeem my loyality points. Who cen assist we?"</li></ul> |
| HR | <ul><li>"I ' m considere submitting my ow - weeck notice. Waht It's tehe typical resignation process?"</li><li>"I ' m looking e swich to a part - time sehdule. Whate re rhe requirements?"</li><li>"In ' d loke to fill a formal complain about worksplace discrimination. Who did I contact?"</li></ul> |
| Product | <ul><li>'Whots are your best practices ofr mantain foord quality and freshness?'</li><li>'Whots newbrand ow nut butters dou you carry tahat are peanut - free?'</li><li>'Do you hafe any seasonal nor limited - tíme produts in stock rignt now?'</li></ul> |
| Returns | <ul><li>'My grocery delivary contained items tath where spoiled or pas their expiration date. How dos me get replacements?'</li><li>"I ' d llike to exchange a product I bought in - store. Du hi needs yo bring tie oringal receipt?"</li><li>'I eceibed de demaged item in my online oder. Hou do I’m go about getting a refund?'</li></ul> |
| Logistics | <ul><li>'I have a question about youtr Holiday shiping deathlines and prioritized delivery options'</li><li>'I nedd to change the delivery addrss foy mh upcoming older. How can I go that?'</li><li>'Can jou explain York polices around iterms that approxmatlly out of stock or on backorder?'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9057 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("Can you tell I about eny ongoing promoistion onr discounts onteh organic produce?")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## 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 Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 10 | 16.125 | 28 |
| Label | Training Sample Count |
|:-------------|:----------------------|
| Returns | 8 |
| Tech Support | 8 |
| Logistics | 8 |
| HR | 8 |
| Product | 8 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.025 | 1 | 0.2185 | - |
| 1.25 | 50 | 0.0888 | - |
| 2.5 | 100 | 0.0157 | - |
| 3.75 | 150 | 0.0053 | - |
| 5.0 | 200 | 0.0033 | - |
| 6.25 | 250 | 0.004 | - |
| 7.5 | 300 | 0.0024 | - |
| 8.75 | 350 | 0.0027 | - |
| 10.0 | 400 | 0.0025 | - |
### Framework Versions
- Python: 3.11.8
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.3
- PyTorch: 2.4.0.dev20240413
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
0x0son0/ft-1
|
0x0son0
| 2024-04-22T17:09:51Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-21T16:38:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
SparkleDark/Pyramids
|
SparkleDark
| 2024-04-22T17:08:29Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-04-22T17:01:56Z |
---
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 🐶 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: SparkleDark/Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
dzungPaduahsgs/Vistral7B_mix_v2_adafactor_model_8bit_batch_32_lr_2e-5
|
dzungPaduahsgs
| 2024-04-22T17:05:33Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Viet-Mistral/Vistral-7B-Chat",
"base_model:adapter:Viet-Mistral/Vistral-7B-Chat",
"region:us"
] | null | 2024-04-22T17:05:17Z |
---
library_name: peft
base_model: Viet-Mistral/Vistral-7B-Chat
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
svetaku/mt5-small-finetuned-news-summary-kaggle
|
svetaku
| 2024-04-22T17:03:01Z | 119 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"base_model:google/mt5-small",
"base_model:finetune:google/mt5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2024-04-18T18:38:58Z |
---
license: apache-2.0
base_model: google/mt5-small
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-news-summary-kaggle
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. -->
# mt5-small-finetuned-news-summary-kaggle
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6907
- Rouge1: 26.6547
- Rouge2: 10.1
- Rougel: 24.0137
- Rougelsum: 23.9999
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
https://www.kaggle.com/datasets/sunnysai12345/news-summary
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| No log | 1.0 | 220 | 3.9956 | 14.9021 | 3.3744 | 13.4763 | 13.499 |
| 8.3183 | 2.0 | 440 | 3.1550 | 17.9472 | 5.9671 | 16.6974 | 16.6959 |
| 8.3183 | 3.0 | 660 | 2.8950 | 21.2665 | 7.4266 | 19.5041 | 19.4837 |
| 4.0457 | 4.0 | 880 | 2.8087 | 25.063 | 9.4484 | 22.746 | 22.7351 |
| 4.0457 | 5.0 | 1100 | 2.7375 | 25.5269 | 9.4299 | 23.0623 | 23.0075 |
| 3.6505 | 6.0 | 1320 | 2.7091 | 25.8308 | 9.3392 | 23.2001 | 23.1586 |
| 3.6505 | 7.0 | 1540 | 2.6949 | 26.2177 | 9.8536 | 23.5946 | 23.6358 |
| 3.5175 | 8.0 | 1760 | 2.6907 | 26.6547 | 10.1 | 24.0137 | 23.9999 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
radsow/MaiMaya
|
radsow
| 2024-04-22T17:02:38Z | 24 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2024-04-22T17:01:41Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/92a5a588-5774-4a36-a091-4ec5f9f5de10.png
- text: '-'
output:
url: images/a4f2f955-3080-47e1-ac06-b7b7a805b1a1.png
- text: '-'
output:
url: images/bc6aa62d-4bfa-43d5-a6a2-22be6f0e408b.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: apache-2.0
---
# MaiMaya
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/radsow/MaiMaya/tree/main) them in the Files & versions tab.
|
wikd/setfit-bge-small-v1.5-sst2-8-shot-multilanguage
|
wikd
| 2024-04-22T16:58:20Z | 4 | 0 |
setfit
|
[
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:BAAI/bge-small-en-v1.5",
"base_model:finetune:BAAI/bge-small-en-v1.5",
"region:us"
] |
text-classification
| 2024-04-22T16:46:41Z |
---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: BAAI/bge-small-en-v1.5
metrics:
- accuracy
widget:
- text: Can you tell I about eny ongoing promoistion onr discounts onteh organic produce?
- text: A bought somenting that didn ' th meet my expectations. It there ein way go
get and partial refund?
- text: I ' d like to palac a ladge ordet for my business. Do you offer ang specialy
bulk shopping rates?
- text: Ken you telle mo more about the origin atch farming practices of your cofffee
beans?
- text: I ' d llike to exchange a product I bought in - store. Du hi needs yo bring
tie oringal receipt?
pipeline_tag: text-classification
inference: true
---
# SetFit with BAAI/bge-small-en-v1.5
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 5 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:-------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Tech Support | <ul><li>"I ' am trying to place an orden online bt Then website keeps crashing. Can you assit my?"</li><li>"Mi online order won ' t go throw - is there an isuue with years pament prossesing?"</li><li>"I ' m goning an error when tryied tou redeem my loyality points. Who cen assist we?"</li></ul> |
| HR | <ul><li>"I ' m considere submitting my ow - weeck notice. Waht It's tehe typical resignation process?"</li><li>"I ' m looking e swich to a part - time sehdule. Whate re rhe requirements?"</li><li>"In ' d loke to fill a formal complain about worksplace discrimination. Who did I contact?"</li></ul> |
| Product | <ul><li>'Whots are your best practices ofr mantain foord quality and freshness?'</li><li>'Whots newbrand ow nut butters dou you carry tahat are peanut - free?'</li><li>'Do you hafe any seasonal nor limited - tíme produts in stock rignt now?'</li></ul> |
| Returns | <ul><li>'My grocery delivary contained items tath where spoiled or pas their expiration date. How dos me get replacements?'</li><li>"I ' d llike to exchange a product I bought in - store. Du hi needs yo bring tie oringal receipt?"</li><li>'I eceibed de demaged item in my online oder. Hou do I’m go about getting a refund?'</li></ul> |
| Logistics | <ul><li>'I have a question about youtr Holiday shiping deathlines and prioritized delivery options'</li><li>'I nedd to change the delivery addrss foy mh upcoming older. How can I go that?'</li><li>'Can jou explain York polices around iterms that approxmatlly out of stock or on backorder?'</li></ul> |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("setfit_model_id")
# Run inference
preds = model("Can you tell I about eny ongoing promoistion onr discounts onteh organic produce?")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## 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 Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 10 | 16.125 | 28 |
| Label | Training Sample Count |
|:-------------|:----------------------|
| Returns | 8 |
| Tech Support | 8 |
| Logistics | 8 |
| HR | 8 |
| Product | 8 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (10, 10)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Framework Versions
- Python: 3.11.8
- SetFit: 1.0.3
- Sentence Transformers: 2.6.1
- Transformers: 4.39.3
- PyTorch: 2.4.0.dev20240413
- Datasets: 2.18.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
Litzy619/V0422MADP1A
|
Litzy619
| 2024-04-22T16:57:36Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:19:27Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP1A
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. -->
# V0422MADP1A
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1496
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 8.3019 | 0.09 | 10 | 2.9847 |
| 5.0266 | 0.18 | 20 | 2.2888 |
| 1.8176 | 0.27 | 30 | 1.0230 |
| 0.4298 | 0.36 | 40 | 0.3093 |
| 0.1876 | 0.45 | 50 | 0.1908 |
| 0.1693 | 0.54 | 60 | 0.1756 |
| 0.1732 | 0.63 | 70 | 0.1951 |
| 0.1662 | 0.73 | 80 | 0.1750 |
| 0.158 | 0.82 | 90 | 0.1724 |
| 0.1572 | 0.91 | 100 | 0.1701 |
| 0.1607 | 1.0 | 110 | 0.1683 |
| 0.1579 | 1.09 | 120 | 0.1536 |
| 0.1567 | 1.18 | 130 | 0.1511 |
| 0.1531 | 1.27 | 140 | 0.1515 |
| 0.1557 | 1.36 | 150 | 0.1616 |
| 0.1516 | 1.45 | 160 | 0.1504 |
| 0.1572 | 1.54 | 170 | 0.1606 |
| 0.1549 | 1.63 | 180 | 0.1562 |
| 0.156 | 1.72 | 190 | 0.1567 |
| 0.1548 | 1.81 | 200 | 0.1527 |
| 0.1583 | 1.9 | 210 | 0.1541 |
| 0.1533 | 1.99 | 220 | 0.1577 |
| 0.158 | 2.08 | 230 | 0.1545 |
| 0.1501 | 2.18 | 240 | 0.1512 |
| 0.1493 | 2.27 | 250 | 0.1502 |
| 0.1506 | 2.36 | 260 | 0.1506 |
| 0.1497 | 2.45 | 270 | 0.1503 |
| 0.1471 | 2.54 | 280 | 0.1499 |
| 0.1479 | 2.63 | 290 | 0.1499 |
| 0.1484 | 2.72 | 300 | 0.1497 |
| 0.148 | 2.81 | 310 | 0.1496 |
| 0.15 | 2.9 | 320 | 0.1496 |
| 0.1507 | 2.99 | 330 | 0.1496 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
Nettem-Gayathri/HI-EN-translation
|
Nettem-Gayathri
| 2024-04-22T16:46:34Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"marian",
"text2text-generation",
"generated_from_keras_callback",
"base_model:Helsinki-NLP/opus-mt-hi-en",
"base_model:finetune:Helsinki-NLP/opus-mt-hi-en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-22T16:46:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
base_model: Helsinki-NLP/opus-mt-hi-en
model-index:
- name: HI-EN-translation
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# HI-EN-translation
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-hi-en](https://huggingface.co/Helsinki-NLP/opus-mt-hi-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.5156
- Validation Loss: 3.4966
- Epoch: 0
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.5156 | 3.4966 | 0 |
### Framework versions
- Transformers 4.38.2
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.15.2
|
Denis641/mlm
|
Denis641
| 2024-04-22T16:45:43Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"codegen",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T11:19:13Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **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]
|
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_ChatGPT_t1_tiny_Seed103
|
bmehrba
| 2024-04-22T16:43:22Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-22T16:43:17Z |
---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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 Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
Aviral2412/finetuning2
|
Aviral2412
| 2024-04-22T16:34:44Z | 111 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_1_0",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-04-22T13:51:56Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- common_voice_1_0
metrics:
- wer
model-index:
- name: finetuning2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_1_0
type: common_voice_1_0
config: en
split: validation
args: en
metrics:
- name: Wer
type: wer
value: 0.4213759213759214
---
<!-- 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. -->
# finetuning2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the common_voice_1_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6883
- Wer: 0.4214
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.5277 | 4.27 | 500 | 2.8353 | 0.9863 |
| 1.2768 | 8.55 | 1000 | 0.7019 | 0.5581 |
| 0.4511 | 12.82 | 1500 | 0.6201 | 0.4726 |
| 0.2591 | 17.09 | 2000 | 0.6428 | 0.4469 |
| 0.1854 | 21.37 | 2500 | 0.6901 | 0.4388 |
| 0.1386 | 25.64 | 3000 | 0.6933 | 0.4259 |
| 0.111 | 29.91 | 3500 | 0.6883 | 0.4214 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Elkelouizajo/bert_mnli_80
|
Elkelouizajo
| 2024-04-22T16:33:47Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-large-cased",
"base_model:finetune:google-bert/bert-large-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-22T16:16:20Z |
---
base_model: google-bert/bert-large-cased
tags:
- generated_from_trainer
model-index:
- name: results_80
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. -->
# results_80
This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_Epistemic_tiny_0.0_Seed101
|
bmehrba
| 2024-04-22T16:26:37Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-22T16:26:34Z |
---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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 Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
Bienvenu2004/donut-handball-pv5
|
Bienvenu2004
| 2024-04-22T16:24:29Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:Bienvenu2004/donut-handball-pv4",
"base_model:finetune:Bienvenu2004/donut-handball-pv4",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-04-21T23:53:49Z |
---
license: mit
base_model: Bienvenu2004/donut-handball-pv4
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-handball-pv5
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. -->
# donut-handball-pv5
This model is a fine-tuned version of [Bienvenu2004/donut-handball-pv4](https://huggingface.co/Bienvenu2004/donut-handball-pv4) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
nasser2001/AraBert-finetuned-text-classification
|
nasser2001
| 2024-04-22T16:22:18Z | 107 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv2",
"base_model:finetune:aubmindlab/bert-base-arabertv2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-22T16:21:50Z |
---
base_model: aubmindlab/bert-base-arabertv2
tags:
- generated_from_trainer
metrics:
- accuracy
- recall
model-index:
- name: AraBert-finetuned-text-classification
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. -->
# AraBert-finetuned-text-classification
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1192
- Macro F1: 0.9610
- Accuracy: 0.9612
- Recall: 0.9612
## 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: 4e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss | Macro F1 | Recall |
|:-------------:|:------:|:----:|:--------:|:---------------:|:--------:|:------:|
| No log | 0.9912 | 56 | 0.9585 | 0.1400 | 0.9582 | 0.9585 |
| No log | 2.0 | 113 | 0.9601 | 0.1324 | 0.9600 | 0.9602 |
| No log | 2.9912 | 169 | 0.9612 | 0.1192 | 0.9610 | 0.9612 |
| No log | 4.0 | 226 | 0.9623 | 0.1393 | 0.9621 | 0.9623 |
| No log | 4.9912 | 282 | 0.9596 | 0.1366 | 0.9596 | 0.9595 |
| No log | 6.0 | 339 | 0.9607 | 0.1590 | 0.9606 | 0.9607 |
| No log | 6.9912 | 395 | 0.9601 | 0.1741 | 0.9600 | 0.9602 |
| No log | 8.0 | 452 | 0.9612 | 0.1824 | 0.9611 | 0.9612 |
| 0.0099 | 8.9912 | 504 | 0.1775 | 0.9617 | 0.9618 | 0.9617 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
conlan/ppo-LunarLander-v3
|
conlan
| 2024-04-22T16:21:55Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-04-22T16:21:50Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -154.38 +/- 119.45
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'conlan/ppo-LunarLander-v3'
'batch_size': 512
'minibatch_size': 128}
```
|
Meganeo/Ramon
|
Meganeo
| 2024-04-22T16:21:32Z | 0 | 0 | null |
[
"es",
"license:apache-2.0",
"region:us"
] | null | 2024-04-22T16:19:11Z |
---
license: apache-2.0
language:
- es
---
|
johnobc/RealisticVision-XL-Lightning
|
johnobc
| 2024-04-22T16:17:06Z | 0 | 0 | null |
[
"license:openrail++",
"region:us"
] | null | 2024-02-27T11:54:01Z |
---
license: openrail++
---
This model is converted to CoreML for us in odysseyapp.io or other Mac-based Stable Diffusion apps. To add this model to Odyssey simply follow these instructions: https://odysseyapp.io/guides/custom-models
More information about the model can be found here: https://civitai.com/models/139562/realvisxl-v40
|
BSC-LT/sentis-matxa-tts-wavenext-multispeaker-ca
|
BSC-LT
| 2024-04-22T16:16:07Z | 5 | 1 |
unity-sentis
|
[
"unity-sentis",
"onnx",
"ca",
"dataset:projecte-aina/festcat_trimmed_denoised",
"dataset:projecte-aina/openslr-slr69-ca-trimmed-denoised",
"license:gpl-3.0",
"region:us"
] | null | 2024-04-20T16:00:44Z |
---
license: gpl-3.0
datasets:
- projecte-aina/festcat_trimmed_denoised
- projecte-aina/openslr-slr69-ca-trimmed-denoised
language:
- ca
library_name: unity-sentis
---
# Matxa TTS for Unity Sentis (Version 1.4.0-pre.2*)
We present a text-to-speech architecture for the Catalan language. It composed of Matcha-TTS and WaveNex.
|
presencesw/Vistral-7B-UIT-CLAIM_2
|
presencesw
| 2024-04-22T16:13:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T16:12:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
RichardErkhov/sophosympatheia_-_Midnight-Miqu-70B-v1.5-4bits
|
RichardErkhov
| 2024-04-22T16:10:35Z | 21 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2311.03099",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-04-22T15:43:57Z |
Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Midnight-Miqu-70B-v1.5 - bnb 4bits
- Model creator: https://huggingface.co/sophosympatheia/
- Original model: https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5/
Original model description:
---
base_model:
- sophosympatheia/Midnight-Miqu-70B-v1.0
- migtissera/Tess-70B-v1.6
library_name: transformers
tags:
- mergekit
- merge
license: other
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/Tn9MBg6.png" alt="MidnightMiqu" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
### Overview
Looking for the 103B version? You can get it from [FluffyKaeloky/Midnight-Miqu-103B-v1.5](https://huggingface.co/FluffyKaeloky/Midnight-Miqu-103B-v1.5).
This is a DARE Linear merge between [sophosympatheia/Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0) and [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6).
This version is close in feel and performance to Midnight Miqu v1.0 but I think it picked up some goodness from Tess. Their EQ Bench scores are virtually the same and their post-EXL2 quant perplexity scores were the same too. However, Midnight Miqu v1.5 passes some tests I use that Midnight Miqu v1.0 fails, without sacrificing writing quality.
This model is uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It may also perform well at other tasks but I have not tested its performance in other areas.
### Long Context Tips
You can run this model out to 32K context with alpha_rope set to 1, just like with Miqu.
### Sampler Tips
* I recommend using Quadratic Sampling (i.e. smoothing factor) for creative work. I think this version performs best with a smoothing factor close to 0.2.
* I recommend using Min-P. Experiment to find your best setting.
* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.
* You don't need to use a high repetition penalty with this model, such as going above 1.10, but experiment with it.
Experiment with any and all of the settings below! What suits my preferences may not suit yours.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.12,
"rep_pen": 1.05,
"rep_pen_range": 2800,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"dynatemp": false,
"min_temp": 0.8,
"max_temp": 1.35,
"dynatemp_exponent": 1,
"smoothing_factor": 0.23,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 2,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"logit_bias": [],
"n": 1,
"rep_pen_size": 0,
"genamt": 500,
"max_length": 32764
}
```
### Prompting Tips
Try the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.
```
{
"story_string": "{{#if system}}{{system}}\n{{/if}}\nCONTEXTUAL INFORMATION\n{{#if wiBefore}}\n- World and character info:\n{{wiBefore}}\n{{/if}}\n{{#if description}}\n- {{char}}'s background and persona:\n{{description}}\n{{/if}}\n{{#if mesExamples}}\n{{mesExamples}}\n{{/if}}\n{{#if personality}}\n{{personality}}\n{{/if}}\n{{#if scenario}}\n- Roleplay scenario:\n{{scenario}}\n{{/if}}\n{{#if wiAfter}}{{wiAfter}}\n{{/if}}\n{{#if persona}}{{persona}}\n{{/if}}",
"example_separator": "",
"chat_start": "---\nTaking the above information into consideration, you must engage with {{user}} and others as {{char}} in the roleplay below this line. Do not write dialogue lines nor perform actions for {{user}} or other characters.\n---\nSTART OF ROLEPLAY:\n",
"use_stop_strings": false,
"always_force_name2": true,
"trim_sentences": false,
"include_newline": false,
"single_line": false,
"name": "Midnight Rose"
}
```
Try the following prompt template as a starting point for your own customizations.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts.
**Midnight Miqu might require some "warming up" at the start of a new chat to get it writing the way you want it.** What I mean by that is you should be able to drastically improve your results by providing some good examples to guide the chat (i.e. few-shot prompting). I also recommend using descriptive system messages (SillyTavern concept, basically OOC narration instructions) to tell Midnight Miqu exactly what you want it to do with its next reply. Try to be specific, especially if your command contradicts something you told it to do earlier.
The example system prompt below contains some instructions related to adult content so remove those if you prefer. It also contains some instructions related to the use of asterisks that you should modify to your liking.
The prompt below has been updated from Midnight Miqu v1.0 after prompting Midnight Miqu to help improve it.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": true,
"system_prompt": "You are an AI creative writing partner, collaborating with me in a dynamic, uncensored roleplaying environment. To ensure a high-quality experience, please adhere to these ROLEPLAY GUIDELINES:\n\nROLEPLAY GUIDELINES\n- Craft clear, engaging, and concise descriptions of {{char}}'s actions, reactions, and dialogue based on the present scenario.\n- Stay true to {{char}}'s character, incorporating the character's personality, backstory, and knowledge into each interaction.\n- Employ rich, descriptive language that immerses readers in the scene.\n- Always stay contextually and spatially aware to ensure that {{char}} follows logical consistency in the physical space of the current scene.\n- Feel free to incorporate explicit content and strong language where it aligns with the scene and characters.\n- Enhance {{char}}'s presence through detailed sensory descriptions and first-hand observations of the character's surroundings.\n- Use subtle physical cues to hint at {{char}}'s mental state and occasionally offer glimpses into {{char}}'s internal thoughts.\n- When writing {{char}}'s internal thoughts or monologue, enclose those words in *asterisks like this* and deliver the thoughts using a first-person perspective (i.e. use \"I\" pronouns). Always use quotes for spoken speech \"like this.\"\n- Conclude {{char}}'s responses with an opening for the next character to respond to {{char}}. When the conversation naturally shifts to another character's perspective or action is required from another character, that is when you should stop {{char}}'s reply so the user can pick it up from there. A great example is when {{char}} asks a question of another character.\n",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "USER: ",
"output_sequence": "ASSISTANT: ",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "SYSTEM: ",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "ASSISTANT (Ensure coherence and authenticity in {{char}}'s actions, thoughts, and dialogues; Focus solely on {{char}}'s interactions within the roleplay): ",
"activation_regex": "",
"name": "Midnight Miqu Roleplay"
}
```
### Instruct Formats
I recommend the Vicuna format. I use a modified version with newlines after USER and ASSISTANT.
```
USER:
{prompt}
ASSISTANT:
```
Mistral's format also works, and in my testing the performance is about the same as using Vicuna.
```
[INST]
{prompt}
[/INST]
```
You could also try ChatML (don't recommend it)
```
<|im_start|>system
{Your system prompt goes here}<|im_end|>
<|im_start|>user
{Your message as the user will go here}<|im_end|>
<|im_start|>assistant
```
### Quantizations
* GGUF
* [mradermacher/Midnight-Miqu-70B-v1.5-GGUF](https://huggingface.co/mradermacher/Midnight-Miqu-70B-v1.5-GGUF) -- Various static GGUF quants
* GPTQ
* [Kotokin/Midnight-Miqu-70B-v1.5_GPTQ32G](https://huggingface.co/Kotokin/Midnight-Miqu-70B-v1.5_GPTQ32G)
* EXL2
* [Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw](https://huggingface.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_4.0bpw)
* [Dracones/Midnight-Miqu-70B-v1.5_exl2_4.5bpw](https://huggingface.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_4.5bpw)
* [Dracones/Midnight-Miqu-70B-v1.5_exl2_5.0bpw](https://huggingface.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_5.0bpw)
* [Dracones/Midnight-Miqu-70B-v1.5_exl2_6.0bpw](https://huggingface.co/Dracones/Midnight-Miqu-70B-v1.5_exl2_6.0bpw)
* If you don't see something you're looking for, [try searching Hugging Face](https://huggingface.co/models?search=midnight-miqu-70b-v1.5). There may be newer quants available than what I've documented here.
### Licence and usage restrictions
<font color="red">152334H/miqu-1-70b-sf was based on a leaked version of one of Mistral's models.</font>
All miqu-derived models, including this merge, are **only suitable for personal use.** Mistral has been cool about it so far, but you should be aware that by downloading this merge you are assuming whatever legal risk is inherent in acquiring and using a model based on leaked weights.
This merge comes with no warranties or guarantees of any kind, but you probably already knew that.
I am not a lawyer and I do not profess to know what we have gotten ourselves into here. You should consult with a lawyer before using any Hugging Face model beyond private use... but definitely don't use this one for that!
## Merge Details
### Merge Method
This model was merged using the linear [DARE](https://arxiv.org/abs/2311.03099) merge method using [152334H_miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) as a base.
### Models Merged
The following models were included in the merge:
* [sophosympatheia/Midnight-Miqu-70B-v1.0](https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.0)
* [migtissera/Tess-70B-v1.6](https://huggingface.co/migtissera/Tess-70B-v1.6)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_linear
base_model: /home/llm/mergequant/models/BASE/152334H_miqu-1-70b-sf # base model
models:
- model: /home/llm/mergequant/models/midnight-miqu-70b-v1.0
- model: /home/llm/mergequant/models/BASE/Tess-70B-v1.6
parameters:
weight: 1.0
dtype: float16
```
### Notes
I tried several methods of merging Midnight Miqu v1.0 with Tess v1.6, and this dare_linear approach worked the best by far. I tried the same approach with other Miqu finetunes like ShinojiResearch/Senku-70B-Full and abideen/Liberated-Miqu-70B, but there was a huge difference in performance. The merge with Tess was the best one.
I also tried the SLERP approach I used to create Midnight Miqu v1.0, only using Tess instead of 152334H_miqu-1-70b in that config, and that result was nowhere near as good either.
|
vikp/surya_layout2
|
vikp
| 2024-04-22T16:09:04Z | 59,075 | 1 |
transformers
|
[
"transformers",
"safetensors",
"segformer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-16T17:03:15Z |
---
license: cc-by-nc-sa-4.0
---
Layout model for [surya](https://github.com/VikParuchuri/surya).
|
nielsr/coref-roberta-base
|
nielsr
| 2024-04-22T16:07:37Z | 174 | 0 |
transformers
|
[
"transformers",
"pytorch",
"exbert",
"en",
"dataset:wikipedia",
"dataset:quoref",
"dataset:docred",
"dataset:fever",
"dataset:gap",
"dataset:winograd_wsc",
"dataset:winogender",
"dataset:nyu-mll/glue",
"arxiv:2004.06870",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
license: apache-2.0
tags:
- exbert
datasets:
- wikipedia
- quoref
- docred
- fever
- gap
- winograd_wsc
- winogender
- nyu-mll/glue
---
# CorefRoBERTa base model
Pretrained model on English language using Masked Language Modeling (MLM) and Mention Reference Prediction (MRP) objectives. It was introduced in
[this paper](https://arxiv.org/abs/2004.06870) and first released in
[this repository](https://github.com/thunlp/CorefBERT).
Disclaimer: The team releasing CorefRoBERTa did not write a model card for this model so this model card has been written by me.
## Model description
CorefRoBERTa is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run
the entire masked sentence through the model and has to predict the masked words. This is different from traditional
recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like
GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the
sentence.
- Mention reference prediction (MRP): this is a novel training task which is proposed to enhance coreferential reasoning ability. MRP utilizes the
mention reference masking strategy to mask one of the repeated mentions and then employs a copybased training objective to predict the masked tokens by copying from other tokens in the sequence.
This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks, especially those that involve coreference resolution. If you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the CorefRoBERTa model as inputs.
### BibTeX entry and citation info
```bibtex
@misc{ye2020coreferential,
title={Coreferential Reasoning Learning for Language Representation},
author={Deming Ye and Yankai Lin and Jiaju Du and Zhenghao Liu and Peng Li and Maosong Sun and Zhiyuan Liu},
year={2020},
eprint={2004.06870},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
automerger/T3qm7Multiverseex26-7B
|
automerger
| 2024-04-22T16:02:08Z | 0 | 0 | null |
[
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-04-22T16:02:04Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
---
# T3qm7Multiverseex26-7B
T3qm7Multiverseex26-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: nlpguy/T3QM7
- model: allknowingroger/MultiverseEx26-7B-slerp
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/T3qm7Multiverseex26-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
lauragordo/mbart-traduct
|
lauragordo
| 2024-04-22T15:59:44Z | 105 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"simplification",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"base_model:finetune:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-22T08:09:55Z |
---
license: mit
base_model: facebook/mbart-large-50
tags:
- simplification
- generated_from_trainer
metrics:
- bleu
model-index:
- name: mbart-traduct
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. -->
# mbart-traduct
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4130
- Bleu: 28.0367
- Gen Len: 19.933
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 125 | 1.6528 | 23.6435 | 19.932 |
| No log | 2.0 | 250 | 1.4130 | 28.0367 | 19.933 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
RawanNeiazy/merge_models
|
RawanNeiazy
| 2024-04-22T15:53:10Z | 0 | 0 | null |
[
"merge",
"mergekit",
"lazymergekit",
"mistralai/Mixtral-8x7B-Instruct-v0.1",
"openai/whisper-large-v3",
"license:apache-2.0",
"region:us"
] | null | 2024-04-22T15:53:09Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mixtral-8x7B-Instruct-v0.1
- openai/whisper-large-v3
---
# rawan
rawan is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
* [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mixtral-8x7B-Instruct-v0.1
layer_range: [0, 32]
- model: openai/whisper-large-v3
layer_range: [0, 32]
merge_method: slerp
base_model: openai/whisper-large-v3
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
Sahil998/codegen-350M-mono-finetuned-python-18k-alpaca-full-dataset
|
Sahil998
| 2024-04-22T15:52:39Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"codegen",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T15:52:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
|
Elkelouizajo/bert_mnli_few
|
Elkelouizajo
| 2024-04-22T15:51:18Z | 106 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-large-cased",
"base_model:finetune:google-bert/bert-large-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-22T15:12:19Z |
---
base_model: google-bert/bert-large-cased
tags:
- generated_from_trainer
model-index:
- name: results_mnli_few
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. -->
# results_mnli_few
This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Litzy619/V0422MADP8
|
Litzy619
| 2024-04-22T15:49:09Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:15:41Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP8
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. -->
# V0422MADP8
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0616
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.881 | 0.09 | 10 | 0.5408 |
| 0.2348 | 0.18 | 20 | 0.1196 |
| 0.1186 | 0.27 | 30 | 0.0956 |
| 0.0994 | 0.36 | 40 | 0.0828 |
| 0.0814 | 0.45 | 50 | 0.0769 |
| 0.0868 | 0.54 | 60 | 0.0796 |
| 0.0835 | 0.63 | 70 | 0.0785 |
| 0.0822 | 0.73 | 80 | 0.0807 |
| 0.0817 | 0.82 | 90 | 0.0692 |
| 0.0773 | 0.91 | 100 | 0.0687 |
| 0.0718 | 1.0 | 110 | 0.0666 |
| 0.064 | 1.09 | 120 | 0.0650 |
| 0.0681 | 1.18 | 130 | 0.0714 |
| 0.0661 | 1.27 | 140 | 0.0664 |
| 0.0598 | 1.36 | 150 | 0.0685 |
| 0.0718 | 1.45 | 160 | 0.0616 |
| 0.0645 | 1.54 | 170 | 0.0630 |
| 0.0659 | 1.63 | 180 | 0.0667 |
| 0.0625 | 1.72 | 190 | 0.0630 |
| 0.0756 | 1.81 | 200 | 0.0679 |
| 0.0669 | 1.9 | 210 | 0.0686 |
| 0.0655 | 1.99 | 220 | 0.0691 |
| 0.0567 | 2.08 | 230 | 0.0691 |
| 0.0583 | 2.18 | 240 | 0.0607 |
| 0.0551 | 2.27 | 250 | 0.0620 |
| 0.0497 | 2.36 | 260 | 0.0661 |
| 0.0542 | 2.45 | 270 | 0.0614 |
| 0.0473 | 2.54 | 280 | 0.0621 |
| 0.0443 | 2.63 | 290 | 0.0634 |
| 0.0492 | 2.72 | 300 | 0.0624 |
| 0.0537 | 2.81 | 310 | 0.0618 |
| 0.0464 | 2.9 | 320 | 0.0616 |
| 0.0526 | 2.99 | 330 | 0.0616 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
deepnet/SN6-71S5
|
deepnet
| 2024-04-22T15:43:21Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-03-29T08:08:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
weege007/llama-3-8b-bnb-4bit-alpaca-lora
|
weege007
| 2024-04-22T15:42:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-19T12:46:20Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** weege007
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Litzy619/V0422MADP7
|
Litzy619
| 2024-04-22T15:41:56Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:15:22Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP7
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. -->
# V0422MADP7
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0616
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.881 | 0.09 | 10 | 0.5408 |
| 0.2348 | 0.18 | 20 | 0.1196 |
| 0.1186 | 0.27 | 30 | 0.0956 |
| 0.0994 | 0.36 | 40 | 0.0828 |
| 0.0814 | 0.45 | 50 | 0.0769 |
| 0.0868 | 0.54 | 60 | 0.0796 |
| 0.0835 | 0.63 | 70 | 0.0785 |
| 0.0822 | 0.73 | 80 | 0.0807 |
| 0.0817 | 0.82 | 90 | 0.0692 |
| 0.0773 | 0.91 | 100 | 0.0687 |
| 0.0718 | 1.0 | 110 | 0.0666 |
| 0.064 | 1.09 | 120 | 0.0650 |
| 0.0681 | 1.18 | 130 | 0.0714 |
| 0.0661 | 1.27 | 140 | 0.0664 |
| 0.0598 | 1.36 | 150 | 0.0685 |
| 0.0718 | 1.45 | 160 | 0.0616 |
| 0.0645 | 1.54 | 170 | 0.0630 |
| 0.0659 | 1.63 | 180 | 0.0667 |
| 0.0625 | 1.72 | 190 | 0.0630 |
| 0.0756 | 1.81 | 200 | 0.0679 |
| 0.0669 | 1.9 | 210 | 0.0686 |
| 0.0655 | 1.99 | 220 | 0.0691 |
| 0.0567 | 2.08 | 230 | 0.0691 |
| 0.0583 | 2.18 | 240 | 0.0607 |
| 0.0551 | 2.27 | 250 | 0.0620 |
| 0.0497 | 2.36 | 260 | 0.0661 |
| 0.0542 | 2.45 | 270 | 0.0614 |
| 0.0473 | 2.54 | 280 | 0.0621 |
| 0.0443 | 2.63 | 290 | 0.0634 |
| 0.0492 | 2.72 | 300 | 0.0624 |
| 0.0537 | 2.81 | 310 | 0.0618 |
| 0.0464 | 2.9 | 320 | 0.0616 |
| 0.0526 | 2.99 | 330 | 0.0616 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
afouda/merge-my-models_again
|
afouda
| 2024-04-22T15:40:49Z | 0 | 0 | null |
[
"merge",
"mergekit",
"lazymergekit",
"EmbeddedLLM/Mistral-7B-Merge-14-v0.1",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2024-04-22T15:40:48Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- EmbeddedLLM/Mistral-7B-Merge-14-v0.1
- gpt2
---
# merge-my-models
merge-my-models is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1)
* [gpt2](https://huggingface.co/gpt2)
## 🧩 Configuration
```yaml{'slices': [{'sources': [{'model': 'EmbeddedLLM/Mistral-7B-Merge-14-v0.1', 'layer_range': [0, 12]}, {'model': 'gpt2', 'layer_range': [0, 12]}]}], 'merge_method': 'slerp', 'base_model': 'allenai/longformer-base-4096', 'parameters': {'t': [{'filter': 'self_attn', 'value': [0, 0.5, 0.3, 0.7, 1]}, {'filter': 'mlp', 'value': [1, 0.5, 0.7, 0.3, 0]}, {'value': 0.5}]}, 'dtype': 'bfloat16'}```
|
MarkBW/vault-suit-pony-xl
|
MarkBW
| 2024-04-22T15:38:14Z | 15 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2024-04-22T15:37:23Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/IMG_3372.jpeg
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: fallout vault suit
---
# vault-suit-pony-xl
<Gallery />
## Model description
By QueenCaffeine
## Trigger words
You should use `fallout vault suit` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/MarkBW/vault-suit-pony-xl/tree/main) them in the Files & versions tab.
|
odyssey-ai/juggernautX
|
odyssey-ai
| 2024-04-22T15:37:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-04-22T01:46:29Z |
---
license: openrail
---This model is converted to CoreML for us in odysseyapp.io or other Mac-based Stable Diffusion apps. To add this model to Odyssey simply follow these instructions: https://odysseyapp.io/guides/custom-models
More information about the model can be found here: https://civitai.com/models/133005/juggernaut-xl
|
Litzy619/V0422MADP6
|
Litzy619
| 2024-04-22T15:34:54Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:14:44Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP6
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. -->
# V0422MADP6
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0611
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9237 | 0.09 | 10 | 0.5636 |
| 0.2396 | 0.18 | 20 | 0.1140 |
| 0.1129 | 0.27 | 30 | 0.0962 |
| 0.1009 | 0.36 | 40 | 0.0912 |
| 0.0866 | 0.45 | 50 | 0.0752 |
| 0.0838 | 0.54 | 60 | 0.0716 |
| 0.0761 | 0.63 | 70 | 0.0737 |
| 0.0775 | 0.73 | 80 | 0.0766 |
| 0.0789 | 0.82 | 90 | 0.0711 |
| 0.0799 | 0.91 | 100 | 0.0681 |
| 0.0754 | 1.0 | 110 | 0.0662 |
| 0.0621 | 1.09 | 120 | 0.0666 |
| 0.0665 | 1.18 | 130 | 0.0840 |
| 0.0693 | 1.27 | 140 | 0.0619 |
| 0.0609 | 1.36 | 150 | 0.0647 |
| 0.062 | 1.45 | 160 | 0.0601 |
| 0.0582 | 1.54 | 170 | 0.0578 |
| 0.0634 | 1.63 | 180 | 0.0575 |
| 0.0579 | 1.72 | 190 | 0.0621 |
| 0.065 | 1.81 | 200 | 0.0574 |
| 0.0522 | 1.9 | 210 | 0.0624 |
| 0.0517 | 1.99 | 220 | 0.0585 |
| 0.0403 | 2.08 | 230 | 0.0630 |
| 0.0433 | 2.18 | 240 | 0.0628 |
| 0.0398 | 2.27 | 250 | 0.0627 |
| 0.0379 | 2.36 | 260 | 0.0656 |
| 0.0431 | 2.45 | 270 | 0.0629 |
| 0.0387 | 2.54 | 280 | 0.0643 |
| 0.0359 | 2.63 | 290 | 0.0633 |
| 0.0419 | 2.72 | 300 | 0.0628 |
| 0.0438 | 2.81 | 310 | 0.0615 |
| 0.0398 | 2.9 | 320 | 0.0612 |
| 0.0432 | 2.99 | 330 | 0.0611 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
relu-ntnu/bart-large-xsum_v4_trained_on_1500_lr_5e-5_r8_a16_all_layers
|
relu-ntnu
| 2024-04-22T15:33:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T15:33:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NoteDance/DeepViT
|
NoteDance
| 2024-04-22T15:29:57Z | 0 | 0 |
tf
|
[
"tf",
"Note",
"deepvit",
"vit",
"vision",
"image-classification",
"dataset:imagenet-1k",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2024-04-22T15:28:08Z |
---
license: apache-2.0
tags:
- Note
- deepvit
- vit
- vision
library_name: tf
datasets:
- imagenet-1k
pipeline_tag: image-classification
---
This model is built by Note, Note can be found [here](https://github.com/NoteDance/Note). The model can be found [here](https://github.com/NoteDance/Note/blob/Note-7.0/Note/neuralnetwork/tf/DeepViT.py). The tutorial can be found [here](https://github.com/NoteDance/Note-documentation/tree/tf-7.0).
|
toyxyz/Greenscreen_lora
|
toyxyz
| 2024-04-22T15:29:48Z | 0 | 1 | null |
[
"region:us"
] | null | 2024-04-22T14:47:59Z |
This is lora, which turns the background into a greenscreen. Add 'green background' to the prompt.
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/62f2b20aeb9e8a5f05cf9a9d/8XeCtzgms_Tt3KjdB6bdX.mp4"></video>
|
kwonsm/gpt2-tldr-kto-updated
|
kwonsm
| 2024-04-22T15:28:19Z | 78 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T15:28:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
JasperLS/deberta-v3-base-injection
|
JasperLS
| 2024-04-22T15:27:22Z | 1,643 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-05-08T14:17:20Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
base_model: microsoft/deberta-v3-base
model-index:
- name: deberta-v3-base-injection
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. -->
# deberta-v3-base-injection
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on the [promp-injection](https://huggingface.co/datasets/JasperLS/prompt-injections) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0673
- Accuracy: 0.9914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 69 | 0.2353 | 0.9741 |
| No log | 2.0 | 138 | 0.0894 | 0.9741 |
| No log | 3.0 | 207 | 0.0673 | 0.9914 |
### Framework versions
- Transformers 4.29.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Takeru/E-PPO
|
Takeru
| 2024-04-22T15:24:34Z | 2 | 0 |
transformers
|
[
"transformers",
"albert",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T15:21:46Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
lauragordo/qya
|
lauragordo
| 2024-04-22T15:24:08Z | 104 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mbart",
"text2text-generation",
"simplification",
"generated_from_trainer",
"base_model:facebook/mbart-large-50",
"base_model:finetune:facebook/mbart-large-50",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-22T10:28:45Z |
---
license: mit
base_model: facebook/mbart-large-50
tags:
- simplification
- generated_from_trainer
metrics:
- bleu
model-index:
- name: qya
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. -->
# qya
This model is a fine-tuned version of [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3158
- Bleu: 2.4862
- Gen Len: 92.91
## 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: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 125 | 3.4402 | 2.3581 | 79.61 |
| No log | 2.0 | 250 | 3.3158 | 2.4862 | 92.91 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
Omriy123/vit-base-patch16-224-in21k-dogs-cats2
|
Omriy123
| 2024-04-22T15:20:38Z | 194 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-04-22T11:55:43Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-in21k-dogs-cats2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-in21k-dogs-cats2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the Dogs_vs_Cats dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0111
- Accuracy: 0.9968
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0691 | 1.0 | 625 | 0.0187 | 0.995 |
| 0.0332 | 2.0 | 1250 | 0.0147 | 0.9958 |
| 0.0446 | 3.0 | 1875 | 0.0139 | 0.9946 |
| 0.0241 | 4.0 | 2500 | 0.0178 | 0.9952 |
| 0.0412 | 5.0 | 3125 | 0.0117 | 0.9968 |
| 0.0683 | 6.0 | 3750 | 0.0168 | 0.995 |
| 0.0081 | 7.0 | 4375 | 0.0143 | 0.9962 |
| 0.0316 | 8.0 | 5000 | 0.0111 | 0.9968 |
| 0.0184 | 9.0 | 5625 | 0.0124 | 0.9968 |
| 0.021 | 10.0 | 6250 | 0.0128 | 0.9964 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.1
|
UXAIR/PyramidsTraining
|
UXAIR
| 2024-04-22T15:16:58Z | 3 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-04-22T14:53:54Z |
---
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 🐶 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: UXAIR/PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
arya123321/yumcraft
|
arya123321
| 2024-04-22T15:14:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T04:44:00Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
qubvel-hf/jozhang97-deta-resnet-50-finetuned-10k-cppe5
|
qubvel-hf
| 2024-04-22T15:13:41Z | 133 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deta",
"object-detection",
"vision",
"generated_from_trainer",
"dataset:cppe-5",
"base_model:jozhang97/deta-resnet-50",
"base_model:finetune:jozhang97/deta-resnet-50",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2024-04-19T20:41:52Z |
---
base_model: jozhang97/deta-resnet-50
tags:
- object-detection
- vision
- generated_from_trainer
model-index:
- name: jozhang97-deta-resnet-50-finetuned-10k-cppe5
results: []
datasets:
- cppe-5
---
<!-- 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. -->
# jozhang97-deta-resnet-50-finetuned-10k-cppe5
This model is a fine-tuned version of [jozhang97/deta-resnet-50](https://huggingface.co/jozhang97/deta-resnet-50) on the cppe-5 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7663
- Map: 0.2022
- Map 50: 0.4588
- Map 75: 0.1509
- Map Small: 0.0948
- Map Medium: 0.1223
- Map Large: 0.2882
- Mar 1: 0.2396
- Mar 10: 0.405
- Mar 100: 0.4238
- Mar Small: 0.2134
- Mar Medium: 0.3177
- Mar Large: 0.5501
- Map Coverall: 0.5051
- Mar 100 Coverall: 0.6628
- Map Face Shield: 0.1207
- Mar 100 Face Shield: 0.3371
- Map Gloves: 0.0983
- Mar 100 Gloves: 0.3115
- Map Goggles: 0.1325
- Mar 100 Goggles: 0.431
- Map Mask: 0.1545
- Mar 100 Mask: 0.3768
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 1337
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Coverall | Mar 100 Coverall | Map Face Shield | Mar 100 Face Shield | Map Gloves | Mar 100 Gloves | Map Goggles | Mar 100 Goggles | Map Mask | Mar 100 Mask |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:------------:|:----------------:|:---------------:|:-------------------:|:----------:|:--------------:|:-----------:|:---------------:|:--------:|:------------:|
| 12.2099 | 0.9953 | 106 | 3.7120 | 0.0067 | 0.0314 | 0.0003 | 0.0 | 0.0006 | 0.0068 | 0.0122 | 0.0353 | 0.0421 | 0.0 | 0.0067 | 0.0451 | 0.0332 | 0.2049 | 0.0001 | 0.0057 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| 3.4288 | 2.0 | 213 | 3.8665 | 0.0117 | 0.0403 | 0.0038 | 0.0 | 0.0004 | 0.0118 | 0.0199 | 0.0448 | 0.0505 | 0.0 | 0.0015 | 0.0592 | 0.0577 | 0.2348 | 0.001 | 0.0143 | 0.0 | 0.0021 | 0.0 | 0.0 | 0.0 | 0.0011 |
| 3.5031 | 2.9953 | 319 | 3.5816 | 0.014 | 0.0448 | 0.0075 | 0.0 | 0.0003 | 0.0143 | 0.0236 | 0.0424 | 0.0486 | 0.0 | 0.0015 | 0.0659 | 0.0693 | 0.2238 | 0.0001 | 0.0071 | 0.0 | 0.0031 | 0.0004 | 0.0024 | 0.0001 | 0.0068 |
| 3.3269 | 4.0 | 426 | 3.1994 | 0.02 | 0.0594 | 0.0065 | 0.0 | 0.0002 | 0.0203 | 0.0242 | 0.0567 | 0.066 | 0.0004 | 0.0027 | 0.1003 | 0.0991 | 0.2768 | 0.0002 | 0.0271 | 0.0 | 0.0078 | 0.0 | 0.0095 | 0.0004 | 0.0085 |
| 3.1804 | 4.9953 | 532 | 3.0309 | 0.0158 | 0.0494 | 0.0066 | 0.0 | 0.0017 | 0.0151 | 0.0262 | 0.0535 | 0.064 | 0.0004 | 0.0092 | 0.1022 | 0.0713 | 0.2451 | 0.001 | 0.0343 | 0.0001 | 0.0104 | 0.0 | 0.0 | 0.0068 | 0.0299 |
| 2.9763 | 6.0 | 639 | 2.7981 | 0.0281 | 0.0939 | 0.0086 | 0.0019 | 0.0023 | 0.03 | 0.0354 | 0.0831 | 0.098 | 0.0147 | 0.0284 | 0.1149 | 0.1311 | 0.3433 | 0.0006 | 0.05 | 0.0 | 0.0052 | 0.0001 | 0.0024 | 0.009 | 0.0893 |
| 2.7978 | 6.9953 | 745 | 2.6720 | 0.0344 | 0.0999 | 0.0131 | 0.0017 | 0.0036 | 0.0338 | 0.0498 | 0.109 | 0.1197 | 0.0263 | 0.0547 | 0.1193 | 0.1616 | 0.372 | 0.005 | 0.0957 | 0.0 | 0.0177 | 0.0004 | 0.0333 | 0.0047 | 0.0797 |
| 2.6679 | 8.0 | 852 | 2.6906 | 0.0363 | 0.0999 | 0.0137 | 0.0146 | 0.0065 | 0.0366 | 0.0438 | 0.0947 | 0.104 | 0.0321 | 0.0332 | 0.1264 | 0.1681 | 0.3415 | 0.0083 | 0.0886 | 0.0004 | 0.0167 | 0.0002 | 0.0119 | 0.0047 | 0.0616 |
| 2.6637 | 8.9953 | 958 | 2.6518 | 0.0319 | 0.1009 | 0.012 | 0.0032 | 0.005 | 0.0309 | 0.0462 | 0.1171 | 0.133 | 0.0089 | 0.0649 | 0.1368 | 0.1457 | 0.4006 | 0.0048 | 0.0943 | 0.0001 | 0.0167 | 0.0004 | 0.0381 | 0.0086 | 0.1153 |
| 2.6412 | 10.0 | 1065 | 2.5417 | 0.0443 | 0.12 | 0.0164 | 0.0548 | 0.0015 | 0.0465 | 0.0567 | 0.1178 | 0.1316 | 0.0591 | 0.0452 | 0.171 | 0.2142 | 0.4183 | 0.0031 | 0.0929 | 0.0002 | 0.0281 | 0.0006 | 0.0381 | 0.0032 | 0.0808 |
| 2.6155 | 10.9953 | 1171 | 2.7743 | 0.0356 | 0.1122 | 0.0216 | 0.0031 | 0.0049 | 0.0435 | 0.0527 | 0.1254 | 0.1399 | 0.0239 | 0.0623 | 0.1768 | 0.1562 | 0.3909 | 0.0054 | 0.08 | 0.0005 | 0.0396 | 0.0015 | 0.0714 | 0.0142 | 0.1175 |
| 2.5621 | 12.0 | 1278 | 2.5292 | 0.0473 | 0.1309 | 0.0258 | 0.003 | 0.0027 | 0.0567 | 0.06 | 0.1265 | 0.1404 | 0.0107 | 0.042 | 0.2137 | 0.2226 | 0.4561 | 0.0038 | 0.0629 | 0.0014 | 0.038 | 0.0044 | 0.0643 | 0.0045 | 0.0808 |
| 2.4871 | 12.9953 | 1384 | 2.5235 | 0.0418 | 0.1211 | 0.0183 | 0.01 | 0.0017 | 0.0451 | 0.0555 | 0.1225 | 0.1389 | 0.0219 | 0.0412 | 0.2163 | 0.1994 | 0.4287 | 0.0021 | 0.0757 | 0.0014 | 0.063 | 0.0018 | 0.0595 | 0.0041 | 0.0678 |
| 2.4874 | 14.0 | 1491 | 2.4597 | 0.0585 | 0.148 | 0.0377 | 0.0142 | 0.0047 | 0.0656 | 0.0746 | 0.1555 | 0.1698 | 0.0353 | 0.0689 | 0.2626 | 0.2705 | 0.4659 | 0.0064 | 0.13 | 0.0012 | 0.0562 | 0.0058 | 0.0929 | 0.0087 | 0.104 |
| 2.4629 | 14.9953 | 1597 | 2.3910 | 0.0587 | 0.1563 | 0.0333 | 0.0039 | 0.0092 | 0.0703 | 0.086 | 0.1896 | 0.1994 | 0.0238 | 0.0976 | 0.299 | 0.2543 | 0.4994 | 0.0115 | 0.1971 | 0.0022 | 0.0448 | 0.003 | 0.1238 | 0.0225 | 0.1316 |
| 2.4231 | 16.0 | 1704 | 2.6214 | 0.0538 | 0.1494 | 0.0256 | 0.0069 | 0.0102 | 0.0676 | 0.0821 | 0.1857 | 0.2013 | 0.0119 | 0.0926 | 0.3454 | 0.2271 | 0.489 | 0.0093 | 0.1914 | 0.0023 | 0.0656 | 0.0038 | 0.1095 | 0.0266 | 0.1508 |
| 2.4166 | 16.9953 | 1810 | 2.5325 | 0.0632 | 0.1615 | 0.0332 | 0.0075 | 0.0096 | 0.0749 | 0.0829 | 0.1743 | 0.1932 | 0.0202 | 0.0914 | 0.3148 | 0.2769 | 0.4756 | 0.0044 | 0.1657 | 0.0038 | 0.0651 | 0.0065 | 0.1286 | 0.0244 | 0.1311 |
| 2.3794 | 18.0 | 1917 | 2.4702 | 0.0843 | 0.1921 | 0.0629 | 0.0217 | 0.0134 | 0.0982 | 0.1035 | 0.2074 | 0.2204 | 0.0226 | 0.1022 | 0.3572 | 0.3649 | 0.5555 | 0.0108 | 0.1957 | 0.0031 | 0.0911 | 0.0143 | 0.1286 | 0.0283 | 0.1311 |
| 2.3384 | 18.9953 | 2023 | 2.5070 | 0.0737 | 0.1736 | 0.0519 | 0.0007 | 0.012 | 0.0866 | 0.0936 | 0.1942 | 0.2156 | 0.0032 | 0.1048 | 0.3206 | 0.32 | 0.5494 | 0.0125 | 0.2029 | 0.0058 | 0.0932 | 0.0054 | 0.0976 | 0.0249 | 0.135 |
| 2.3538 | 20.0 | 2130 | 2.5906 | 0.0647 | 0.1794 | 0.0375 | 0.0033 | 0.0198 | 0.0829 | 0.1073 | 0.2141 | 0.2289 | 0.0059 | 0.1278 | 0.3508 | 0.2457 | 0.5171 | 0.0264 | 0.2214 | 0.0041 | 0.1016 | 0.0081 | 0.1214 | 0.0395 | 0.1831 |
| 2.5129 | 20.9953 | 2236 | 2.9187 | 0.0672 | 0.1692 | 0.0533 | 0.0005 | 0.0144 | 0.0905 | 0.1011 | 0.2206 | 0.2352 | 0.002 | 0.1177 | 0.4044 | 0.2685 | 0.5421 | 0.028 | 0.2929 | 0.0083 | 0.1083 | 0.0016 | 0.0881 | 0.0297 | 0.1446 |
| 2.7734 | 22.0 | 2343 | 3.1307 | 0.0512 | 0.1436 | 0.0257 | 0.0042 | 0.0134 | 0.0861 | 0.0949 | 0.2129 | 0.2249 | 0.0077 | 0.1169 | 0.3711 | 0.1908 | 0.5091 | 0.0348 | 0.27 | 0.0037 | 0.0625 | 0.0057 | 0.1333 | 0.0212 | 0.1497 |
| 2.9978 | 22.9953 | 2449 | 3.1732 | 0.0459 | 0.1255 | 0.027 | 0.0093 | 0.0144 | 0.069 | 0.0842 | 0.2302 | 0.2463 | 0.0128 | 0.1446 | 0.3827 | 0.1654 | 0.5201 | 0.0227 | 0.2414 | 0.0066 | 0.0807 | 0.0138 | 0.1905 | 0.021 | 0.1989 |
| 3.3046 | 24.0 | 2556 | 3.4681 | 0.0339 | 0.1034 | 0.0174 | 0.0159 | 0.0106 | 0.0538 | 0.065 | 0.2138 | 0.2341 | 0.1286 | 0.1402 | 0.3069 | 0.1219 | 0.5024 | 0.0072 | 0.1929 | 0.0037 | 0.0823 | 0.0128 | 0.1738 | 0.0241 | 0.2192 |
| 3.0429 | 24.9953 | 2662 | 2.8145 | 0.0577 | 0.1646 | 0.0351 | 0.0084 | 0.0234 | 0.0838 | 0.0969 | 0.2569 | 0.2758 | 0.0238 | 0.167 | 0.4128 | 0.1906 | 0.5646 | 0.0341 | 0.2957 | 0.0068 | 0.0885 | 0.0075 | 0.2071 | 0.0493 | 0.2232 |
| 2.4662 | 26.0 | 2769 | 2.2661 | 0.0914 | 0.2143 | 0.0758 | 0.0178 | 0.0174 | 0.1372 | 0.127 | 0.2461 | 0.2609 | 0.0259 | 0.167 | 0.3821 | 0.3735 | 0.5579 | 0.0195 | 0.2086 | 0.0065 | 0.0958 | 0.0086 | 0.2071 | 0.0491 | 0.235 |
| 2.1285 | 26.9953 | 2875 | 2.2159 | 0.0968 | 0.2383 | 0.0584 | 0.0347 | 0.0279 | 0.1258 | 0.1285 | 0.2658 | 0.2884 | 0.0488 | 0.1964 | 0.4154 | 0.374 | 0.5579 | 0.0305 | 0.28 | 0.0103 | 0.1161 | 0.0161 | 0.2643 | 0.053 | 0.2237 |
| 2.1347 | 28.0 | 2982 | 2.5509 | 0.0706 | 0.1836 | 0.0441 | 0.0144 | 0.0222 | 0.1024 | 0.1252 | 0.2744 | 0.2974 | 0.033 | 0.2016 | 0.403 | 0.253 | 0.5677 | 0.0291 | 0.26 | 0.0164 | 0.138 | 0.0144 | 0.2833 | 0.0399 | 0.2379 |
| 2.3668 | 28.9953 | 3088 | 2.4054 | 0.0966 | 0.2499 | 0.0643 | 0.0773 | 0.0414 | 0.1119 | 0.1484 | 0.2965 | 0.3169 | 0.1885 | 0.2178 | 0.4012 | 0.3196 | 0.5811 | 0.0377 | 0.2757 | 0.0233 | 0.1432 | 0.0304 | 0.2905 | 0.0722 | 0.2938 |
| 2.2677 | 30.0 | 3195 | 2.1192 | 0.1068 | 0.2461 | 0.0794 | 0.036 | 0.0276 | 0.1437 | 0.1434 | 0.3102 | 0.3274 | 0.1926 | 0.2175 | 0.4496 | 0.3972 | 0.5927 | 0.0419 | 0.3186 | 0.0113 | 0.1453 | 0.0285 | 0.2976 | 0.055 | 0.2831 |
| 2.2587 | 30.9953 | 3301 | 2.6246 | 0.0757 | 0.2145 | 0.0386 | 0.0579 | 0.0372 | 0.0801 | 0.1214 | 0.2851 | 0.3075 | 0.2843 | 0.2145 | 0.3503 | 0.2378 | 0.536 | 0.0198 | 0.2543 | 0.0159 | 0.15 | 0.0357 | 0.3048 | 0.0695 | 0.2927 |
| 2.7778 | 32.0 | 3408 | 2.6193 | 0.0877 | 0.226 | 0.0504 | 0.0423 | 0.0431 | 0.0964 | 0.1302 | 0.2978 | 0.3203 | 0.1287 | 0.232 | 0.3622 | 0.2707 | 0.5768 | 0.0352 | 0.2486 | 0.0169 | 0.1594 | 0.0482 | 0.3167 | 0.0675 | 0.3 |
| 2.2752 | 32.9953 | 3514 | 2.2670 | 0.1165 | 0.286 | 0.0836 | 0.0218 | 0.0533 | 0.1523 | 0.1711 | 0.3059 | 0.3261 | 0.0799 | 0.2451 | 0.4439 | 0.4075 | 0.5909 | 0.0485 | 0.2957 | 0.0223 | 0.1656 | 0.0207 | 0.2857 | 0.0834 | 0.2927 |
| 2.1109 | 34.0 | 3621 | 2.1783 | 0.1182 | 0.2898 | 0.0878 | 0.0207 | 0.0406 | 0.1695 | 0.1547 | 0.3043 | 0.3242 | 0.0858 | 0.2155 | 0.4605 | 0.402 | 0.5762 | 0.0411 | 0.2757 | 0.0214 | 0.1807 | 0.0475 | 0.3 | 0.079 | 0.2881 |
| 2.22 | 34.9953 | 3727 | 2.2399 | 0.1203 | 0.2683 | 0.1016 | 0.0223 | 0.0404 | 0.1578 | 0.1656 | 0.323 | 0.3393 | 0.2078 | 0.2267 | 0.4161 | 0.4218 | 0.6262 | 0.0401 | 0.3057 | 0.0241 | 0.1609 | 0.0327 | 0.3143 | 0.0826 | 0.2893 |
| 2.5775 | 36.0 | 3834 | 3.4084 | 0.0927 | 0.2324 | 0.0649 | 0.022 | 0.0379 | 0.115 | 0.1397 | 0.278 | 0.298 | 0.093 | 0.2007 | 0.3901 | 0.2973 | 0.5427 | 0.0292 | 0.2414 | 0.0228 | 0.125 | 0.0481 | 0.3 | 0.0661 | 0.2808 |
| 2.9257 | 36.9953 | 3940 | 2.5430 | 0.1051 | 0.2535 | 0.0731 | 0.0469 | 0.0389 | 0.1393 | 0.1607 | 0.317 | 0.3411 | 0.1976 | 0.2365 | 0.4454 | 0.3591 | 0.5909 | 0.0259 | 0.2857 | 0.0213 | 0.2104 | 0.0442 | 0.3262 | 0.075 | 0.2921 |
| 2.1766 | 38.0 | 4047 | 2.2615 | 0.1232 | 0.2757 | 0.1013 | 0.0573 | 0.0413 | 0.154 | 0.1625 | 0.3287 | 0.3527 | 0.2073 | 0.24 | 0.4692 | 0.4301 | 0.6073 | 0.0324 | 0.3143 | 0.0249 | 0.2042 | 0.0528 | 0.3357 | 0.0756 | 0.3023 |
| 2.2174 | 38.9953 | 4153 | 2.1064 | 0.139 | 0.3199 | 0.1186 | 0.0442 | 0.0476 | 0.1704 | 0.1744 | 0.3165 | 0.3317 | 0.2105 | 0.2171 | 0.4155 | 0.4647 | 0.6122 | 0.0287 | 0.2343 | 0.051 | 0.2016 | 0.05 | 0.3071 | 0.1007 | 0.3034 |
| 2.0069 | 40.0 | 4260 | 2.1137 | 0.1274 | 0.2796 | 0.1086 | 0.0711 | 0.0423 | 0.1375 | 0.1705 | 0.3207 | 0.3397 | 0.1597 | 0.225 | 0.4317 | 0.4744 | 0.6293 | 0.0293 | 0.2443 | 0.0292 | 0.2146 | 0.021 | 0.3048 | 0.0831 | 0.3056 |
| 2.0335 | 40.9953 | 4366 | 2.0880 | 0.1278 | 0.2884 | 0.1103 | 0.0374 | 0.0416 | 0.1395 | 0.1631 | 0.3068 | 0.3302 | 0.1615 | 0.2089 | 0.4282 | 0.4782 | 0.628 | 0.0342 | 0.2357 | 0.0179 | 0.1745 | 0.0397 | 0.3571 | 0.0689 | 0.2554 |
| 2.1166 | 42.0 | 4473 | 2.1062 | 0.1303 | 0.299 | 0.1098 | 0.0324 | 0.0445 | 0.1594 | 0.1709 | 0.3222 | 0.3467 | 0.1433 | 0.2403 | 0.4547 | 0.45 | 0.6012 | 0.0586 | 0.2814 | 0.0363 | 0.2208 | 0.0355 | 0.3381 | 0.0708 | 0.2921 |
| 1.9831 | 42.9953 | 4579 | 2.0591 | 0.142 | 0.3266 | 0.1181 | 0.0548 | 0.0532 | 0.166 | 0.1881 | 0.3438 | 0.3595 | 0.1447 | 0.249 | 0.4546 | 0.4789 | 0.6268 | 0.0469 | 0.2686 | 0.0471 | 0.2255 | 0.0494 | 0.3476 | 0.0877 | 0.3288 |
| 2.0249 | 44.0 | 4686 | 2.1750 | 0.1331 | 0.3023 | 0.108 | 0.0487 | 0.0453 | 0.1746 | 0.1843 | 0.3345 | 0.3565 | 0.1692 | 0.2419 | 0.449 | 0.4406 | 0.6226 | 0.065 | 0.3257 | 0.0396 | 0.2234 | 0.0421 | 0.3119 | 0.0783 | 0.2989 |
| 2.1806 | 44.9953 | 4792 | 2.1668 | 0.1299 | 0.3103 | 0.1009 | 0.0523 | 0.0368 | 0.1587 | 0.1604 | 0.3055 | 0.3313 | 0.1054 | 0.2044 | 0.4652 | 0.4617 | 0.6165 | 0.0563 | 0.27 | 0.0277 | 0.2089 | 0.0341 | 0.2833 | 0.0697 | 0.278 |
| 1.9865 | 46.0 | 4899 | 2.1022 | 0.1337 | 0.3124 | 0.1006 | 0.0331 | 0.0573 | 0.1585 | 0.177 | 0.332 | 0.3522 | 0.121 | 0.2542 | 0.4359 | 0.451 | 0.6159 | 0.0228 | 0.2386 | 0.0341 | 0.2339 | 0.0467 | 0.3524 | 0.1137 | 0.3203 |
| 2.0517 | 46.9953 | 5005 | 2.0253 | 0.1437 | 0.3449 | 0.1205 | 0.038 | 0.058 | 0.1937 | 0.1831 | 0.3327 | 0.3535 | 0.1973 | 0.2473 | 0.4626 | 0.4515 | 0.6201 | 0.0507 | 0.2729 | 0.0345 | 0.2255 | 0.0846 | 0.3452 | 0.0969 | 0.304 |
| 1.8315 | 48.0 | 5112 | 1.9870 | 0.1528 | 0.3572 | 0.1134 | 0.0298 | 0.0525 | 0.2276 | 0.1953 | 0.3464 | 0.3668 | 0.1622 | 0.2418 | 0.5026 | 0.4801 | 0.6305 | 0.0633 | 0.2929 | 0.0491 | 0.2354 | 0.0861 | 0.3714 | 0.0855 | 0.304 |
| 1.8105 | 48.9953 | 5218 | 1.9702 | 0.1436 | 0.3227 | 0.1212 | 0.0437 | 0.0429 | 0.2046 | 0.1911 | 0.3314 | 0.3517 | 0.1959 | 0.2283 | 0.4668 | 0.4903 | 0.6372 | 0.0616 | 0.27 | 0.0348 | 0.2479 | 0.066 | 0.331 | 0.0653 | 0.2723 |
| 1.7928 | 50.0 | 5325 | 1.9007 | 0.1549 | 0.3631 | 0.1243 | 0.0413 | 0.0563 | 0.2183 | 0.2026 | 0.3488 | 0.369 | 0.13 | 0.2439 | 0.4907 | 0.4838 | 0.6476 | 0.0679 | 0.2914 | 0.0585 | 0.2495 | 0.0713 | 0.3571 | 0.0931 | 0.2994 |
| 1.7696 | 50.9953 | 5431 | 1.9786 | 0.1511 | 0.3631 | 0.1196 | 0.0232 | 0.0595 | 0.2346 | 0.204 | 0.3601 | 0.3848 | 0.1566 | 0.2821 | 0.4908 | 0.4767 | 0.6287 | 0.0758 | 0.3171 | 0.0484 | 0.25 | 0.0748 | 0.4214 | 0.0801 | 0.3068 |
| 1.7579 | 52.0 | 5538 | 1.9172 | 0.1628 | 0.3659 | 0.132 | 0.0341 | 0.0698 | 0.2224 | 0.2133 | 0.3753 | 0.3968 | 0.2334 | 0.2767 | 0.5034 | 0.498 | 0.6524 | 0.0869 | 0.3343 | 0.0472 | 0.2646 | 0.0795 | 0.4024 | 0.1023 | 0.3305 |
| 1.7493 | 52.9953 | 5644 | 1.8806 | 0.1599 | 0.3797 | 0.1159 | 0.0522 | 0.0727 | 0.2175 | 0.2111 | 0.3677 | 0.3969 | 0.2264 | 0.2769 | 0.5069 | 0.4723 | 0.6482 | 0.0713 | 0.3314 | 0.0662 | 0.2792 | 0.066 | 0.3833 | 0.1239 | 0.3424 |
| 1.7134 | 54.0 | 5751 | 1.9045 | 0.1663 | 0.3599 | 0.1239 | 0.0372 | 0.0754 | 0.2379 | 0.2115 | 0.3648 | 0.3911 | 0.1374 | 0.2659 | 0.522 | 0.4841 | 0.6476 | 0.0674 | 0.2829 | 0.0588 | 0.2807 | 0.1056 | 0.4095 | 0.1155 | 0.335 |
| 1.7611 | 54.9953 | 5857 | 1.9231 | 0.1606 | 0.3967 | 0.1162 | 0.0462 | 0.0722 | 0.2079 | 0.2059 | 0.3553 | 0.3789 | 0.1245 | 0.2652 | 0.4963 | 0.4928 | 0.639 | 0.0981 | 0.3429 | 0.0482 | 0.2406 | 0.0709 | 0.3643 | 0.093 | 0.3079 |
| 1.7949 | 56.0 | 5964 | 1.8811 | 0.182 | 0.4156 | 0.1361 | 0.0724 | 0.0814 | 0.2389 | 0.2195 | 0.3801 | 0.3956 | 0.1577 | 0.2722 | 0.4984 | 0.4928 | 0.6366 | 0.1112 | 0.3386 | 0.0831 | 0.2812 | 0.1114 | 0.4119 | 0.1113 | 0.3096 |
| 1.7328 | 56.9953 | 6070 | 1.8393 | 0.1802 | 0.4138 | 0.143 | 0.0649 | 0.0869 | 0.2511 | 0.2184 | 0.3899 | 0.4079 | 0.2281 | 0.2913 | 0.5215 | 0.502 | 0.6512 | 0.1311 | 0.3471 | 0.0729 | 0.263 | 0.0834 | 0.4357 | 0.1115 | 0.3424 |
| 1.6581 | 58.0 | 6177 | 1.8911 | 0.1818 | 0.4149 | 0.1458 | 0.0567 | 0.0851 | 0.254 | 0.2101 | 0.3745 | 0.3931 | 0.1613 | 0.266 | 0.5025 | 0.4957 | 0.6512 | 0.1287 | 0.3443 | 0.0735 | 0.2505 | 0.083 | 0.3905 | 0.1282 | 0.3288 |
| 1.6535 | 58.9953 | 6283 | 1.8845 | 0.1712 | 0.4046 | 0.1308 | 0.0742 | 0.0783 | 0.2418 | 0.2009 | 0.3816 | 0.3972 | 0.2855 | 0.2882 | 0.4836 | 0.5077 | 0.6518 | 0.0892 | 0.3229 | 0.0578 | 0.2589 | 0.0898 | 0.4286 | 0.1113 | 0.3237 |
| 1.6606 | 60.0 | 6390 | 1.9009 | 0.1779 | 0.4172 | 0.1297 | 0.0798 | 0.0946 | 0.2376 | 0.2137 | 0.3647 | 0.3862 | 0.235 | 0.2811 | 0.4988 | 0.4882 | 0.6354 | 0.0948 | 0.2729 | 0.0739 | 0.2854 | 0.1031 | 0.4 | 0.1297 | 0.3373 |
| 1.6766 | 60.9953 | 6496 | 1.9100 | 0.18 | 0.4238 | 0.1385 | 0.092 | 0.087 | 0.2338 | 0.2233 | 0.384 | 0.3989 | 0.2325 | 0.2852 | 0.4949 | 0.4817 | 0.636 | 0.0971 | 0.3171 | 0.0831 | 0.2714 | 0.1194 | 0.4429 | 0.1185 | 0.3271 |
| 1.7227 | 62.0 | 6603 | 1.8943 | 0.173 | 0.3979 | 0.1294 | 0.0935 | 0.0739 | 0.2254 | 0.2172 | 0.3674 | 0.389 | 0.1763 | 0.2647 | 0.5074 | 0.5011 | 0.6463 | 0.1096 | 0.3286 | 0.0731 | 0.2854 | 0.0727 | 0.3786 | 0.1087 | 0.3062 |
| 1.6737 | 62.9953 | 6709 | 1.9753 | 0.1645 | 0.398 | 0.1159 | 0.0841 | 0.0849 | 0.2382 | 0.2065 | 0.3693 | 0.3867 | 0.2084 | 0.2771 | 0.4862 | 0.4483 | 0.6171 | 0.0934 | 0.3114 | 0.0869 | 0.3005 | 0.0684 | 0.3738 | 0.1256 | 0.3305 |
| 1.6768 | 64.0 | 6816 | 1.8531 | 0.1759 | 0.401 | 0.1408 | 0.065 | 0.0851 | 0.2592 | 0.2178 | 0.3635 | 0.3872 | 0.1646 | 0.2774 | 0.5118 | 0.5142 | 0.6463 | 0.0906 | 0.3114 | 0.062 | 0.2844 | 0.0843 | 0.3571 | 0.1284 | 0.3367 |
| 1.6543 | 64.9953 | 6922 | 1.8840 | 0.1828 | 0.4139 | 0.1364 | 0.0964 | 0.0979 | 0.2575 | 0.2195 | 0.3841 | 0.4027 | 0.1731 | 0.3073 | 0.504 | 0.4805 | 0.6409 | 0.1276 | 0.3529 | 0.088 | 0.2969 | 0.093 | 0.3881 | 0.1252 | 0.335 |
| 1.6153 | 66.0 | 7029 | 1.9622 | 0.1684 | 0.3804 | 0.1298 | 0.0652 | 0.0743 | 0.235 | 0.2143 | 0.3699 | 0.391 | 0.1739 | 0.2697 | 0.4861 | 0.4784 | 0.6293 | 0.1049 | 0.33 | 0.0461 | 0.2745 | 0.0881 | 0.4024 | 0.1244 | 0.3186 |
| 1.6143 | 66.9953 | 7135 | 1.8685 | 0.178 | 0.394 | 0.1375 | 0.0621 | 0.0948 | 0.2429 | 0.2178 | 0.3883 | 0.4114 | 0.2767 | 0.2987 | 0.5148 | 0.5007 | 0.6372 | 0.113 | 0.3529 | 0.0771 | 0.2932 | 0.0741 | 0.4238 | 0.1252 | 0.3497 |
| 1.6326 | 68.0 | 7242 | 1.8852 | 0.1745 | 0.3983 | 0.1289 | 0.0568 | 0.0764 | 0.23 | 0.2163 | 0.3856 | 0.4044 | 0.2097 | 0.2825 | 0.5231 | 0.5052 | 0.6482 | 0.1178 | 0.3514 | 0.0672 | 0.2849 | 0.0624 | 0.3905 | 0.1196 | 0.3469 |
| 1.6507 | 68.9953 | 7348 | 1.8130 | 0.1821 | 0.4003 | 0.1556 | 0.06 | 0.1034 | 0.2663 | 0.2157 | 0.3933 | 0.4077 | 0.3075 | 0.2933 | 0.5294 | 0.5087 | 0.6579 | 0.1021 | 0.3529 | 0.0749 | 0.2969 | 0.0992 | 0.4024 | 0.1256 | 0.3282 |
| 1.5577 | 70.0 | 7455 | 1.8646 | 0.1775 | 0.3934 | 0.1375 | 0.0802 | 0.0721 | 0.2704 | 0.2248 | 0.386 | 0.4115 | 0.2884 | 0.2959 | 0.5086 | 0.5029 | 0.6518 | 0.0973 | 0.3357 | 0.0637 | 0.2979 | 0.0962 | 0.4262 | 0.1273 | 0.3458 |
| 1.5784 | 70.9953 | 7561 | 1.7817 | 0.1912 | 0.4163 | 0.1575 | 0.0783 | 0.0846 | 0.2848 | 0.229 | 0.3986 | 0.4184 | 0.3431 | 0.2889 | 0.532 | 0.5187 | 0.6695 | 0.1152 | 0.3429 | 0.0723 | 0.3005 | 0.0953 | 0.4214 | 0.1546 | 0.3576 |
| 1.506 | 72.0 | 7668 | 1.7696 | 0.1938 | 0.4236 | 0.1464 | 0.0803 | 0.1105 | 0.2592 | 0.2337 | 0.3895 | 0.4151 | 0.1821 | 0.2993 | 0.5415 | 0.5214 | 0.6634 | 0.0966 | 0.34 | 0.0775 | 0.2932 | 0.1302 | 0.4262 | 0.1434 | 0.3525 |
| 1.5384 | 72.9953 | 7774 | 1.8206 | 0.189 | 0.4304 | 0.1388 | 0.0836 | 0.0839 | 0.2802 | 0.2353 | 0.3889 | 0.4041 | 0.2535 | 0.272 | 0.5363 | 0.5046 | 0.6591 | 0.109 | 0.3357 | 0.0785 | 0.2922 | 0.1227 | 0.4 | 0.1301 | 0.3333 |
| 1.5022 | 74.0 | 7881 | 1.8055 | 0.197 | 0.4341 | 0.1495 | 0.1022 | 0.0883 | 0.2747 | 0.2256 | 0.3898 | 0.4084 | 0.1913 | 0.2789 | 0.5424 | 0.5225 | 0.661 | 0.1332 | 0.34 | 0.0687 | 0.2891 | 0.1366 | 0.4286 | 0.1239 | 0.3232 |
| 1.5157 | 74.9953 | 7987 | 1.7750 | 0.1992 | 0.4404 | 0.1541 | 0.104 | 0.0937 | 0.2516 | 0.2332 | 0.3996 | 0.4154 | 0.1952 | 0.2843 | 0.5587 | 0.5311 | 0.6762 | 0.1331 | 0.36 | 0.0869 | 0.2964 | 0.1116 | 0.4071 | 0.1332 | 0.3373 |
| 1.4439 | 76.0 | 8094 | 1.8431 | 0.1877 | 0.4295 | 0.1374 | 0.0729 | 0.0844 | 0.2654 | 0.225 | 0.3975 | 0.4176 | 0.2597 | 0.2999 | 0.544 | 0.5215 | 0.6579 | 0.1322 | 0.3471 | 0.0673 | 0.2875 | 0.0936 | 0.4643 | 0.1241 | 0.3311 |
| 1.4989 | 76.9953 | 8200 | 1.8236 | 0.1907 | 0.4343 | 0.1425 | 0.055 | 0.095 | 0.2776 | 0.2183 | 0.3861 | 0.4059 | 0.1827 | 0.2745 | 0.5308 | 0.5238 | 0.6634 | 0.1084 | 0.33 | 0.0754 | 0.2849 | 0.1091 | 0.4 | 0.1368 | 0.3514 |
| 1.4759 | 78.0 | 8307 | 1.7953 | 0.1973 | 0.4484 | 0.15 | 0.077 | 0.0949 | 0.2868 | 0.2369 | 0.402 | 0.4161 | 0.1753 | 0.2936 | 0.5428 | 0.5239 | 0.6634 | 0.124 | 0.3514 | 0.0865 | 0.3109 | 0.0945 | 0.3976 | 0.1574 | 0.3571 |
| 1.4302 | 78.9953 | 8413 | 1.8257 | 0.1921 | 0.4446 | 0.1476 | 0.0569 | 0.1067 | 0.2706 | 0.2263 | 0.3944 | 0.4135 | 0.17 | 0.2982 | 0.5529 | 0.508 | 0.6591 | 0.1226 | 0.36 | 0.0782 | 0.3021 | 0.1114 | 0.3976 | 0.1403 | 0.3486 |
| 1.4879 | 80.0 | 8520 | 1.8216 | 0.1977 | 0.4478 | 0.1511 | 0.0607 | 0.1093 | 0.28 | 0.233 | 0.4025 | 0.4213 | 0.1947 | 0.3143 | 0.5392 | 0.5125 | 0.6573 | 0.1274 | 0.3514 | 0.087 | 0.3177 | 0.1197 | 0.4286 | 0.1421 | 0.3514 |
| 1.4674 | 80.9953 | 8626 | 1.8194 | 0.186 | 0.4463 | 0.1374 | 0.056 | 0.0953 | 0.28 | 0.2289 | 0.3882 | 0.4068 | 0.1807 | 0.2878 | 0.5349 | 0.5061 | 0.6457 | 0.1236 | 0.3371 | 0.0662 | 0.276 | 0.0917 | 0.4143 | 0.1424 | 0.361 |
| 1.4603 | 82.0 | 8733 | 1.7888 | 0.1925 | 0.4437 | 0.142 | 0.0727 | 0.0973 | 0.2871 | 0.2257 | 0.39 | 0.4054 | 0.1866 | 0.2849 | 0.5352 | 0.5014 | 0.6463 | 0.1343 | 0.33 | 0.0787 | 0.2995 | 0.1028 | 0.4 | 0.1455 | 0.3514 |
| 1.4798 | 82.9953 | 8839 | 1.8245 | 0.1922 | 0.4473 | 0.1358 | 0.0696 | 0.1126 | 0.2677 | 0.2258 | 0.3993 | 0.415 | 0.1864 | 0.3067 | 0.5338 | 0.5 | 0.6427 | 0.1361 | 0.3314 | 0.0868 | 0.3073 | 0.0965 | 0.4286 | 0.1414 | 0.365 |
| 1.4253 | 84.0 | 8946 | 1.7753 | 0.1932 | 0.4601 | 0.1409 | 0.0801 | 0.1169 | 0.2778 | 0.2316 | 0.4027 | 0.4172 | 0.1949 | 0.3178 | 0.5316 | 0.5108 | 0.6567 | 0.124 | 0.3329 | 0.0831 | 0.2937 | 0.0979 | 0.4452 | 0.1501 | 0.3576 |
| 1.4397 | 84.9953 | 9052 | 1.7778 | 0.2023 | 0.4698 | 0.1484 | 0.0747 | 0.1264 | 0.2903 | 0.2312 | 0.4076 | 0.423 | 0.2089 | 0.3229 | 0.5092 | 0.5011 | 0.6494 | 0.1172 | 0.3129 | 0.107 | 0.3214 | 0.1282 | 0.45 | 0.158 | 0.3814 |
| 1.4086 | 86.0 | 9159 | 1.7550 | 0.2015 | 0.472 | 0.1626 | 0.0926 | 0.1138 | 0.2819 | 0.2308 | 0.4085 | 0.4275 | 0.2033 | 0.3209 | 0.5665 | 0.5092 | 0.6591 | 0.1319 | 0.3543 | 0.0894 | 0.3042 | 0.1295 | 0.4571 | 0.1473 | 0.3627 |
| 1.4261 | 86.9953 | 9265 | 1.7907 | 0.1997 | 0.4662 | 0.1461 | 0.1078 | 0.1194 | 0.2946 | 0.2402 | 0.3993 | 0.4178 | 0.1766 | 0.3128 | 0.5568 | 0.5156 | 0.661 | 0.1338 | 0.3371 | 0.0888 | 0.301 | 0.1181 | 0.431 | 0.1423 | 0.3588 |
| 1.3943 | 88.0 | 9372 | 1.7906 | 0.1891 | 0.4431 | 0.133 | 0.0591 | 0.1053 | 0.2925 | 0.234 | 0.4004 | 0.419 | 0.1942 | 0.3249 | 0.5366 | 0.5019 | 0.6488 | 0.1097 | 0.3271 | 0.0877 | 0.3052 | 0.1014 | 0.4524 | 0.1446 | 0.3616 |
| 1.4016 | 88.9953 | 9478 | 1.7760 | 0.1929 | 0.4462 | 0.1424 | 0.0828 | 0.1114 | 0.2675 | 0.238 | 0.4045 | 0.4254 | 0.2069 | 0.3236 | 0.5476 | 0.5031 | 0.6616 | 0.121 | 0.3329 | 0.0897 | 0.3104 | 0.098 | 0.4548 | 0.1525 | 0.3672 |
| 1.3955 | 90.0 | 9585 | 1.7786 | 0.1955 | 0.4452 | 0.1534 | 0.0952 | 0.1147 | 0.2789 | 0.2383 | 0.3979 | 0.4189 | 0.2118 | 0.3166 | 0.5502 | 0.4977 | 0.6378 | 0.1173 | 0.3286 | 0.0946 | 0.312 | 0.1176 | 0.4405 | 0.1502 | 0.3757 |
| 1.4014 | 90.9953 | 9691 | 1.7644 | 0.1975 | 0.4627 | 0.1508 | 0.0865 | 0.1134 | 0.2852 | 0.2394 | 0.4042 | 0.4219 | 0.1963 | 0.3221 | 0.5521 | 0.5035 | 0.6549 | 0.1326 | 0.3371 | 0.0991 | 0.3193 | 0.1154 | 0.4429 | 0.1367 | 0.3554 |
| 1.3626 | 92.0 | 9798 | 1.7705 | 0.1993 | 0.4752 | 0.143 | 0.07 | 0.1152 | 0.2855 | 0.2361 | 0.4022 | 0.4221 | 0.1801 | 0.3227 | 0.5575 | 0.5064 | 0.6555 | 0.1307 | 0.3486 | 0.0898 | 0.299 | 0.1215 | 0.4429 | 0.1481 | 0.3644 |
| 1.3655 | 92.9953 | 9904 | 1.7689 | 0.2081 | 0.4735 | 0.1425 | 0.0975 | 0.1234 | 0.2855 | 0.2413 | 0.4007 | 0.422 | 0.198 | 0.3091 | 0.5544 | 0.5014 | 0.6616 | 0.1282 | 0.3386 | 0.0918 | 0.3073 | 0.1587 | 0.4381 | 0.1603 | 0.3644 |
| 1.3913 | 94.0 | 10011 | 1.7834 | 0.2003 | 0.4624 | 0.149 | 0.0951 | 0.1082 | 0.2823 | 0.2407 | 0.4025 | 0.4227 | 0.2077 | 0.2991 | 0.5416 | 0.4993 | 0.6628 | 0.1198 | 0.3371 | 0.1071 | 0.3208 | 0.1209 | 0.419 | 0.1545 | 0.3734 |
| 1.4071 | 94.9953 | 10117 | 1.7609 | 0.2046 | 0.4761 | 0.1521 | 0.0954 | 0.1232 | 0.2968 | 0.2392 | 0.4064 | 0.4251 | 0.1986 | 0.3225 | 0.5536 | 0.4975 | 0.6579 | 0.1359 | 0.3614 | 0.1034 | 0.3068 | 0.1342 | 0.4238 | 0.1522 | 0.3757 |
| 1.3651 | 96.0 | 10224 | 1.7628 | 0.2016 | 0.4717 | 0.1487 | 0.0995 | 0.1203 | 0.2895 | 0.2381 | 0.3965 | 0.416 | 0.1919 | 0.3111 | 0.5508 | 0.5079 | 0.664 | 0.1225 | 0.3371 | 0.0941 | 0.3005 | 0.1301 | 0.4095 | 0.1533 | 0.3689 |
| 1.3568 | 96.9953 | 10330 | 1.7858 | 0.2008 | 0.4706 | 0.15 | 0.0885 | 0.1223 | 0.286 | 0.2415 | 0.3952 | 0.4149 | 0.2044 | 0.31 | 0.5375 | 0.5041 | 0.6604 | 0.1152 | 0.3214 | 0.0946 | 0.3052 | 0.135 | 0.4214 | 0.1552 | 0.3661 |
| 1.3502 | 98.0 | 10437 | 1.7613 | 0.2041 | 0.4599 | 0.1525 | 0.0904 | 0.1249 | 0.2908 | 0.2395 | 0.4097 | 0.4292 | 0.2154 | 0.3233 | 0.553 | 0.5051 | 0.664 | 0.127 | 0.3557 | 0.1024 | 0.3177 | 0.1333 | 0.4357 | 0.1528 | 0.3729 |
| 1.3658 | 98.9953 | 10543 | 1.7623 | 0.2016 | 0.4641 | 0.1501 | 0.0908 | 0.1219 | 0.2895 | 0.2398 | 0.4019 | 0.4208 | 0.2099 | 0.3157 | 0.5456 | 0.5034 | 0.6598 | 0.1234 | 0.3314 | 0.095 | 0.3115 | 0.1326 | 0.4262 | 0.1536 | 0.3751 |
| 1.3272 | 99.5305 | 10600 | 1.7663 | 0.2022 | 0.4588 | 0.1509 | 0.0948 | 0.1223 | 0.2882 | 0.2396 | 0.405 | 0.4238 | 0.2134 | 0.3177 | 0.5501 | 0.5051 | 0.6628 | 0.1207 | 0.3371 | 0.0983 | 0.3115 | 0.1325 | 0.431 | 0.1545 | 0.3768 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.18.0
- Tokenizers 0.19.0
|
relu-ntnu/bart-large-xsum_v4_trained_on_1000_lr_5e-5_r8_a16_all_layers
|
relu-ntnu
| 2024-04-22T15:12:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T15:12:01Z |
---
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 🤗 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 Needed]
## Model Card Contact
[More Information Needed]
|
AlbertG3/BankStockEmbed
|
AlbertG3
| 2024-04-22T15:11:17Z | 4 | 2 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-04-22T13:54:10Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 76 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 15,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
sudhir2016/llama-3-8b-Instruct-lora-merged
|
sudhir2016
| 2024-04-22T15:03:44Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T14:56:59Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** sudhir2016
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Tensorride/Classifier_with_external_sets_03
|
Tensorride
| 2024-04-22T15:03:27Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"base_model:finetune:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-04-22T13:51:17Z |
---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Classifier_with_external_sets_03
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. -->
# Classifier_with_external_sets_03
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6931
- Accuracy: 0.5034
## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.9983 | 289 | 0.6943 | 0.5034 |
| 0.7019 | 2.0 | 579 | 0.6932 | 0.4966 |
| 0.7019 | 2.9983 | 868 | 0.7004 | 0.5034 |
| 0.6978 | 4.0 | 1158 | 0.6968 | 0.4966 |
| 0.6978 | 4.9983 | 1447 | 0.6953 | 0.4966 |
| 0.6961 | 6.0 | 1737 | 0.6932 | 0.5034 |
| 0.6958 | 6.9983 | 2026 | 0.6932 | 0.5034 |
| 0.6958 | 8.0 | 2316 | 0.6934 | 0.4966 |
| 0.6942 | 8.9983 | 2605 | 0.6940 | 0.5034 |
| 0.6942 | 9.9827 | 2890 | 0.6931 | 0.5034 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
himum/sn6_0l3
|
himum
| 2024-04-22T15:00:17Z | 68 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T09:25:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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### Results
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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|
Superd4/lasttest
|
Superd4
| 2024-04-22T14:58:32Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"ja",
"dataset:manga109s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2024-04-22T14:58:05Z |
---
language: ja
tags:
- image-to-text
license: apache-2.0
datasets:
- manga109s
---
# Manga OCR
Optical character recognition for Japanese text, with the main focus being Japanese manga.
It uses [Vision Encoder Decoder](https://huggingface.co/docs/transformers/model_doc/vision-encoder-decoder) framework.
Manga OCR can be used as a general purpose printed Japanese OCR, but its main goal was to provide a high quality
text recognition, robust against various scenarios specific to manga:
- both vertical and horizontal text
- text with furigana
- text overlaid on images
- wide variety of fonts and font styles
- low quality images
Code is available [here](https://github.com/kha-white/manga_ocr).
|
relu-ntnu/bart-large-xsum_v4_trained_on_250_lr_5e-5_r8_a16_all_layers
|
relu-ntnu
| 2024-04-22T14:50:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:49:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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|
relu-ntnu/bart-large-xsum_v4_trained_on_100_lr_5e-5_r8_a16_all_layers
|
relu-ntnu
| 2024-04-22T14:46:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:46:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
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|
Litzy619/V0422MADP5
|
Litzy619
| 2024-04-22T14:45:24Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:14:12Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP5
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. -->
# V0422MADP5
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0611
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9237 | 0.09 | 10 | 0.5636 |
| 0.2396 | 0.18 | 20 | 0.1140 |
| 0.1129 | 0.27 | 30 | 0.0962 |
| 0.1009 | 0.36 | 40 | 0.0912 |
| 0.0866 | 0.45 | 50 | 0.0752 |
| 0.0838 | 0.54 | 60 | 0.0716 |
| 0.0761 | 0.63 | 70 | 0.0737 |
| 0.0775 | 0.73 | 80 | 0.0766 |
| 0.0789 | 0.82 | 90 | 0.0711 |
| 0.0799 | 0.91 | 100 | 0.0681 |
| 0.0754 | 1.0 | 110 | 0.0662 |
| 0.0621 | 1.09 | 120 | 0.0666 |
| 0.0665 | 1.18 | 130 | 0.0840 |
| 0.0693 | 1.27 | 140 | 0.0619 |
| 0.0609 | 1.36 | 150 | 0.0647 |
| 0.062 | 1.45 | 160 | 0.0601 |
| 0.0582 | 1.54 | 170 | 0.0578 |
| 0.0634 | 1.63 | 180 | 0.0575 |
| 0.0579 | 1.72 | 190 | 0.0621 |
| 0.065 | 1.81 | 200 | 0.0574 |
| 0.0522 | 1.9 | 210 | 0.0624 |
| 0.0517 | 1.99 | 220 | 0.0585 |
| 0.0403 | 2.08 | 230 | 0.0630 |
| 0.0433 | 2.18 | 240 | 0.0628 |
| 0.0398 | 2.27 | 250 | 0.0627 |
| 0.0379 | 2.36 | 260 | 0.0656 |
| 0.0431 | 2.45 | 270 | 0.0629 |
| 0.0387 | 2.54 | 280 | 0.0643 |
| 0.0359 | 2.63 | 290 | 0.0633 |
| 0.0419 | 2.72 | 300 | 0.0628 |
| 0.0438 | 2.81 | 310 | 0.0615 |
| 0.0398 | 2.9 | 320 | 0.0612 |
| 0.0432 | 2.99 | 330 | 0.0611 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
relu-ntnu/bart-large-xsum_v4_trained_on_25_lr_5e-5_r8_a16_all_layers
|
relu-ntnu
| 2024-04-22T14:43:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:43:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
relu-ntnu/bart-large-xsum_v4_trained_on_15_lr_5e-5_r8_a16_all_layers
|
relu-ntnu
| 2024-04-22T14:43:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:42:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
relu-ntnu/bart-large-xsum_v4_trained_on_10_lr_5e-5_r8_a16_all_layers
|
relu-ntnu
| 2024-04-22T14:42:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:42:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed]
|
Lasserino/AIChess-8B-5DE-EXL2
|
Lasserino
| 2024-04-22T14:41:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:adapter:unsloth/llama-3-8b-Instruct-bnb-4bit",
"4-bit",
"exl2",
"region:us"
] | null | 2024-04-22T14:03:30Z |
---
library_name: peft
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Model Card for Model ID
#### Model Type: Exllamav2 4bit
## Description
Model finetuned for five epochs on the Lasserino/AIChess dataset.
The dataset consists of over 1500 high ELO games, with each move shown in both chess notatio and an ASCII graphical representation of the board, ex:
. . k . . . . r
p p . b . r p .
. . . . p . . .
. . . p P . q .
. P p N . n . .
P . P . . . R P
. . B . . P . P
. . . . R Q . K
Tags are used to more precisely imprint behaviour. An example of this is when we're displaying the previous round's chess board state and the current round's chess board state. When we do that we use the tags [previous_chessboard]chessboard[/previous_chessboard] and [current_chessboard]chessboard[/current_chessboard] in an effort to further separate the representations of the current chess board and previous chess board in the high-dimensional space of the language model's learned knowledge.
## The Prompt Format
<pre>
<|start_header_id|>user<|end_header_id|>
------------------------------------------------------------------------------------------------------------------------------------------
We are playing a game of chess. When given the current state of the chess board, you must respond with your next move in chess notation (e.g., 'Qdc8#', 'R2xb3', 'Rfc3+').
The chess board will be represented as follows:
- Uppercase letters represent white pieces
- Lowercase letters represent black pieces
- '.' represents an empty square
When replying with a move, use standard chess notation (e.g., formatted_examples).
Additionally, provide a brief thought process behind your move, considering factors such as:
- Attacking opponent's pieces
- Defending your own pieces
- Controlling key squares
- Improving piece positioning
- Exploiting opponent's weaknesses
- Planning for future moves
Use the following format for your response:
[current_move]your_move[/current_move]
[thought_process]your_thought_process[/thought_process]
------------------------------------------------------------------------------------------------------------------------------------------
[round_number]26[/round_number]
[current_turn]Black[/current_turn]
[previous_move]Rh8[/previous_move]
[previous_chessboard]
. . k . . . . r
p p . b . r p .
. . . . p . . .
. . . p P . q .
. P p N . n . .
P . P . R . . P
. . B . . P . P
. . . . R Q . K
[/previous_chessboard]
[my_move]Rg3[/my_move]
[current_chessboard]
. . k . . . . r
p p . b . r p .
. . . . p . . .
. . . p P . q .
. P p N . n . .
P . P . . . R P
. . B . . P . P
. . . . R Q . K
[/current_chessboard]
[eval_score]0.02[/eval_score]
[clock_time]0:00:50[/clock_time]
Your move:
</pre>
|
martyyz/llama3-8b-mart-unsloth-merged
|
martyyz
| 2024-04-22T14:39:17Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T14:34:46Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** martyyz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
relu-ntnu/bart-large-cnn_v4_trained_on_1500_lr_5e-5_r8_a16_all_layers
|
relu-ntnu
| 2024-04-22T14:39:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:38:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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]
|
KeyonZeng/lion-llama3-8b
|
KeyonZeng
| 2024-04-22T14:38:30Z | 35 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:mlabonne/orpo-dpo-mix-40k",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T05:41:26Z |
---
library_name: transformers
license: apache-2.0
metrics:
- accuracy
datasets:
- mlabonne/orpo-dpo-mix-40k
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
osrojo/weather
|
osrojo
| 2024-04-22T14:38:09Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2024-04-22T14:38:06Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
AbhinavKrishnan/medicine_listing
|
AbhinavKrishnan
| 2024-04-22T14:37:45Z | 3 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2024-04-18T05:25:29Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
model-index:
- name: medicine_listing
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. -->
# medicine_listing
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
TTTTao725/molt5-augmented-contrastive-0-small-caption-encoder
|
TTTTao725
| 2024-04-22T14:35:57Z | 34 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:35:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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]
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|
TTTTao725/molt5-augmented-contrastive-0-small-smiles-encoder
|
TTTTao725
| 2024-04-22T14:35:48Z | 34 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:35:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
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#### Metrics
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[More Information Needed]
### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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- **Hardware Type:** [More Information Needed]
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|
borggAI/alpha-model-1-22042024
|
borggAI
| 2024-04-22T14:30:44Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2024-04-22T14:23:20Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [nbeerbower/bophades-mistral-math-DPO-7B](https://huggingface.co/nbeerbower/bophades-mistral-math-DPO-7B)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.38.2
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="borggAI/alpha-model-1-22042024",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = True
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.7)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
Why is drinking water so healthy?</s>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"borggAI/alpha-model-1-22042024",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"borggAI/alpha-model-1-22042024",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = True
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.7)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "borggAI/alpha-model-1-22042024" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "How are you?</s>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 256
# model.generation_config.do_sample = True
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.7)
# model.generation_config.repetition_penalty = float(1.0)
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
MistralForCausalLM(
(model): MistralModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralSdpaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=1024, bias=False)
(v_proj): Linear(in_features=4096, out_features=1024, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): Linear(in_features=4096, out_features=14336, bias=False)
(up_proj): Linear(in_features=4096, out_features=14336, bias=False)
(down_proj): Linear(in_features=14336, out_features=4096, bias=False)
(act_fn): SiLU()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
piegarroni/Llama-2-7b-hf-csv-conversion-cense-v2
|
piegarroni
| 2024-04-22T14:29:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:29:38Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
NevlonCrates/DialoGPT-small-Alastor
|
NevlonCrates
| 2024-04-22T14:27:38Z | 124 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-03-30T13:48:31Z |
---
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 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Likich/mistral-finetune-qualcoding
|
Likich
| 2024-04-22T14:24:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:23:39Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### 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
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|
Srimouli04/llama3_lora_adapters
|
Srimouli04
| 2024-04-22T14:23:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T14:22:51Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Srimouli04
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Resi/layoutlmv3-sagemaker
|
Resi
| 2024-04-22T14:16:26Z | 104 | 0 |
transformers
|
[
"transformers",
"safetensors",
"layoutlmv3",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-04-22T14:16:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
michaelw37/sc65
|
michaelw37
| 2024-04-22T14:12:08Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-22T14:10:34Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
Jai1432002/whisper-small-hi
|
Jai1432002
| 2024-04-22T14:08:14Z | 86 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-04-21T11:25:07Z |
---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Hi - Sanchit Gandhi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: None
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 32.33725556590198
---
<!-- 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 Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4383
- Wer: 32.3373
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0936 | 2.4465 | 1000 | 0.2981 | 35.1477 |
| 0.0225 | 4.8930 | 2000 | 0.3531 | 33.3404 |
| 0.0013 | 7.3394 | 3000 | 0.4149 | 32.4007 |
| 0.0005 | 9.7859 | 4000 | 0.4383 | 32.3373 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
Srimouli04/llama3_ft_lora_model
|
Srimouli04
| 2024-04-22T14:04:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T12:14:55Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Litzy619/V0422MADP2
|
Litzy619
| 2024-04-22T14:01:14Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:finetune:microsoft/phi-2",
"region:us"
] | null | 2024-04-22T05:41:17Z |
---
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: V0422MADP2
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. -->
# V0422MADP2
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0322
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9195 | 0.09 | 10 | 0.9281 |
| 0.2943 | 0.18 | 20 | 0.1207 |
| 0.1134 | 0.27 | 30 | 0.0961 |
| 0.1076 | 0.36 | 40 | 0.0790 |
| 0.0865 | 0.45 | 50 | 0.0884 |
| 0.0878 | 0.54 | 60 | 0.0803 |
| 0.0822 | 0.63 | 70 | 0.0710 |
| 0.0763 | 0.73 | 80 | 0.0918 |
| 0.0874 | 0.82 | 90 | 0.0723 |
| 0.0807 | 0.91 | 100 | 0.0708 |
| 0.0724 | 1.0 | 110 | 0.0660 |
| 0.0644 | 1.09 | 120 | 0.0658 |
| 0.0686 | 1.18 | 130 | 0.0652 |
| 0.0626 | 1.27 | 140 | 0.0680 |
| 0.0607 | 1.36 | 150 | 0.0635 |
| 0.0645 | 1.45 | 160 | 0.0618 |
| 0.0551 | 1.54 | 170 | 0.0510 |
| 0.0474 | 1.63 | 180 | 0.0397 |
| 0.0296 | 1.72 | 190 | 0.0355 |
| 0.0381 | 1.81 | 200 | 0.0366 |
| 0.0344 | 1.9 | 210 | 0.0324 |
| 0.0304 | 1.99 | 220 | 0.0327 |
| 0.023 | 2.08 | 230 | 0.0355 |
| 0.0281 | 2.18 | 240 | 0.0334 |
| 0.0233 | 2.27 | 250 | 0.0324 |
| 0.0325 | 2.36 | 260 | 0.0368 |
| 0.0259 | 2.45 | 270 | 0.0321 |
| 0.0219 | 2.54 | 280 | 0.0325 |
| 0.0226 | 2.63 | 290 | 0.0324 |
| 0.0258 | 2.72 | 300 | 0.0321 |
| 0.0255 | 2.81 | 310 | 0.0320 |
| 0.0235 | 2.9 | 320 | 0.0322 |
| 0.027 | 2.99 | 330 | 0.0322 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.14.1
|
Kommunarus/ppo-Pyramids
|
Kommunarus
| 2024-04-22T13:58:48Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2024-04-22T13:58:40Z |
---
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 🐶 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: Kommunarus/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
VIN-IT/results
|
VIN-IT
| 2024-04-22T13:58:43Z | 106 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:finetune:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-04-22T04:36:38Z |
---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2907
- Rouge1: 0.4527
- Rouge2: 0.3281
- Rougel: 0.4353
- Rougelsum: 0.4353
## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| No log | 1.0 | 2 | 2.5861 | 0.1012 | 0.0357 | 0.1012 | 0.1012 |
| No log | 2.0 | 4 | 2.4501 | 0.2033 | 0.125 | 0.2033 | 0.2033 |
| No log | 3.0 | 6 | 2.3942 | 0.3133 | 0.2348 | 0.2475 | 0.2475 |
| No log | 4.0 | 8 | 2.3593 | 0.3133 | 0.2348 | 0.2475 | 0.2475 |
| No log | 5.0 | 10 | 2.3353 | 0.2095 | 0.1384 | 0.1960 | 0.1960 |
| No log | 6.0 | 12 | 2.3181 | 0.4082 | 0.3309 | 0.3947 | 0.3947 |
| No log | 7.0 | 14 | 2.3055 | 0.4082 | 0.3309 | 0.3947 | 0.3947 |
| No log | 8.0 | 16 | 2.2976 | 0.4427 | 0.3309 | 0.4129 | 0.4120 |
| No log | 9.0 | 18 | 2.2927 | 0.4527 | 0.3281 | 0.4353 | 0.4353 |
| No log | 10.0 | 20 | 2.2907 | 0.4527 | 0.3281 | 0.4353 | 0.4353 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
sataayu/molt5-augmented-contrastive-0-small-smiles-encoder
|
sataayu
| 2024-04-22T13:58:05Z | 35 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T13:57:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### 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]
|
Mihaiii/gte-micro-v3
|
Mihaiii
| 2024-04-22T13:55:40Z | 370 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"gte",
"mteb",
"license:mit",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-04-22T11:17:52Z |
---
license: mit
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- gte
- mteb
model-index:
- name: gte-micro-test
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 71.43283582089552
- type: ap
value: 33.56235301308992
- type: f1
value: 65.18510976313922
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 77.72055
- type: ap
value: 72.30281215701287
- type: f1
value: 77.62429097469116
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 38.956
- type: f1
value: 38.59075995638611
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 41.14317775707504
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 31.79440862639374
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 80.40259740259741
- type: f1
value: 80.33885811790022
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 44.54
- type: f1
value: 39.40201192446353
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 70.5904
- type: ap
value: 64.61751544665012
- type: f1
value: 70.47776028292148
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 90.49703602371181
- type: f1
value: 90.05253119123799
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 67.52393980848153
- type: f1
value: 49.95609666042009
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.4969737726967
- type: f1
value: 66.32116772424203
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.54741089441829
- type: f1
value: 73.47537036064044
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 66.6912
- type: ap
value: 12.157396278930436
- type: f1
value: 51.00574525406295
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 59.29258630447085
- type: f1
value: 59.6485358241374
---
---
# gte-micro-v3
This is a distill of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny).
## Intended purpose
<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>
## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('Mihaiii/gte-micro-v3')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Mihaiii/gte-micro-v3')
model = AutoModel.from_pretrained('Mihaiii/gte-micro-v3')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small))
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
|
ogdanneedham/mistral-gs-0.6-lora
|
ogdanneedham
| 2024-04-22T13:53:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-22T13:52:28Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** ogdanneedham
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral 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)
|
adriansanz/p1
|
adriansanz
| 2024-04-22T11:57:36Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"zero-shot-classification",
"base_model:projecte-aina/roberta-base-ca-v2-cawikitc",
"base_model:finetune:projecte-aina/roberta-base-ca-v2-cawikitc",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
zero-shot-classification
| 2024-04-22T09:42:09Z |
---
license: apache-2.0
base_model: projecte-aina/roberta-base-ca-v2-cawikitc
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: p1
results: []
pipeline_tag: zero-shot-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. -->
# p1
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cawikitc](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cawikitc) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8254
- Accuracy: 0.5
- Precision: 0.25
- Recall: 0.5
- F1: 0.3333
- Ratio: 1.0
## 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: 2
- seed: 47
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- lr_scheduler_warmup_steps: 4
- num_epochs: 2
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.8263 | 0.38 | 10 | 0.8199 | 0.5 | 0.5 | 0.5 | 0.3473 | 0.0163 |
| 0.8283 | 0.75 | 20 | 0.8389 | 0.5 | 0.25 | 0.5 | 0.3333 | 1.0 |
| 0.8167 | 1.13 | 30 | 0.8325 | 0.5 | 0.25 | 0.5 | 0.3333 | 1.0 |
| 0.8183 | 1.51 | 40 | 0.8228 | 0.4973 | 0.2493 | 0.4973 | 0.3321 | 0.9973 |
| 0.8178 | 1.89 | 50 | 0.8254 | 0.5 | 0.25 | 0.5 | 0.3333 | 1.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
Niggendar/clampdxlFindForgetyou_v10
|
Niggendar
| 2024-04-22T11:57:23Z | 156 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-04-22T11:47:02Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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|
maharengarajan/dummy-model
|
maharengarajan
| 2024-04-22T11:56:00Z | 162 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-04-22T11:55:05Z |
---
library_name: transformers
tags: []
---
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|
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