Search is not available for this dataset
pipeline_tag
stringclasses 48
values | library_name
stringclasses 205
values | text
stringlengths 0
18.3M
| metadata
stringlengths 2
1.07B
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stringlengths 5
122
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listlengths 1
1.84k
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stringlengths 25
25
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|---|---|---|---|---|---|---|---|---|
text-generation
|
transformers
|
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [motherfucker0/zhun02](https://huggingface.co/motherfucker0/zhun02)
* [motherfucker0/zhun01](https://huggingface.co/motherfucker0/zhun01)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: motherfucker0/zhun02
layer_range: [0, 30]
- model: motherfucker0/zhun01
layer_range: [0, 30]
merge_method: slerp
base_model: motherfucker0/zhun01
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: 0.05
dtype: bfloat16
```
|
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["motherfucker0/zhun02", "motherfucker0/zhun01"]}
|
motherfucker0/zhen09
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:motherfucker0/zhun02",
"base_model:motherfucker0/zhun01",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T09:51:36+00:00
|
reinforcement-learning
|
stable-baselines3
|
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AkiraHase -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga AkiraHase -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga AkiraHase
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "652.00 +/- 142.43", "name": "mean_reward", "verified": false}]}]}]}
|
AkiraHase/dqn-SpaceInvadersNoFrameskip-v4
| null |
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T09:51:40+00:00
|
reinforcement-learning
| null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
{"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-CartPole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
|
lightyip/Reinforce-CartPole-v1
| null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null |
2024-04-25T09:51:44+00:00
|
null | null |
The GGUF files of [RDson/Phi-3-mini-code-finetune-128k-instruct-v1](https://huggingface.co/RDson/Phi-3-mini-code-finetune-128k-instruct-v1).
|
{"license": "other", "tags": ["gguf", "phi", "3", "code", "phi-3"], "license_name": "phi-3", "license_link": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/raw/main/LICENSE"}
|
RDson/Phi-3-mini-code-finetune-128k-instruct-v1-GGUF
| null |
[
"gguf",
"phi",
"3",
"code",
"phi-3",
"license:other",
"region:us"
] | null |
2024-04-25T09:53:14+00:00
|
null | null |
{}
|
chegri1/giiads
| null |
[
"region:us"
] | null |
2024-04-25T09:53:30+00:00
|
|
text-generation
|
transformers
|
# 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]
|
{"library_name": "transformers", "tags": ["unsloth"]}
|
hoang1123/llam3-8b-4bit-unsloth
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-25T09:53:35+00:00
|
null | null |
{}
|
oxyzen/headshot
| null |
[
"region:us"
] | null |
2024-04-25T09:53:47+00:00
|
|
text-classification
|
transformers
|
{"license": "apache-2.0"}
|
yukiura/bert-dailydialogue
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T09:54:32+00:00
|
|
null |
transformers
|
{"license": "llama3"}
|
mysticai/llama-3-8B-instruct-trt-bf16-batch1-maxin1024-maxout1024
| null |
[
"transformers",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T09:56:16+00:00
|
|
automatic-speech-recognition
|
transformers
|
# Model Card for whisper-large-v3-taiwanese-hakka
<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of the Taiwanese Hakka [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3), which uses the ids of each dialect as prompts during training, to experiment whether the addition of prompts to the finetune of whisper when using multiple dialects will give better results.
## Dialect and Id
- 四縣: htia_sixian
- 海陸: htia_hailu
- 大埔: htia_dapu
- 饒平: htia_raoping
- 詔安: htia_zhaoan
- 南四縣: htia_nansixian
### Training process
The training of the model was performed with the following hyperparameters
- Batch size: 32
- Epochs: 3
- Warmup Steps: 50
- Total Steps: 42549
- Learning rate: 7e-5
- Data augmentation: No
### How to use
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "formospeech/whisper-large-v3-taiwanese-hakka"
dialect_id = "htia_sixian"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=30,
batch_size=16,
torch_dtype=torch_dtype,
device=device,
)
generate_kwargs = {"language": "Chinese", "prompt_ids": torch.from_numpy(processor.get_prompt_ids(dialect_id)).to(device)}
transcription = pipe("path/to/my_audio.wav", generate_kwargs=generate_kwargs)
print(transcription.replace(f" {dialect_id}", ""))
```
|
{"language": ["hak"], "license": "mit", "pipeline_tag": "automatic-speech-recognition"}
|
formospeech/whisper-large-v3-taiwanese-hakka
| null |
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hak",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T09:56:20+00:00
|
null |
peft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-x-base-pegasus_article_summarization_base2
This model is a fine-tuned version of [google/pegasus-x-base](https://huggingface.co/google/pegasus-x-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.5067
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 50 | 3.9509 |
| No log | 2.0 | 100 | 3.5692 |
| No log | 3.0 | 150 | 3.5067 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.15.0
|
{"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "google/pegasus-x-base", "model-index": [{"name": "pegasus-x-base-pegasus_article_summarization_base2", "results": []}]}
|
LAKSHM11-G/pegasus-x-base-pegasus_article_summarization_base2
| null |
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/pegasus-x-base",
"region:us"
] | null |
2024-04-25T09:56:57+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MARBERT-QADI
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0342
- Macro F1: 0.5099
- Accuracy: 0.5138
- Recall: 0.5136
- Precision: 0.6223
## 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-06
- train_batch_size: 128
- eval_batch_size: 128
- 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 | Macro F1 | Accuracy | Recall | Precision |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.8588 | 1.0 | 1125 | 0.7883 | 0.7550 | 0.7554 | 0.7552 | 0.7609 |
| 0.7475 | 2.0 | 2250 | 0.7718 | 0.7632 | 0.7634 | 0.7631 | 0.7653 |
| 0.6527 | 3.0 | 3375 | 0.7758 | 0.7668 | 0.7673 | 0.7671 | 0.7679 |
| 0.5654 | 4.0 | 4500 | 0.7845 | 0.7665 | 0.7673 | 0.7671 | 0.7682 |
| 0.5001 | 5.0 | 5625 | 0.8068 | 0.7650 | 0.7663 | 0.7660 | 0.7657 |
| 0.4641 | 6.0 | 6750 | 0.8216 | 0.7647 | 0.7658 | 0.7655 | 0.7650 |
| 0.4049 | 7.0 | 7875 | 0.8393 | 0.7645 | 0.7654 | 0.7649 | 0.7657 |
| 0.3773 | 8.0 | 9000 | 0.8477 | 0.7651 | 0.7657 | 0.7654 | 0.7659 |
| 0.3393 | 9.0 | 10125 | 0.8569 | 0.7663 | 0.7669 | 0.7665 | 0.7670 |
| 0.3383 | 10.0 | 11250 | 0.8589 | 0.7663 | 0.7669 | 0.7666 | 0.7667 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "recall", "precision"], "model-index": [{"name": "MARBERT-QADI", "results": []}]}
|
MohamedAtta-AI/MARBERT-QADI
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T09:57:15+00:00
|
null |
peft
|
## 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: False
- bnb_4bit_compute_dtype: float16
- PEFT 0.4.0
|
{"library_name": "peft"}
|
mmosko/Sheldon_BOT_llama_2
| null |
[
"peft",
"tensorboard",
"region:us"
] | null |
2024-04-25T09:58:28+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-410m_mz-131_PasswordMatch
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-410m", "model-index": [{"name": "robust_llm_pythia-410m_mz-131_PasswordMatch", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-410m_mz-131_PasswordMatch
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T09:59:31+00:00
|
null | null |
{}
|
MattNandavong/bert-base-uncased-fine-tuned-swin-faq
| null |
[
"region:us"
] | null |
2024-04-25T09:59:31+00:00
|
|
null |
peft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0
base_model_config: T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: true
hub_model_id: T3Q-LLM-sft1.0-dpo1.0_100QA
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: admin_data.csv
type: alpaca
# The below are defaults. only set what's needed if you use a different column name.
# system_prompt: ""
# system_format: "{system}"
# field_system: system
# field_instruction: instruction
# field_input: input
# field_output: output
# format: |-
# Human: {instruction} {input}
# Assistant:
# no_input_format: "{instruction} "
# dataset_prepared_path: yanolja_preprocessed_data
dataset_prepared_path: last_run_prepared
val_set_size: 0.2
output_dir: ./T3Q-LLM-sft1.0-dpo1.0_100QA
adapter: qlora
lora_model_dir:
# device_map: [0,1,3]
sequence_len: 4096
sample_packing: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: axolotl_T3Q
wandb_entity:
wandb_watch:
wandb_run_id: T3Q_mod_100
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 10
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "<|im_end|>"
unk_token: "<unk>"
pad_token: "</s>" # EOS와 PAD가 동일
```
</details><br>
# T3Q-LLM-sft1.0-dpo1.0_100QA
This model is a fine-tuned version of [T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0](https://huggingface.co/T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6629
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8867 | 0.2 | 1 | 1.0564 |
| 0.9385 | 0.4 | 2 | 1.0557 |
| 0.9454 | 0.6 | 3 | 1.0535 |
| 0.8469 | 0.8 | 4 | 1.0494 |
| 0.8583 | 1.0 | 5 | 1.0412 |
| 0.8691 | 1.2 | 6 | 1.0262 |
| 0.8306 | 1.4 | 7 | 1.0073 |
| 0.8302 | 1.6 | 8 | 0.9834 |
| 0.8028 | 1.8 | 9 | 0.9556 |
| 0.8987 | 2.0 | 10 | 0.9181 |
| 0.7826 | 2.2 | 11 | 0.8777 |
| 0.6936 | 2.4 | 12 | 0.8379 |
| 0.6453 | 2.6 | 13 | 0.8035 |
| 0.6613 | 2.8 | 14 | 0.7741 |
| 0.6548 | 3.0 | 15 | 0.7483 |
| 0.6078 | 3.2 | 16 | 0.7238 |
| 0.6185 | 3.4 | 17 | 0.7004 |
| 0.5293 | 3.6 | 18 | 0.6815 |
| 0.5617 | 3.8 | 19 | 0.6666 |
| 0.4845 | 4.0 | 20 | 0.6541 |
| 0.4904 | 4.2 | 21 | 0.6443 |
| 0.5375 | 4.4 | 22 | 0.6349 |
| 0.5099 | 4.6 | 23 | 0.6254 |
| 0.4286 | 4.8 | 24 | 0.6187 |
| 0.4952 | 5.0 | 25 | 0.6133 |
| 0.4394 | 5.2 | 26 | 0.6089 |
| 0.4974 | 5.4 | 27 | 0.6041 |
| 0.3877 | 5.6 | 28 | 0.5999 |
| 0.4992 | 5.8 | 29 | 0.5952 |
| 0.4187 | 6.0 | 30 | 0.5902 |
| 0.4302 | 6.2 | 31 | 0.5871 |
| 0.3861 | 6.4 | 32 | 0.5836 |
| 0.3966 | 6.6 | 33 | 0.5805 |
| 0.4399 | 6.8 | 34 | 0.5786 |
| 0.3732 | 7.0 | 35 | 0.5777 |
| 0.3727 | 7.2 | 36 | 0.5780 |
| 0.3442 | 7.4 | 37 | 0.5786 |
| 0.3477 | 7.6 | 38 | 0.5801 |
| 0.3763 | 7.8 | 39 | 0.5808 |
| 0.3498 | 8.0 | 40 | 0.5824 |
| 0.312 | 8.2 | 41 | 0.5834 |
| 0.3282 | 8.4 | 42 | 0.5869 |
| 0.2938 | 8.6 | 43 | 0.5912 |
| 0.2908 | 8.8 | 44 | 0.5967 |
| 0.3083 | 9.0 | 45 | 0.6031 |
| 0.244 | 9.2 | 46 | 0.6111 |
| 0.2894 | 9.4 | 47 | 0.6228 |
| 0.2318 | 9.6 | 48 | 0.6353 |
| 0.2375 | 9.8 | 49 | 0.6474 |
| 0.1939 | 10.0 | 50 | 0.6629 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["axolotl", "generated_from_trainer"], "base_model": "T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0", "model-index": [{"name": "T3Q-LLM-sft1.0-dpo1.0_100QA", "results": []}]}
|
superiort/T3Q-LLM-sft1.0-dpo1.0_100QA_10epochs
| null |
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T09:59:52+00:00
|
text-generation
|
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
happylayers/sc19
| null |
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:00:20+00:00
|
reinforcement-learning
|
stable-baselines3
|
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Alvaroooooooo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Alvaroooooooo -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Alvaroooooooo
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "722.50 +/- 238.77", "name": "mean_reward", "verified": false}]}]}]}
|
Alvaroooooooo/dqn-SpaceInvadersNoFrameskip-v4
| null |
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T10:01:26+00:00
|
null | null |
{}
|
Tobius/gtp2_model
| null |
[
"region:us"
] | null |
2024-04-25T10:01:45+00:00
|
|
text-generation
|
transformers
|
{}
|
pavlopt/llama2-shibing-all
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:03:09+00:00
|
|
null |
transformers
|
# 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]
|
{"library_name": "transformers", "tags": ["trl", "sft"]}
|
Daniel-007/phi-2_qlora_consumer
| null |
[
"transformers",
"safetensors",
"trl",
"sft",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:03:15+00:00
|
null |
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
HenryCai1129/adapter-toxic2nontoxic-100-50-0.0003
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:03:29+00:00
|
text-generation
|
transformers
|
{}
|
berkouille/assistant_merged_84
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:04:01+00:00
|
|
null |
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
PakinClean/git-large-coco-travel
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:04:41+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6727
- Accuracy: 0.6562
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 4 | 0.6505 | 0.75 |
| No log | 2.0 | 8 | 0.6727 | 0.6562 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "bert-base-uncased", "results": []}]}
|
DenysZakharkevych/bert-base-uncased
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:05:19+00:00
|
null | null |
# Multiverseex26Shadowm7exp-7B
Multiverseex26Shadowm7exp-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: allknowingroger/MultiverseEx26-7B-slerp
- model: mahiatlinux/ShadowM7EXP-7B
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/Multiverseex26Shadowm7exp-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"])
```
|
{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]}
|
automerger/Multiverseex26Shadowm7exp-7B
| null |
[
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T10:06:10+00:00
|
null |
transformers
|
{"license": "llama3"}
|
scieditor/Llama-3-8B-Instruct-ct2
| null |
[
"transformers",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:06:42+00:00
|
|
null | null |
{}
|
wakersue2024/test
| null |
[
"region:us"
] | null |
2024-04-25T10:07:09+00:00
|
|
text-classification
|
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
KhimNguyen/ranker_model
| null |
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:07:46+00:00
|
text2text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# events-mem-base
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0031
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0079 | 1.0 | 665 | 0.0052 |
| 0.005 | 2.0 | 1330 | 0.0042 |
| 0.1106 | 3.0 | 1995 | 0.0035 |
| 0.0048 | 4.0 | 2660 | 0.0033 |
| 0.0056 | 5.0 | 3325 | 0.0032 |
| 0.0024 | 6.0 | 3990 | 0.0032 |
| 0.0039 | 7.0 | 4655 | 0.0032 |
| 0.0024 | 8.0 | 5320 | 0.0031 |
| 0.0043 | 9.0 | 5985 | 0.0032 |
| 0.0036 | 10.0 | 6650 | 0.0031 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/flan-t5-base", "model-index": [{"name": "events-mem-base", "results": []}]}
|
eddieman78/events-mem-base
| null |
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:08:12+00:00
|
null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b1-finetuned-cityscapes-1024-1024-full-ds
This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0506
- Mean Iou: 0.9137
- Mean Accuracy: 0.9561
- Overall Accuracy: 0.9831
- Accuracy Default: 1e-06
- Accuracy Pipe: 0.9020
- Accuracy Floor: 0.9742
- Accuracy Background: 0.9920
- Iou Default: 1e-06
- Iou Pipe: 0.7996
- Iou Floor: 0.9590
- Iou Background: 0.9824
## 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: 0.0006
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Default | Accuracy Pipe | Accuracy Floor | Accuracy Background | Iou Default | Iou Pipe | Iou Floor | Iou Background |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------:|:--------------:|:-------------------:|:-----------:|:--------:|:---------:|:--------------:|
| 0.2488 | 1.0 | 39 | 0.1108 | 0.8539 | 0.9260 | 0.9669 | 1e-06 | 0.8345 | 0.9681 | 0.9754 | 1e-06 | 0.6794 | 0.9185 | 0.9639 |
| 0.0768 | 2.0 | 78 | 0.0659 | 0.8845 | 0.9254 | 0.9772 | 1e-06 | 0.8239 | 0.9573 | 0.9951 | 1e-06 | 0.7287 | 0.9506 | 0.9741 |
| 0.0663 | 3.0 | 117 | 0.0588 | 0.8918 | 0.9320 | 0.9793 | 1e-06 | 0.8343 | 0.9687 | 0.9931 | 1e-06 | 0.7439 | 0.9540 | 0.9776 |
| 0.0562 | 4.0 | 156 | 0.0534 | 0.9000 | 0.9592 | 0.9806 | 1e-06 | 0.9237 | 0.9627 | 0.9912 | 1e-06 | 0.7654 | 0.9539 | 0.9808 |
| 0.0509 | 5.0 | 195 | 0.0512 | 0.9063 | 0.9492 | 0.9817 | 1e-06 | 0.8876 | 0.9660 | 0.9940 | 1e-06 | 0.7813 | 0.9569 | 0.9806 |
| 0.0456 | 6.0 | 234 | 0.0498 | 0.9058 | 0.9550 | 0.9819 | 1e-06 | 0.9037 | 0.9692 | 0.9920 | 1e-06 | 0.7783 | 0.9574 | 0.9817 |
| 0.0425 | 7.0 | 273 | 0.0493 | 0.9045 | 0.9515 | 0.9817 | 1e-06 | 0.8918 | 0.9709 | 0.9918 | 1e-06 | 0.7748 | 0.9576 | 0.9810 |
| 0.0402 | 8.0 | 312 | 0.0503 | 0.9074 | 0.9456 | 0.9821 | 1e-06 | 0.8722 | 0.9706 | 0.9939 | 1e-06 | 0.7833 | 0.9581 | 0.9810 |
| 0.0382 | 9.0 | 351 | 0.0501 | 0.9108 | 0.9471 | 0.9825 | 1e-06 | 0.8766 | 0.9702 | 0.9943 | 1e-06 | 0.7930 | 0.9581 | 0.9812 |
| 0.0402 | 10.0 | 390 | 0.0474 | 0.9122 | 0.9520 | 0.9830 | 1e-06 | 0.8907 | 0.9720 | 0.9933 | 1e-06 | 0.7959 | 0.9583 | 0.9824 |
| 0.0367 | 11.0 | 429 | 0.0497 | 0.9089 | 0.9571 | 0.9824 | 1e-06 | 0.9088 | 0.9705 | 0.9919 | 1e-06 | 0.7863 | 0.9585 | 0.9820 |
| 0.0355 | 12.0 | 468 | 0.0445 | 0.9191 | 0.9618 | 0.9843 | 1e-06 | 0.9202 | 0.9719 | 0.9933 | 1e-06 | 0.8132 | 0.9597 | 0.9844 |
| 0.033 | 13.0 | 507 | 0.0494 | 0.9114 | 0.9543 | 0.9828 | 1e-06 | 0.8965 | 0.9746 | 0.9918 | 1e-06 | 0.7943 | 0.9571 | 0.9827 |
| 0.0319 | 14.0 | 546 | 0.0471 | 0.9163 | 0.9542 | 0.9837 | 1e-06 | 0.8953 | 0.9740 | 0.9934 | 1e-06 | 0.8068 | 0.9585 | 0.9835 |
| 0.0304 | 15.0 | 585 | 0.0476 | 0.9167 | 0.9527 | 0.9839 | 1e-06 | 0.8911 | 0.9726 | 0.9944 | 1e-06 | 0.8070 | 0.9598 | 0.9834 |
| 0.0304 | 16.0 | 624 | 0.0492 | 0.9151 | 0.9498 | 0.9835 | 1e-06 | 0.8812 | 0.9744 | 0.9939 | 1e-06 | 0.8036 | 0.9585 | 0.9832 |
| 0.0297 | 17.0 | 663 | 0.0504 | 0.9147 | 0.9549 | 0.9834 | 1e-06 | 0.9003 | 0.9705 | 0.9939 | 1e-06 | 0.8023 | 0.9587 | 0.9830 |
| 0.03 | 18.0 | 702 | 0.0504 | 0.9123 | 0.9584 | 0.9830 | 1e-06 | 0.9103 | 0.9732 | 0.9917 | 1e-06 | 0.7953 | 0.9588 | 0.9828 |
| 0.0294 | 19.0 | 741 | 0.0483 | 0.9162 | 0.9553 | 0.9839 | 1e-06 | 0.8980 | 0.9749 | 0.9931 | 1e-06 | 0.8054 | 0.9596 | 0.9838 |
| 0.0295 | 20.0 | 780 | 0.0506 | 0.9137 | 0.9561 | 0.9831 | 1e-06 | 0.9020 | 0.9742 | 0.9920 | 1e-06 | 0.7996 | 0.9590 | 0.9824 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
{"license": "other", "tags": ["generated_from_trainer"], "base_model": "nvidia/segformer-b1-finetuned-cityscapes-1024-1024", "model-index": [{"name": "segformer-b1-finetuned-cityscapes-1024-1024-full-ds", "results": []}]}
|
selvaa/segformer-b1-finetuned-cityscapes-1024-1024-full-ds
| null |
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"base_model:nvidia/segformer-b1-finetuned-cityscapes-1024-1024",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:09:07+00:00
|
text2text-generation
|
transformers
|
# 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]
|
{"license": "mit", "library_name": "transformers"}
|
kishorea/T5-qa
| null |
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:10:40+00:00
|
null | null |
{"license": "llama3"}
|
dbgzrdzb/dgzgber
| null |
[
"license:llama3",
"region:us"
] | null |
2024-04-25T10:11:16+00:00
|
|
text-generation
|
transformers
|
# Turkish-9b-merged
Turkish-9b-merged is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [TURKCELL/Turkcell-LLM-7b-v1](https://huggingface.co/TURKCELL/Turkcell-LLM-7b-v1)
* [Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0](https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: TURKCELL/Turkcell-LLM-7b-v1
layer_range: [0, 32]
- sources:
- model: Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0
layer_range: [24, 32]
merge_method: passthrough
dtype: bfloat16
```
|
{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "TURKCELL/Turkcell-LLM-7b-v1", "Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0"]}
|
burak/Turkish-9b-merged
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"TURKCELL/Turkcell-LLM-7b-v1",
"Trendyol/Trendyol-LLM-7b-chat-dpo-v1.0",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:11:32+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABCZ_9_992_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:12:42+00:00
|
|
null | null |
{"license": "llama3"}
|
ren1161/ryhrt
| null |
[
"license:llama3",
"region:us"
] | null |
2024-04-25T10:12:47+00:00
|
|
text-generation
|
transformers
|
# jeiku/Soulful_Bepis_9B AWQ
- Model creator: [jeiku](https://huggingface.co/jeiku)
- Original model: [Soulful_Bepis_9B](https://huggingface.co/jeiku/Soulful_Bepis_9B)
## Model SUmmary
Bepis_9B finetuned on Synthetic_Soul_1k. Does it do anything? Who knows...
## How to use
### Install the necessary packages
```bash
pip install --upgrade autoawq autoawq-kernels
```
### Example Python code
```python
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
model_path = "solidrust/Soulful_Bepis_9B-AWQ"
system_message = "You are Soulful_Bepis_9B, incarnated as a powerful AI. You were created by jeiku."
# Load model
model = AutoAWQForCausalLM.from_quantized(model_path,
fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True)
streamer = TextStreamer(tokenizer,
skip_prompt=True,
skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = """\
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant"""
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(prompt_template.format(system_message=system_message,prompt=prompt),
return_tensors='pt').input_ids.cuda()
# Generate output
generation_output = model.generate(tokens,
streamer=streamer,
max_new_tokens=512)
```
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
|
{"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible", "mergekit", "merge"], "datasets": ["ChaoticNeutrals/Synthetic_Soul_1k"], "base_model": ["ChaoticNeutrals/Bepis_9B", "jeiku/Synthetic_Soul_1k_Mistral_128"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}
|
solidrust/Soulful_Bepis_9B-AWQ
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"mergekit",
"merge",
"en",
"dataset:ChaoticNeutrals/Synthetic_Soul_1k",
"base_model:ChaoticNeutrals/Bepis_9B",
"base_model:jeiku/Synthetic_Soul_1k_Mistral_128",
"license:other",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:13:28+00:00
|
null | null |
{"license": "apache-2.0"}
|
AmenBC/amencodellama.gguf
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T10:13:40+00:00
|
|
text-to-image
|
diffusers
|
# SDXL LoRA DreamBooth - computational-mama/bike-doodles
<Gallery />
## Model description
### These are computational-mama/bike-doodles LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`bike-doodles.safetensors` here 💾](/computational-mama/bike-doodles/blob/main/bike-doodles.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:bike-doodles:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`bike-doodles_emb.safetensors` here 💾](/computational-mama/bike-doodles/blob/main/bike-doodles_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `bike-doodles_emb` to your prompt. For example, `A photo of bike-doodles_emb`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('computational-mama/bike-doodles', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='computational-mama/bike-doodles', filename='bike-doodles_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A photo of <s0><s1>').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/computational-mama/bike-doodles/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A drawing of <s0><s1>, a drawing of a bike, road bike, green color, racing handle, vintage bike, fenders", "output": {"url": "image-0.png"}}, {"text": "A drawing of <s0><s1>, a drawing of a bike, city bike with fenders, green color", "output": {"url": "image-1.png"}}, {"text": "A drawing of <s0><s1>, a drawing of a bike, city bike with a light, pink color", "output": {"url": "image-2.png"}}, {"text": "A drawing of <s0><s1>, a drawing of a bike, city bike with lights, pink color", "output": {"url": "image-3.png"}}, {"text": "A drawing of <s0><s1>, a drawing of a bike, foldable bike, black color, small wheels", "output": {"url": "image-4.png"}}, {"text": "A drawing of <s0><s1>, a drawing of a bike, city bike with carrier, black color, Next Bike, bike sharing, fenders", "output": {"url": "image-5.png"}}, {"text": "A drawing of <s0><s1>, a drawing of a bike, city bike with carrier, blue color, bike basket", "output": {"url": "image-6.png"}}, {"text": "A drawing of <s0><s1>, a drawing of a bike, city bike with carrier, blue color, bike basket", "output": {"url": "image-7.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A photo of <s0><s1>"}
|
computational-mama/bike-doodles
| null |
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null |
2024-04-25T10:14:20+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** ack2050
- **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)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}
|
ack2050/llama3-8b-oig-unsloth
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:14:20+00:00
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
{"library_name": "peft", "base_model": "smallcloudai/Refact-1_6B-fim"}
|
Bry14/Refact-1_6B-fim-haskell-v0.1
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:smallcloudai/Refact-1_6B-fim",
"region:us"
] | null |
2024-04-25T10:14:30+00:00
|
automatic-speech-recognition
|
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
suke0327/whisper-large_rear_en
| null |
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:15:20+00:00
|
reinforcement-learning
|
stable-baselines3
|
# **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
...
```
|
{"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": "212.15 +/- 97.27", "name": "mean_reward", "verified": false}]}]}]}
|
Artemijs/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T10:15:21+00:00
|
null | null |
{}
|
MattNandavong/QA_model
| null |
[
"region:us"
] | null |
2024-04-25T10:16:27+00:00
|
|
image-feature-extraction
|
transformers
|
# 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]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
mali17361/detr-finetuned-table-v4
| null |
[
"transformers",
"safetensors",
"detr",
"image-feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:16:32+00:00
|
text-generation
|
transformers
|
<a href="https://github.com/MLP-Lab/Bllossom">
<img src="https://github.com/teddysum/bllossom/blob/main//bllossom_icon.png?raw=true" width="40%" height="50%">
</a>
# Bllossom | [Demo]() | [Homepage](https://www.bllossom.ai/) | [Github](https://github.com/MLP-Lab/Bllossom) | [Colab-tutorial](https://colab.research.google.com/drive/1fBOzUVZ6NRKk_ugeoTbAOokWKqSN47IG?usp=sharing) |
The Bllossom language model is a Korean-English bilingual language model based on the open-source LLama3. It enhances the connection of knowledge between Korean and English. It has the following features:
* **Knowledge Linking**: Linking Korean and English knowledge through additional training
* **Vocabulary Expansion**: Expansion of Korean vocabulary to enhance Korean expressiveness.
* **Instruction Tuning**: Tuning using custom-made instruction following data specialized for Korean language and Korean culture
* **Human Feedback**: DPO has been applied
* **Vision-Language Alignment**: Aligning the vision transformer with this language model
**This model developed by [MLPLab at Seoultech](http://mlp.seoultech.ac.kr), [Teddysum](http://teddysum.ai/) and [Yonsei Univ](https://sites.google.com/view/hansaemkim/hansaem-kim)**
## Demo Video
<div style="display: flex; justify-content: space-between;">
<!-- 첫 번째 컬럼 -->
<div style="width: 49%;">
<a>
<img src="https://github.com/lhsstn/lhsstn/blob/main/x-llava_dem.gif?raw=true" style="width: 100%; height: auto;">
</a>
<p style="text-align: center;">Bllossom-V Demo</p>
</div>
<!-- 두 번째 컬럼 (필요하다면) -->
<div style="width: 49%;">
<a>
<img src="https://github.com/lhsstn/lhsstn/blob/main/bllossom_demo_kakao.gif?raw=true" style="width: 70%; height: auto;">
</a>
<p style="text-align: center;">Bllossom Demo(Kakao)ㅤㅤㅤㅤㅤㅤㅤㅤ</p>
</div>
</div>
## NEWS
* [2024/04] We released Bllossom v2.0, based on llama-3
* [2023/12] We released Bllossom-Vision v1.0, based on Bllossom
* [2023/08] We released Bllossom v1.0, based on llama-2.
* [2023/07] We released Bllossom v0.7, based on polyglot-ko.
```bash
저희 서울과기대 MLP연구실에서 한국어-영어 이중 언어모델인 Bllossom을 공개했습니다!
- LLama3-8B 기반의 경량화된 사이즈
- 한국어-영어 지식연결을 통한 한국어 지식 강화
- 한국어 어휘추가
- 한국어 문화, 언어를 고려한 자체제작 데이터 기반 미세조정
- 강화학습 (DPO)
- 시각-언어 모델확장
1. Bllossom은 서울과기대, 테디썸, 연세대 언어자원 연구실의 언어학자와 협업해 만든 실용주의기반 언어모델입니다! 앞으로 지속적인 업데이트를 통해 관리하겠습니다 많이 활용해주세요 🙂
2. Bllossom70B모델, 어휘확장모델, 시각-언어모델은 추후 공개할 예정입니다. (궁금하신분은 개별 연락주세요, GPU만 지원해주시면 무료로 드립니다!)
3. Bllossom은 NAACL2024, LREC-COLING2024 (구두) 발표로 채택되었습니다.
4. 좋은 언어모델 계속 업데이트 하겠습니다!! 한국어 강화를위해 공동 연구하실분 언제든 환영합니다!!
```
## Example code
### Colab Tutorial
- [Inference-Code-Link](https://colab.research.google.com/drive/1fBOzUVZ6NRKk_ugeoTbAOokWKqSN47IG?usp=sharing)
### Install Dependencies
```bash
pip install torch transformers==4.40.0 accelerate
```
### Python code with Pipeline
```python
import transformers
import torch
model_id = "MLP-KTLim/llama3-Bllossom"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
pipeline.model.eval()
PROMPT = '''당신은 유용한 AI 어시스턴트입니다. 사용자의 질의에 대해 친절하고 정확하게 답변해야 합니다.'''
instruction = "서울과학기술대학교 MLP연구실에 대해 소개해줘"
messages = [
{"role": "system", "content": f"{PROMPT}"},
{"role": "user", "content": f"{instruction}"}
]
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("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=2048,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
repetition_penalty = 1.1
)
print(outputs[0]["generated_text"][len(prompt):])
# 서울과학기술대학교 MLP연구실은 멀티모달 자연어처리 연구를 하고 있습니다. 구성원은 임경태 교수와 김민준, 김상민, 최창수, 원인호, 유한결, 임현석, 송승우, 육정훈, 신동재 학생이 있습니다.
```
### Python code with AutoModel
```python
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = 'MLP-KTLim/llama3-Bllossom'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
model.eval()
PROMPT = '''당신은 유용한 AI 어시스턴트입니다. 사용자의 질의에 대해 친절하고 정확하게 답변해야 합니다.'''
instruction = "서울과학기술대학교 MLP연구실에 대해 소개해줘"
messages = [
{"role": "system", "content": f"{PROMPT}"},
{"role": "user", "content": f"{instruction}"}
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=2048,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
repetition_penalty = 1.1
)
print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))
# 서울과학기술대학교 MLP연구실은 멀티모달 자연어처리 연구를 하고 있습니다. 구성원은 임경태 교수와 김민준, 김상민, 최창수, 원인호, 유한결, 임현석, 송승우, 육정훈, 신동재 학생이 있습니다.
```
## Citation
**Language Model**
```text
@misc{bllossom,
author = {ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim},
title = {Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean},
year = {2024},
journal = {LREC-COLING 2024},
paperLink = {\url{https://arxiv.org/pdf/2403.10882}},
},
}
```
**Vision-Language Model**
```text
@misc{bllossom-V,
author = {Dongjae Shin, Hyunseok Lim, Inho Won, Changsu Choi, Minjun Kim, Seungwoo Song, Hangyeol Yoo, Sangmin Kim, Kyungtae Lim},
title = {X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment},
year = {2024},
publisher = {GitHub},
journal = {NAACL 2024 findings},
paperLink = {\url{https://arxiv.org/pdf/2403.11399}},
},
}
```
## Contact
- 임경태(KyungTae Lim), Professor at Seoultech. `[email protected]`
- 함영균(Younggyun Hahm), CEO of Teddysum. `[email protected]`
- 김한샘(Hansaem Kim), Professor at Yonsei. `[email protected]`
## Contributor
- 최창수(Chansu Choi), [email protected]
- 김상민(Sangmin Kim), [email protected]
- 원인호(Inho Won), [email protected]
- 김민준(Minjun Kim), [email protected]
- 송승우(Seungwoo Song), [email protected]
- 신동재(Dongjae Shin), [email protected]
- 임현석(Hyeonseok Lim), [email protected]
- 육정훈(Jeonghun Yuk), [email protected]
- 유한결(Hangyeol Yoo), [email protected]
- 송서현(Seohyun Song), [email protected]
|
{"language": ["en", "ko"], "license": "llama3", "library_name": "transformers", "base_model": ["meta-llama/Meta-Llama-3-8B"]}
|
MLP-KTLim/llama3-Bllossom
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ko",
"arxiv:2403.10882",
"arxiv:2403.11399",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:16:43+00:00
|
null | null |
{}
|
vanisus/abiturientSSTU
| null |
[
"region:us"
] | null |
2024-04-25T10:17:36+00:00
|
|
null | null |
{}
|
FrankTCH/nllb-finetuned-ted-en-to-ar
| null |
[
"region:us"
] | null |
2024-04-25T10:17:55+00:00
|
|
fill-mask
|
transformers
|
{}
|
pyrac/greencast-mlm
| null |
[
"transformers",
"safetensors",
"xlm-roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:18:13+00:00
|
|
null |
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
Rohit1412/experiments
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:18:15+00:00
|
text-to-image
|
diffusers
|
<Gallery />
## BitDiffusionV0.1
This is the initial version of the image model trained on the Bittensor network within subnet 17. It's not expected for this model to perform as well as MidJourney V6 at the moment. However, it does generate better images than base SDXL model.
**Trained on the dataset of Subnet 19 Vision.**
## Subnet 17 Checkpoint
Model ID : gtsru/sn17-dek-012
Revision : 5852d39e8413a377a3477b8278ade9af311f83a4
UID : 42
Perplexity : 1.1325
## Settings for BitDiffusionV0.1
Use these settings for the best results with BitDiffusionV0.1:
CFG Scale: Use a CFG scale of 8
Steps: 40 to 60 steps
Sampler: DPM++ 2M SDE
Scheduler: Karras
Resolution: 1024x1024
**For best results, set a negative_prompt**
## Use it with 🧨 diffusers
```python
import torch
from diffusers import (
StableDiffusionXLPipeline,
KDPM2AncestralDiscreteScheduler,
AutoencoderKL
)
# Load VAE component
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix",
torch_dtype=torch.float16
)
# Configure the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
"PlixAI/BitDiffusionV0.1",
vae=vae,
torch_dtype=torch.float16
)
pipe.scheduler = KDPM2AncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to('cuda')
# Define prompts and generate image
prompt = "black fluffy gorgeous dangerous cat animal creature, large orange eyes, big fluffy ears, piercing gaze, full moon, dark ambiance, best quality, extremely detailed"
negative_prompt = "nsfw, bad quality, bad anatomy, worst quality, low quality, low resolutions, extra fingers, blur, blurry, ugly, wrongs proportions, watermark, image artifacts, lowres, ugly, jpeg artifacts, deformed, noisy image"
image = pipe(
prompt,
negative_prompt=negative_prompt,
width=1024,
height=1024,
guidance_scale=7.5,
num_inference_steps=50
).images[0]
```
Training Subnet : https://github.com/PlixML/pixel
Data Subnet : https://github.com/namoray/vision
|
{"license": "gpl-3.0", "pipeline_tag": "text-to-image", "widget": [{"text": "Three cow grazing in a bay window", "output": {"url": "cow.png"}}, {"text": "Super Closeup Portrait, action shot, Profoundly dark whitish meadow, glass flowers, Stains, space grunge style, Jeanne d'Arc wearing White Olive green used styled Cotton frock, Wielding thin silver sword, Sci-fi vibe, dirty, noisy, Vintage monk style, very detailed, hd", "output": {"url": "girl.png"}}, {"text": "spacious,circular underground room,{dirtied and bloodied white tiles},amalgamation,flesh,plastic,dark fabric,core,pulsating heart,limbs,human-like arms,twisted angelic wings,arms,covered in skin,feathers,scales,undulate slowly,unseen current,convulsing,head area,chaotic,mass of eyes,mouths,no human features,smaller forms,cherubs,demons,golden wires,surround,holy light,tv static effect,golden glow,shadows,terrifying essence,overwhelming presence,nightmarish,landscape,sparse,cavernous,eerie,dynamic,motion,striking,awe-inspiring,nightmarish,nightmarish,nightmare,horrifying,bio-mechanical,body horror,amalgamation", "output": {"url": "aigle.png"}}]}
|
PlixAI/BitDiffusionV0.1
| null |
[
"diffusers",
"safetensors",
"text-to-image",
"license:gpl-3.0",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null |
2024-04-25T10:18:55+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 2504v4
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6121
- Accuracy: 0.8193
- Precision: 0.8296
- Recall: 0.8193
- F1: 0.8179
- Ratio: 0.5882
## 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: 8
- seed: 42
- 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
- num_epochs: 4
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 2.127 | 0.9870 | 38 | 0.8502 | 0.6345 | 0.6397 | 0.6345 | 0.6310 | 0.5966 |
| 0.7538 | 2.0 | 77 | 0.6640 | 0.7689 | 0.7885 | 0.7689 | 0.7649 | 0.6303 |
| 0.6205 | 2.9870 | 115 | 0.6121 | 0.8193 | 0.8296 | 0.8193 | 0.8179 | 0.5882 |
| 0.5664 | 3.9481 | 152 | 0.6239 | 0.8109 | 0.8278 | 0.8109 | 0.8085 | 0.6134 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "projecte-aina/roberta-base-ca-v2-cased-te", "model-index": [{"name": "2504v4", "results": []}]}
|
adriansanz/2504v4
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:projecte-aina/roberta-base-ca-v2-cased-te",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:21:14+00:00
|
null | null |
The d-w2gm models are introduced in our paper "Dynamic Gaussian Word Embeddings". Model files will be uploaded after our paper has got accepted from the journal.
|
{"license": "bsd-3-clause"}
|
KocLab-Bilkent/d-w2gm
| null |
[
"license:bsd-3-clause",
"region:us"
] | null |
2024-04-25T10:21:39+00:00
|
text-generation
|
transformers
|
{}
|
Akshay95/Llama-2-7b-chat-finetune
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:23:50+00:00
|
|
text-to-speech
|
adapter-transformers
|
{"language": ["en"], "license": "apache-2.0", "library_name": "adapter-transformers", "datasets": ["HuggingFaceFW/fineweb"], "metrics": ["accuracy"], "pipeline_tag": "text-to-speech"}
|
xgh127/test
| null |
[
"adapter-transformers",
"text-to-speech",
"en",
"dataset:HuggingFaceFW/fineweb",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T10:24:14+00:00
|
|
null |
diffusers
|
# Text2Face-LoRa
This is a LoRa-finetuned version of the Stable Diffusion 2.1 model specifically optimized
for generating face images. The model was trained with [FFHQ](https://github.com/NVlabs/ffhq-dataset) and [easyportrait](https://github.com/hukenovs/easyportrait)
using synthetic text captions for both datasets.
Details on the dataset format and preparation will be available soon.
## Checkpoints
You can download the pretrained LoRa weights for the diffusion model and text encoder using
```python
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="michaeltrs/text2face",
filename="checkpoints/lora30k/pytorch_lora_weights.safetensors",
local_dir="checkpoints")
```
## Inference
Generate images using the `generate.py` script, which loads the SD2.1 foundation model from Hugging Face and applies the LoRa weights.
Generation is driven by defining a prompt and optionally a negative prompt.
```python
from diffusers import StableDiffusionPipeline
import torch
class Model:
def __init__(self, checkpoint="checkpoints/lora30k", weight_name="pytorch_lora_weights.safetensors", device="cuda"):
self.checkpoint = checkpoint
state_dict, network_alphas = StableDiffusionPipeline.lora_state_dict(
# Path to my trained lora output_dir
checkpoint,
weight_name=weight_name
)
self.pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16).to(device)
self.pipe.load_lora_into_unet(state_dict, network_alphas, self.pipe.unet, adapter_name='test_lora')
self.pipe.load_lora_into_text_encoder(state_dict, network_alphas, self.pipe.text_encoder, adapter_name='test_lora')
self.pipe.set_adapters(["test_lora"], adapter_weights=[1.0])
def generate(self, prompt, negprompt='', steps=50, savedir=None, seed=1):
lora_scale = 1.0
image = self.pipe(prompt,
negative_prompt=negprompt,
num_inference_steps=steps,
cross_attention_kwargs={"scale": lora_scale},
generator=torch.manual_seed(seed)).images[0]
if savedir is None:
image.save(f"{self.checkpoint}/{'_'.join(prompt.replace('.', ' ').split(' '))}.png")
else:
image.save(f"{savedir}/{'_'.join(prompt.replace('.', ' ').split(' '))}.png")
return image
if __name__ == "__main__":
model = Model()
prompt = 'A happy 55 year old male with blond hair and a goatee smiles with visible teeth.'
negprompt = ''
image = model.generate(prompt, negprompt=negprompt, steps=50, seed=42)
```
## Limitations
This model, Text2Face-LoRa, is finetuned from Stable Diffusion 2.1 and as such, inherits all the limitations and biases
associated with the base model. These biases may manifest in skewed representations across different ethnicities and
genders due to the nature of the training data originally used for Stable Diffusion 2.1.
### Specific Limitations Include:
- **Ethnic and Gender Biases**: The model may generate images that do not equally represent the diversity of human
features in different ethnic and gender groups, potentially reinforcing or exacerbating existing stereotypes.
- **Selection Bias in Finetuning Datasets**: The datasets used for finetuning this model were selected with specific
criteria in mind, which may not encompass a wide enough variety of data points to correct for the inherited biases of the base model.
- **Caption Generation Bias**: The synthetic annotations used to finetune this model were generated by automated
face analysis models, which themselves may be biased. This could lead to inaccuracies in facial feature interpretation
and representation, particularly for less-represented demographics in the training data.
### Ethical Considerations:
Users are encouraged to consider these limitations when deploying the model in real-world applications, especially
those involving diverse human subjects. It is advisable to perform additional validations and seek ways to mitigate
these biases in practical use cases.
|
{"language": ["en"], "license": "mit", "library_name": "diffusers", "tags": ["lora", "image-generation", "diffusion", "face-generation", "text-conditioned-human-portrait", "synthetic-captions", "diffusers"]}
|
michaeltrs/text2face
| null |
[
"diffusers",
"lora",
"image-generation",
"diffusion",
"face-generation",
"text-conditioned-human-portrait",
"synthetic-captions",
"en",
"license:mit",
"region:us"
] | null |
2024-04-25T10:24:33+00:00
|
text-classification
|
transformers
|
{}
|
KalaiselvanD/albert_model__25_3
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:24:37+00:00
|
|
null | null |
{}
|
Firedoge/embedding
| null |
[
"region:us"
] | null |
2024-04-25T10:24:41+00:00
|
|
null | null |
{}
|
MattNandavong/QA_model2
| null |
[
"region:us"
] | null |
2024-04-25T10:24:54+00:00
|
|
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# QA_model3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) 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: 3
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "QA_model3", "results": []}]}
|
MattNandavong/QA_model3
| null |
[
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:26:44+00:00
|
null |
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
LumousInTheWild/image_captioning_tokenizer
| null |
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:28:29+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetune-open-question
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7613
- Balanced weighted accuracy: 0.7674
- Mcc: 0.7837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Balanced weighted accuracy | Mcc |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------:|:------:|
| 0.7591 | 1.0 | 304 | 0.8721 | 0.6565 | 0.6915 |
| 0.802 | 2.0 | 608 | 0.8703 | 0.7387 | 0.7258 |
| 0.3863 | 3.0 | 912 | 0.7613 | 0.7674 | 0.7837 |
| 0.3088 | 4.0 | 1216 | 0.8113 | 0.7972 | 0.7879 |
| 0.4292 | 5.0 | 1520 | 0.9155 | 0.7923 | 0.7961 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "roberta-base", "model-index": [{"name": "roberta-finetune-open-question", "results": []}]}
|
nolnolon/roberta-finetune-open-question
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:29:14+00:00
|
null | null |
{}
|
0415Hatakeyama/aki
| null |
[
"region:us"
] | null |
2024-04-25T10:29:28+00:00
|
|
null | null |
{"license": "mit"}
|
bit-zyy/Vosh-synthetic
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-25T10:30:29+00:00
|
|
text-generation
|
transformers
|
{}
|
itay-nakash/model_c1082aeb9b
| null |
[
"transformers",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:30:48+00:00
|
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABCZ_2_111_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:30:48+00:00
|
|
text-generation
|
transformers
|
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo mattshumer/Llama-3-8B-16K installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/mattshumer-Llama-3-8B-16K-HQQ-2bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/mattshumer-Llama-3-8B-16K-HQQ-2bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("mattshumer/Llama-3-8B-16K")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model mattshumer/Llama-3-8B-16K before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
|
{"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}
|
PrunaAI/mattshumer-Llama-3-8B-16K-HQQ-2bit-smashed
| null |
[
"transformers",
"llama",
"text-generation",
"pruna-ai",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:31:22+00:00
|
token-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-invoice
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9513
- Precision: 0.125
- Recall: 0.0122
- F1: 0.0222
- Accuracy: 0.8763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.1 | 5 | 1.9513 | 0.125 | 0.0122 | 0.0222 | 0.8763 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1
|
{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["generated"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/layoutlmv3-base", "model-index": [{"name": "layoutlmv3-finetuned-invoice", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "generated", "type": "generated", "config": "sroie", "split": "test", "args": "sroie"}, "metrics": [{"type": "precision", "value": 0.125, "name": "Precision"}, {"type": "recall", "value": 0.012170385395537525, "name": "Recall"}, {"type": "f1", "value": 0.022181146025878003, "name": "F1"}, {"type": "accuracy", "value": 0.8763429534442806, "name": "Accuracy"}]}]}]}
|
abhaysanu/layoutlmv3-finetuned-invoice
| null |
[
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:generated",
"base_model:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:31:40+00:00
|
image-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold2
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1859
- Accuracy: 0.67
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0892 | 1.0 | 923 | 1.2197 | 0.5941 |
| 0.9178 | 2.0 | 1846 | 1.0433 | 0.6486 |
| 0.7099 | 3.0 | 2769 | 0.9578 | 0.6786 |
| 0.7148 | 4.0 | 3692 | 0.9821 | 0.6770 |
| 0.7006 | 5.0 | 4615 | 1.0065 | 0.6632 |
| 0.4578 | 6.0 | 5538 | 1.0536 | 0.6673 |
| 0.421 | 7.0 | 6461 | 1.1039 | 0.6681 |
| 0.2925 | 8.0 | 7384 | 1.1421 | 0.6654 |
| 0.2854 | 9.0 | 8307 | 1.1816 | 0.6654 |
| 0.1695 | 10.0 | 9230 | 1.1859 | 0.67 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-base-patch4-window7-224-in22k", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.67, "name": "Accuracy"}]}]}]}
|
onizukal/Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold2
| null |
[
"transformers",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224-in22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:31:40+00:00
|
null |
peft
|
<!-- 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. -->
# LORA_gemma_trained_on_MMLU_5shot_test
This model is a fine-tuned version of [justshao/gemma-7b-with-confidence](https://huggingface.co/justshao/gemma-7b-with-confidence) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2779
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3002 | 1.0 | 877 | 0.2812 |
| 0.2731 | 2.0 | 1755 | 0.2820 |
| 0.2539 | 3.0 | 2632 | 0.2787 |
| 0.2419 | 4.0 | 3510 | 0.2775 |
| 0.2351 | 5.0 | 4385 | 0.2779 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "gemma", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "justshao/gemma-7b-with-confidence", "model-index": [{"name": "LORA_gemma_trained_on_MMLU_5shot_test", "results": []}]}
|
justshao/LORA_gemma_trained_on_MMLU_5shot_test
| null |
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:justshao/gemma-7b-with-confidence",
"license:gemma",
"region:us"
] | null |
2024-04-25T10:31:43+00:00
|
text2text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CS505_COQE_viT5_train_Instruction0_SOAPL_v1_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SOAPL_v1_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL_v1_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:31:54+00:00
|
null | null |
{"license": "openrail"}
|
MusicBox27/CRACKEN
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-25T10:31:56+00:00
|
|
text2text-generation
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# CS505_COQE_viT5_train_Instruction0_SOAPL_v2_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SOAPL_v2_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_SOAPL_v2_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:32:15+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 2504v4-8ep
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5711
- Accuracy: 0.8487
- Precision: 0.8523
- Recall: 0.8487
- F1: 0.8484
- Ratio: 0.5504
## 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: 8
- seed: 42
- 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
- num_epochs: 4
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 2.0709 | 0.9870 | 38 | 0.8049 | 0.7059 | 0.7073 | 0.7059 | 0.7054 | 0.4580 |
| 0.7325 | 2.0 | 77 | 0.6190 | 0.8067 | 0.8081 | 0.8067 | 0.8065 | 0.5336 |
| 0.6249 | 2.9870 | 115 | 0.5998 | 0.8109 | 0.8230 | 0.8109 | 0.8091 | 0.5966 |
| 0.5768 | 3.9481 | 152 | 0.5711 | 0.8487 | 0.8523 | 0.8487 | 0.8484 | 0.5504 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "projecte-aina/roberta-base-ca-v2-cased-te", "model-index": [{"name": "2504v4-8ep", "results": []}]}
|
adriansanz/2504separado1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:projecte-aina/roberta-base-ca-v2-cased-te",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:32:22+00:00
|
text2text-generation
|
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
kishorea/qa2
| null |
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:32:26+00:00
|
null |
peft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0
base_model_config: T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: true
hub_model_id: T3Q-LLM-sft1.0-dpo1.0_4300QA
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
# - path: admin_data.csv
- path: superiort/multiplechoice-4300
type: alpaca
# The below are defaults. only set what's needed if you use a different column name.
# system_prompt: ""
# system_format: "{system}"
# field_system: system
# field_instruction: instruction
# field_input: input
# field_output: output
# format: |-
# Human: {instruction} {input}
# Assistant:
# no_input_format: "{instruction} "
# dataset_prepared_path: yanolja_preprocessed_data
dataset_prepared_path: last_run_prepared
val_set_size: 0.2
output_dir: ./T3Q-LLM-sft1.0-dpo1.0_4300QA
adapter: qlora
lora_model_dir:
# device_map: [0,1,3]
sequence_len: 4096
sample_packing: false
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: axolotl_T3Q_4300
wandb_entity:
wandb_watch:
wandb_run_id: T3Q_mod_4300
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 10
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_steps: 0.01
save_strategy: epoch
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "<|im_end|>"
unk_token: "<unk>"
pad_token: "</s>" # EOS와 PAD가 동일
```
</details><br>
# T3Q-LLM-sft1.0-dpo1.0_4300QA
This model is a fine-tuned version of [T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0](https://huggingface.co/T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2288
## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2424 | 0.0093 | 1 | 1.0432 |
| 1.0333 | 0.1023 | 11 | 0.9004 |
| 0.8715 | 0.2047 | 22 | 0.7157 |
| 0.7053 | 0.3070 | 33 | 0.6548 |
| 0.6688 | 0.4093 | 44 | 0.6449 |
| 0.6823 | 0.5116 | 55 | 0.6282 |
| 0.5876 | 0.6140 | 66 | 0.6251 |
| 0.6994 | 0.7163 | 77 | 0.6290 |
| 0.6662 | 0.8186 | 88 | 0.6311 |
| 0.6239 | 0.9209 | 99 | 0.6338 |
| 0.5959 | 1.0233 | 110 | 0.6319 |
| 0.6408 | 1.1256 | 121 | 0.6668 |
| 0.595 | 1.2279 | 132 | 0.6221 |
| 0.5476 | 1.3302 | 143 | 0.6295 |
| 0.587 | 1.4326 | 154 | 0.6569 |
| 0.5867 | 1.5349 | 165 | 0.6208 |
| 0.5895 | 1.6372 | 176 | 0.6264 |
| 0.6581 | 1.7395 | 187 | 0.6208 |
| 0.5872 | 1.8419 | 198 | 0.6290 |
| 0.6314 | 1.9442 | 209 | 0.6243 |
| 0.4397 | 2.0465 | 220 | 0.6591 |
| 0.4568 | 2.1488 | 231 | 0.7095 |
| 0.422 | 2.2512 | 242 | 0.6914 |
| 0.453 | 2.3535 | 253 | 0.7001 |
| 0.4678 | 2.4558 | 264 | 0.6896 |
| 0.4335 | 2.5581 | 275 | 0.6776 |
| 0.4796 | 2.6605 | 286 | 0.6829 |
| 0.4637 | 2.7628 | 297 | 0.6742 |
| 0.4532 | 2.8651 | 308 | 0.6828 |
| 0.4348 | 2.9674 | 319 | 0.6836 |
| 0.2787 | 3.0698 | 330 | 0.8085 |
| 0.2336 | 3.1721 | 341 | 0.8380 |
| 0.2341 | 3.2744 | 352 | 0.7998 |
| 0.2393 | 3.3767 | 363 | 0.8041 |
| 0.2826 | 3.4791 | 374 | 0.8040 |
| 0.2505 | 3.5814 | 385 | 0.8099 |
| 0.3057 | 3.6837 | 396 | 0.8103 |
| 0.2789 | 3.7860 | 407 | 0.7964 |
| 0.269 | 3.8884 | 418 | 0.7891 |
| 0.2493 | 3.9907 | 429 | 0.7958 |
| 0.1193 | 4.0930 | 440 | 0.9242 |
| 0.1143 | 4.1953 | 451 | 0.9331 |
| 0.1147 | 4.2977 | 462 | 0.9112 |
| 0.1351 | 4.4 | 473 | 0.9290 |
| 0.0982 | 4.5023 | 484 | 0.9358 |
| 0.1011 | 4.6047 | 495 | 0.9279 |
| 0.09 | 4.7070 | 506 | 0.9289 |
| 0.1063 | 4.8093 | 517 | 0.9392 |
| 0.1038 | 4.9116 | 528 | 0.9267 |
| 0.0361 | 5.0140 | 539 | 0.9412 |
| 0.0371 | 5.1163 | 550 | 1.0589 |
| 0.033 | 5.2186 | 561 | 1.0253 |
| 0.0426 | 5.3209 | 572 | 1.0482 |
| 0.0357 | 5.4233 | 583 | 1.0388 |
| 0.0355 | 5.5256 | 594 | 1.0566 |
| 0.0373 | 5.6279 | 605 | 1.0470 |
| 0.0395 | 5.7302 | 616 | 1.0581 |
| 0.0366 | 5.8326 | 627 | 1.0696 |
| 0.0387 | 5.9349 | 638 | 1.0641 |
| 0.0127 | 6.0372 | 649 | 1.0692 |
| 0.0114 | 6.1395 | 660 | 1.1612 |
| 0.0105 | 6.2419 | 671 | 1.1575 |
| 0.0121 | 6.3442 | 682 | 1.1479 |
| 0.0082 | 6.4465 | 693 | 1.1591 |
| 0.011 | 6.5488 | 704 | 1.1669 |
| 0.0112 | 6.6512 | 715 | 1.1645 |
| 0.0109 | 6.7535 | 726 | 1.1628 |
| 0.0102 | 6.8558 | 737 | 1.1705 |
| 0.0098 | 6.9581 | 748 | 1.1769 |
| 0.006 | 7.0605 | 759 | 1.1840 |
| 0.0064 | 7.1628 | 770 | 1.2016 |
| 0.0063 | 7.2651 | 781 | 1.2133 |
| 0.0058 | 7.3674 | 792 | 1.2182 |
| 0.0056 | 7.4698 | 803 | 1.2218 |
| 0.0057 | 7.5721 | 814 | 1.2234 |
| 0.0059 | 7.6744 | 825 | 1.2245 |
| 0.0057 | 7.7767 | 836 | 1.2247 |
| 0.0048 | 7.8791 | 847 | 1.2247 |
| 0.0054 | 7.9814 | 858 | 1.2246 |
| 0.0051 | 8.0837 | 869 | 1.2252 |
| 0.0059 | 8.1860 | 880 | 1.2261 |
| 0.0053 | 8.2884 | 891 | 1.2272 |
| 0.0057 | 8.3907 | 902 | 1.2275 |
| 0.0056 | 8.4930 | 913 | 1.2280 |
| 0.0052 | 8.5953 | 924 | 1.2283 |
| 0.007 | 8.6977 | 935 | 1.2287 |
| 0.0052 | 8.8 | 946 | 1.2285 |
| 0.005 | 8.9023 | 957 | 1.2289 |
| 0.0056 | 9.0047 | 968 | 1.2288 |
| 0.005 | 9.1070 | 979 | 1.2289 |
| 0.0054 | 9.2093 | 990 | 1.2290 |
| 0.0053 | 9.3116 | 1001 | 1.2288 |
| 0.0049 | 9.4140 | 1012 | 1.2290 |
| 0.0052 | 9.5163 | 1023 | 1.2290 |
| 0.0058 | 9.6186 | 1034 | 1.2291 |
| 0.0059 | 9.7209 | 1045 | 1.2289 |
| 0.0055 | 9.8233 | 1056 | 1.2289 |
| 0.0054 | 9.9256 | 1067 | 1.2288 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["axolotl", "generated_from_trainer"], "base_model": "T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0", "model-index": [{"name": "T3Q-LLM-sft1.0-dpo1.0_4300QA", "results": []}]}
|
superiort/T3Q-LLM-sft1.0-dpo1.0_4300QA_10epochs
| null |
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:T3Q-LLM/T3Q-LLM-sft1.0-dpo1.0",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T10:32:35+00:00
|
text-generation
|
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
tomaszki/llama-10
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:34:13+00:00
|
null | null |
{"license": "openrail"}
|
Artzy7/Gru
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-25T10:34:16+00:00
|
|
text-generation
|
transformers
|
{}
|
itay-nakash/model_4ea60dae10
| null |
[
"transformers",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:35:08+00:00
|
|
null | null |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
{}
|
tsang326/chatbot_dalat
| null |
[
"arxiv:1910.09700",
"region:us"
] | null |
2024-04-25T10:35:45+00:00
|
null |
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Donut_Sroie_data
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) 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: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "Donut_Sroie_data", "results": []}]}
|
shubhambhange4471/Donut_Sroie_data
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:36:28+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABmbert_9_992_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:37:15+00:00
|
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/david_hajdu/huggingface/runs/0bqlwuvd)
# fine-tuned-rvl-cdip
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7731
- F1: 0.8177
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 96 | 2.0377 | 0.4479 |
| No log | 2.0 | 192 | 1.3075 | 0.6641 |
| No log | 3.0 | 288 | 0.9850 | 0.7240 |
| No log | 4.0 | 384 | 0.8775 | 0.7630 |
| No log | 5.0 | 480 | 0.7824 | 0.7865 |
| 1.2987 | 6.0 | 576 | 0.7516 | 0.8021 |
| 1.2987 | 7.0 | 672 | 0.7688 | 0.7865 |
| 1.2987 | 8.0 | 768 | 0.7462 | 0.8125 |
| 1.2987 | 9.0 | 864 | 0.7731 | 0.8177 |
| 1.2987 | 10.0 | 960 | 0.7755 | 0.8125 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1
|
{"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "microsoft/layoutlmv3-base", "model-index": [{"name": "fine-tuned-rvl-cdip", "results": []}]}
|
davidhajdu/fine-tuned-rvl-cdip
| null |
[
"transformers",
"safetensors",
"layoutlmv3",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:37:36+00:00
|
text-generation
|
transformers
|
{}
|
AndromedaPL/Prometheus-v0.2
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:38:10+00:00
|
|
text-generation
|
transformers
|
# 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]
|
{"library_name": "transformers", "tags": []}
|
tomaszki/llama-10-a
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:38:17+00:00
|
reinforcement-learning
|
sample-factory
|
A(n) **APPO** model trained on the **mujoco_ant** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r LLParallax/sf_Ant
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=mujoco_ant --train_dir=./train_dir --experiment=sf_Ant
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=mujoco_ant --train_dir=./train_dir --experiment=sf_Ant --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "mujoco_ant", "type": "mujoco_ant"}, "metrics": [{"type": "mean_reward", "value": "5230.16 +/- 1124.38", "name": "mean_reward", "verified": false}]}]}]}
|
LLParallax/sf_Ant
| null |
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T10:38:21+00:00
|
null |
transformers
|
{"license": "other", "license_name": "cnn", "license_link": "LICENSE"}
|
tutuhu/shanshui1
| null |
[
"transformers",
"safetensors",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:39:40+00:00
|
|
null |
peft
|
<!-- 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. -->
# breeze_7b_lora_completion_only_5_epochs
This model is a fine-tuned version of [MediaTek-Research/Breeze-7B-Instruct-v1_0](https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0) on the DandinPower/ZH-Reading-Comprehension-Breeze-Instruct dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1658
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1419 | 0.3690 | 250 | 0.1250 |
| 0.1404 | 0.7380 | 500 | 0.1611 |
| 0.1554 | 1.1070 | 750 | 0.1358 |
| 0.1426 | 1.4760 | 1000 | 0.1543 |
| 0.1194 | 1.8450 | 1250 | 0.1823 |
| 0.0865 | 2.2140 | 1500 | 0.1511 |
| 0.0728 | 2.5830 | 1750 | 0.1463 |
| 0.4116 | 2.9520 | 2000 | 0.1224 |
| 0.0405 | 3.3210 | 2250 | 0.1939 |
| 0.0573 | 3.6900 | 2500 | 0.1324 |
| 0.0237 | 4.0590 | 2750 | 0.1657 |
| 0.0208 | 4.4280 | 3000 | 0.1818 |
| 0.0111 | 4.7970 | 3250 | 0.1658 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"language": ["zh"], "license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "nycu-112-2-deeplearning-hw2", "generated_from_trainer"], "datasets": ["DandinPower/ZH-Reading-Comprehension-Breeze-Instruct"], "base_model": "MediaTek-Research/Breeze-7B-Instruct-v1_0", "model-index": [{"name": "breeze_7b_lora_completion_only_5_epochs", "results": []}]}
|
DandinPower/breeze_7b_lora_completion_only_5_epochs
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"nycu-112-2-deeplearning-hw2",
"generated_from_trainer",
"zh",
"dataset:DandinPower/ZH-Reading-Comprehension-Breeze-Instruct",
"base_model:MediaTek-Research/Breeze-7B-Instruct-v1_0",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T10:40:45+00:00
|
text-generation
|
transformers
|
{}
|
LucileFavero/llama_s2_5
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:41:36+00:00
|
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-132_WordLength_n-its-10
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-132_WordLength_n-its-10", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-1b_mz-132_WordLength_n-its-10
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:41:38+00:00
|
text-generation
|
transformers
|
# Llama-3-8B-saiga-suzume-ties
Llama-3-8B-saiga-suzume-ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [IlyaGusev/saiga_llama3_8b](https://huggingface.co/IlyaGusev/saiga_llama3_8b)
* [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual)
## 🧩 Configuration
```yaml
models:
- model: NousResearch/Meta-Llama-3-8B-Instruct
- model: IlyaGusev/saiga_llama3_8b
parameters:
density: 0.5
weight: 0.3
- model: lightblue/suzume-llama-3-8B-multilingual
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
normalize: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "d0rj/Llama-3-8B-saiga-suzume-ties"
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"])
```
or
```python
import torch
from transformers import AutoTokenizer, GenerationConfig, AutoModelForCausalLM
model_id = "d0rj/Llama-3-8B-saiga-suzume-ties"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.float16,
attn_implementation="flash_attention_2", # if you can
).to("cuda").eval()
generation_config = GenerationConfig(
do_sample=True,
top_k=30,
top_p=0.9,
temperature=1.04,
repeatition_penalty=1.2,
max_length=8192,
max_new_tokens=512,
min_new_tokens=2,
pad_token_id=tokenizer.eos_token_id,
)
data = tokenizer.apply_chat_template(
[
{"role": "system", "content": "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."},
{"role": "user", "content": "Привет! Как дела?"},
{"role": "assistant", "content": "Привет! Спасибо, дела неплохо. Как у тебя? Чем могу помочь?"},
{"role": "user", "content": "Расскажи, как сдать сессию, если лень даже думать о ней?"},
],
return_tensors="pt",
return_dict=True,
add_generation_prompt=True,
).to(model.device)
with torch.inference_mode():
output_ids = model.generate(
**data,
generation_config=generation_config
)[0]
output_ids = output_ids[len(data["input_ids"][0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True)
print(output.strip())
```
```
Сдача сессии — это важный момент в жизни каждого студента. Если вы чувствуете лень думать о ней, возможно, стоит попытаться найти мотивацию. Вот несколько советов, которые могут помочь:
1. **Определите причины своей лени.** Если лень связана с чем-то конкретным, попробуйте определить и устранить эту проблему. Например, может быть, вы недосыпаете, вечно устаете или что-то еще.
2. **Рассмотрите сессию как часть вашей жизни.** Понимание того, что сессия — это не просто обязанность, а также возможность учиться и развиваться, может изменить ваше отношение к этому процессу.
3. **Разбейте задачи на маленькие части.** Часто кажется, что большая задача непреодолима, но если разделить ее на меньшие, они станут более доступными.
4. **Планируйте и организуйте свое время.** Разработайте план изучения и следуйте ему. Это поможет вам лучше управлять своим временем и мотивацией.
5. **Получите поддержку.** Поделитесь своими трудностями с друзьями или семьей. Они могут предложить советы или поддержку.
6. **Найдите способы сделать изучение интересным.** Может быть, найдите что-то, что вам нравится, и начните изучать вместе с этим. Это поможет сделать процесс более приятным и стимулирует вас к обучению.
7. **Создайте для себя награды за выполнение задач.** Это может быть что-то простое, например, посмотреть свою любимую серию или сходить на прогулку. Таким образом, вы будете мотивированы продолжать изучение.
8. **Помните о своих целях.** Долгосрочные цели могут служить хорошим мотивационным фактором. Помните, что каждая сессия — это шаг к достижению ваших мечт.
Помните, что самое главное — это не сдача сессии, а процесс обучения и развития. Будьте добры к себе и не забывайте о своих успехах
```
|
{"language": ["ru", "en"], "license": "llama3", "tags": ["merge", "mergekit", "lazymergekit", "IlyaGusev/saiga_llama3_8b", "lightblue/suzume-llama-3-8B-multilingual"], "base_model": ["IlyaGusev/saiga_llama3_8b", "lightblue/suzume-llama-3-8B-multilingual"], "pipeline_tag": "text-generation"}
|
d0rj/Llama-3-8B-saiga-suzume-ties
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"IlyaGusev/saiga_llama3_8b",
"lightblue/suzume-llama-3-8B-multilingual",
"conversational",
"ru",
"en",
"base_model:IlyaGusev/saiga_llama3_8b",
"base_model:lightblue/suzume-llama-3-8B-multilingual",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:41:54+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABmbert_9_111_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:42:29+00:00
|
|
text-to-image
|
diffusers
|
# AutoTrain SDXL LoRA DreamBooth - Kajalbaria/autotrain-6wqjb-p3q03
<Gallery />
## Model description
These are Kajalbaria/autotrain-6wqjb-p3q03 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use <artist painting> to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](Kajalbaria/autotrain-6wqjb-p3q03/tree/main) them in the Files & versions tab.
|
{"license": "openrail++", "tags": ["autotrain", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "<artist painting>"}
|
Kajalbaria/autotrain-6wqjb-p3q03
| null |
[
"diffusers",
"autotrain",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null |
2024-04-25T10:42:47+00:00
|
text-generation
|
transformers
|
# jeiku/Average_Normie_v2_l3_8B AWQ
- Model creator: [jeiku](https://huggingface.co/jeiku)
- Original model: [Average_Normie_v2_l3_8B](https://huggingface.co/jeiku/Average_Normie_v2_l3_8B)
## Model Summary
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [ResplendentAI/Kei_Llama3_8B](https://huggingface.co/ResplendentAI/Kei_Llama3_8B) as a base.
The following models were included in the merge:
* [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B)
* [vicgalle/Roleplay-Llama-3-8B](https://huggingface.co/vicgalle/Roleplay-Llama-3-8B)
* [cgato/L3-TheSpice-8b-v0.1.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.1.3)
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
|
{"library_name": "transformers", "tags": ["mergekit", "merge", "4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "base_model": ["ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B", "vicgalle/Roleplay-Llama-3-8B", "cgato/L3-TheSpice-8b-v0.1.3", "ResplendentAI/Kei_Llama3_8B"], "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"}
|
solidrust/Average_Normie_v2_l3_8B-AWQ
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"conversational",
"arxiv:2403.19522",
"base_model:ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B",
"base_model:vicgalle/Roleplay-Llama-3-8B",
"base_model:cgato/L3-TheSpice-8b-v0.1.3",
"base_model:ResplendentAI/Kei_Llama3_8B",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:43:20+00:00
|
null |
peft
|
<!-- 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. -->
# codet5-770m-running
This model is a fine-tuned version of [Salesforce/codet5p-770m](https://huggingface.co/Salesforce/codet5p-770m) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.15.2
|
{"license": "bsd-3-clause", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "Salesforce/codet5p-770m", "model-index": [{"name": "codet5-770m-running", "results": []}]}
|
dtruong46me/codet5-770m-running
| null |
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:Salesforce/codet5p-770m",
"license:bsd-3-clause",
"region:us"
] | null |
2024-04-25T10:47:51+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HSE_PRAVO_complexity_classifier_large40steps
This model is a fine-tuned version of [ai-forever/ruBert-large](https://huggingface.co/ai-forever/ruBert-large) 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: 5e-06
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 15
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 40
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
{"tags": ["generated_from_trainer"], "base_model": "ai-forever/ruBert-large", "model-index": [{"name": "HSE_PRAVO_complexity_classifier_large40steps", "results": []}]}
|
marcus2000/HSE_PRAVO_complexity_classifier_large40steps
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:ai-forever/ruBert-large",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:49:34+00:00
|
automatic-speech-recognition
|
transformers
|
# 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]
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## Model Card Authors [optional]
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## Model Card Contact
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|
{"library_name": "transformers", "tags": []}
|
suke0327/whisper-large_front_en
| null |
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:49:55+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# arbert_arabic_dialect_identification
This model is a fine-tuned version of [lafifi-24/arbert_arabic_dialect_identification](https://huggingface.co/lafifi-24/arbert_arabic_dialect_identification) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7977
- F1-score: 0.5948
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 290 | 1.4459 | 0.5037 |
| 1.4958 | 2.0 | 580 | 1.5787 | 0.5153 |
| 1.4958 | 3.0 | 870 | 1.4938 | 0.5693 |
| 0.7952 | 4.0 | 1160 | 1.7462 | 0.5504 |
| 0.7952 | 5.0 | 1450 | 1.7977 | 0.5948 |
| 0.4922 | 6.0 | 1740 | 2.1508 | 0.5682 |
| 0.3171 | 7.0 | 2030 | 2.5673 | 0.5335 |
| 0.3171 | 8.0 | 2320 | 2.5563 | 0.5658 |
| 0.2204 | 9.0 | 2610 | 2.8487 | 0.5336 |
| 0.2204 | 10.0 | 2900 | 3.0247 | 0.5422 |
| 0.1511 | 11.0 | 3190 | 2.8925 | 0.5594 |
| 0.1511 | 12.0 | 3480 | 3.2729 | 0.5333 |
| 0.0967 | 13.0 | 3770 | 3.2754 | 0.5531 |
| 0.0615 | 14.0 | 4060 | 3.3330 | 0.5430 |
| 0.0615 | 15.0 | 4350 | 3.3549 | 0.5430 |
| 0.043 | 16.0 | 4640 | 3.3918 | 0.5637 |
| 0.043 | 17.0 | 4930 | 3.5727 | 0.5381 |
| 0.037 | 18.0 | 5220 | 3.5450 | 0.5499 |
| 0.0187 | 19.0 | 5510 | 3.5421 | 0.5394 |
| 0.0187 | 20.0 | 5800 | 3.5514 | 0.5403 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"tags": ["generated_from_trainer"], "base_model": "lafifi-24/arbert_arabic_dialect_identification", "model-index": [{"name": "arbert_arabic_dialect_identification", "results": []}]}
|
yemen2016/arbert_arabic_dialect_identification
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:lafifi-24/arbert_arabic_dialect_identification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:50:04+00:00
|
automatic-speech-recognition
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# acoustic_model0_cv_17_fr_XLSR-53
This model was trained from scratch on the Common Voice 17 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4810
- eval_wer: 0.3881
- eval_runtime: 167.4913
- eval_samples_per_second: 6.538
- eval_steps_per_second: 0.818
- epoch: 11.0465
- step: 1600
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- 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_steps: 500
- training_steps: 2000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"language": ["fr"], "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_17_0"], "model-index": [{"name": "acoustic_model0_cv_17_fr_XLSR-53", "results": []}]}
|
SemValX/wav2vec2-standard_model0-xlsr-53-fr-colab
| null |
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"fr",
"dataset:mozilla-foundation/common_voice_17_0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:50:55+00:00
|
text-to-image
|
diffusers
|
# SDXL LoRA DreamBooth - computational-mama/underwater-humanoid
<Gallery />
## Model description
### These are computational-mama/underwater-humanoid LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`underwater-humanoid.safetensors` here 💾](/computational-mama/underwater-humanoid/blob/main/underwater-humanoid.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:underwater-humanoid:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`underwater-humanoid_emb.safetensors` here 💾](/computational-mama/underwater-humanoid/blob/main/underwater-humanoid_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `underwater-humanoid_emb` to your prompt. For example, `A underwater-humanoid_emb character`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('computational-mama/underwater-humanoid', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='computational-mama/underwater-humanoid', filename='underwater-humanoid_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A <s0><s1> character').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/computational-mama/underwater-humanoid/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A <s0><s1> character, An underwater humanoid that floats surrounded by coral, fish and algae, with translucent skin, no genus, no nose, a soft body, large fish-like antennae on the face, and tentacular, webbed extremities. In the background, a submarine cave lets in light from the surface. Photorealistic", "output": {"url": "image-0.png"}}, {"text": "A <s0><s1> character, An underwater humanoid with salt crystals on its face floating in a seated position surrounded by fish, coral and algae, with translucent skin, no genus, no nose, veins and organs visible through the skin, spaced-aperture eyes without a globe, and webbed extremities. In the background, a submarine cave lets in light from the surface. Photorealistic", "output": {"url": "image-1.png"}}, {"text": "A <s0><s1> character, An underwater humanoid-algae that swims surrounded by coral and fish and algae, harvests algae, with translucent skin, no genus, no nose, organs visible through the skin, it has a head of large fish antennae on its face, and tentacular, webbed extremities. Fish are attached to its body. In the background, a submarine cave lets in light from the surface. Photorealistic", "output": {"url": "image-2.png"}}, {"text": "A <s0><s1> character, A winged underwater humanoid sits surrounded by its garden of coral and algae, with translucent, genderless skin, a snout-like nose, muscles and organs visible through the skin, large globe-shaped eyes and webbed extremities. In the background, a submarine cave lets in light from the surface. Photorealistic", "output": {"url": "image-3.png"}}, {"text": "A <s0><s1> character, An underwater humanoid crowned with translucent antennae appears from behind corals and algae, with translucent skin, no genus, no nose, muscles and organs visible through the skin, flexible, tubular limbs, no eyes, and slender, humanoid extremities. In the background, a submarine cave lets in light from the surface. Photorealistic", "output": {"url": "image-4.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A <s0><s1> character"}
|
computational-mama/underwater-humanoid
| null |
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null |
2024-04-25T10:51:10+00:00
|
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