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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| likes
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11.7k
| library_name
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paulonasc7/Taxi-v3 | paulonasc7 | 2023-11-08T21:54:32Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T21:54:30Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="paulonasc7/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
bartowski/opus-v0-7b-exl2 | bartowski | 2023-11-08T21:51:15Z | 3 | 1 | null | [
"text-generation",
"en",
"region:us"
]
| text-generation | 2023-11-08T20:24:54Z | ---
language:
- en
pipeline_tag: text-generation
quantized_by: bartowski
---
## Exllama v2 Quantizations of opus-v0-7b
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.7">turboderp's ExLlamaV2 v0.0.7</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.
Original model: https://huggingface.co/dreamgen/opus-v0-7b
<a href="https://huggingface.co/bartowski/opus-v0-7b-exl2/tree/4.0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/opus-v0-7b-exl2/tree/6.0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/opus-v0-7b-exl2/tree/8.0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/opus-v0-7b-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `opus-v0-7b-exl2`:
```shell
mkdir opus-v0-7b-exl2
huggingface-cli download bartowski/opus-v0-7b-exl2 --local-dir opus-v0-7b-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir opus-v0-7b-exl2
huggingface-cli download bartowski/opus-v0-7b-exl2 --revision 4.0 --local-dir opus-v0-7b-exl2 --local-dir-use-symlinks False
```
|
paulonasc7/q-FrozenLake-v1-4x4-noSlippery | paulonasc7 | 2023-11-08T21:36:18Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T21:36:15Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="paulonasc7/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
judy93536/distilroberta-base-reuters-bloomberg-ep30-ep20 | judy93536 | 2023-11-08T21:34:00Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:judy93536/distilroberta-newsapi121k",
"base_model:finetune:judy93536/distilroberta-newsapi121k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-11-08T13:39:02Z | ---
license: apache-2.0
base_model: judy93536/distilroberta-base-reuters-bloomberg
tags:
- generated_from_trainer
model-index:
- name: distilroberta-base-reuters-bloomberg-ep30-ep20
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-reuters-bloomberg-ep30-ep20
This model is a fine-tuned version of [judy93536/distilroberta-base-reuters-bloomberg](https://huggingface.co/judy93536/distilroberta-base-reuters-bloomberg) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2767
## 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: 7.2115e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.12
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 1.3775 | 1.0 | 13905 | 1.3298 |
| 1.4586 | 2.0 | 27810 | 1.4049 |
| 1.5213 | 3.0 | 41715 | 1.4486 |
| 1.5175 | 4.0 | 55620 | 1.4431 |
| 1.5007 | 5.0 | 69525 | 1.4346 |
| 1.4875 | 6.0 | 83430 | 1.4237 |
| 1.4695 | 7.0 | 97335 | 1.4145 |
| 1.4625 | 8.0 | 111240 | 1.4062 |
| 1.4343 | 9.0 | 125145 | 1.3892 |
| 1.4276 | 10.0 | 139050 | 1.3822 |
| 1.4147 | 11.0 | 152955 | 1.3658 |
| 1.3914 | 12.0 | 166860 | 1.3549 |
| 1.3774 | 13.0 | 180765 | 1.3425 |
| 1.3691 | 14.0 | 194670 | 1.3323 |
| 1.3523 | 15.0 | 208575 | 1.3193 |
| 1.3354 | 16.0 | 222480 | 1.3098 |
| 1.3221 | 17.0 | 236385 | 1.2990 |
| 1.3083 | 18.0 | 250290 | 1.2885 |
| 1.3052 | 19.0 | 264195 | 1.2830 |
| 1.2818 | 20.0 | 278100 | 1.2793 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
ArtiKitten/ppo-Huggy | ArtiKitten | 2023-11-08T21:22:27Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-11-08T21:22:16Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ArtiKitten/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
oraul/pneumonia_SD_1 | oraul | 2023-11-08T21:08:20Z | 2 | 2 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2023-11-08T18:40:30Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of pneumonia disease
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
MayIBorn/mrpc_qlora-llama-7b-init-svd-A_from_back | MayIBorn | 2023-11-08T20:58:33Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:huggyllama/llama-7b",
"base_model:adapter:huggyllama/llama-7b",
"region:us"
]
| null | 2023-11-08T20:58:27Z | ---
library_name: peft
base_model: huggyllama/llama-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
lmqg/mt5-small-koquad-qg-trimmed-50000 | lmqg | 2023-11-08T20:44:31Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-08T20:30:38Z | # Vocabulary Trimmed [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg): `lmqg/mt5-small-koquad-qg-trimmed-50000`
This model is a trimmed version of [lmqg/mt5-small-koquad-qg](https://huggingface.co/lmqg/mt5-small-koquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-koquad-qg | lmqg/mt5-small-koquad-qg-trimmed-50000 |
|:---------------------------|:---------------------------|:-----------------------------------------|
| parameter_size_full | 300,165,504 | 95,264,128 |
| parameter_size_embedding | 256,103,424 | 51,202,048 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 31.74 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ko | vocabtrimmer/mc4_validation | text | ko | validation | 50000 | 2 | |
waldie/Yi-34B-GiftedConvo-merged-4bpw-h6-exl2 | waldie | 2023-11-08T20:44:25Z | 8 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:NobodyExistsOnTheInternet/GiftedConvoBeforeEcons",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-11-08T19:53:59Z | ---
license: mit
datasets:
- NobodyExistsOnTheInternet/GiftedConvoBeforeEcons
---
Trained on over 20k instruct generated all by gpt-4 or humans
Dataset features:
1000 long evolved conversations based off LIMA
Subsection of correct PRM800k data
Subsection of CamelAI's Physics and Chemistry data
The model is trained with Qlora as well as Axolotl. |
VanoInvestigations/bertin-gpt-j-6B-es-finetuned-BOE-summary-LoRA-8bit | VanoInvestigations | 2023-11-08T20:27:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bertin-project/bertin-gpt-j-6B",
"base_model:adapter:bertin-project/bertin-gpt-j-6B",
"region:us"
]
| null | 2023-11-08T20:26:22Z | ---
library_name: peft
base_model: bertin-project/bertin-gpt-j-6B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.7.0.dev0
|
HeitorMatt/bert-finetuned-ner | HeitorMatt | 2023-11-08T20:27:02Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-08T15:02:00Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9291714709273596
- name: Recall
type: recall
value: 0.9493436553349041
- name: F1
type: f1
value: 0.9391492549737784
- name: Accuracy
type: accuracy
value: 0.9860923058809677
---
<!-- 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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Precision: 0.9292
- Recall: 0.9493
- F1: 0.9391
- Accuracy: 0.9861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0784 | 1.0 | 1756 | 0.0815 | 0.9080 | 0.9307 | 0.9192 | 0.9798 |
| 0.0371 | 2.0 | 3512 | 0.0606 | 0.9287 | 0.9492 | 0.9388 | 0.9857 |
| 0.0202 | 3.0 | 5268 | 0.0614 | 0.9292 | 0.9493 | 0.9391 | 0.9861 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Laurent1/Mistral-7B-Instruct-v0.1-QLoRa-medical-QA | Laurent1 | 2023-11-08T20:25:01Z | 18 | 1 | peft | [
"peft",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"region:us"
]
| null | 2023-11-06T11:31:47Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.1
---
# Model Card for Mistral-7B-Instruct-v0.1-QLoRa-medical-QA
<font color="FF0000" size="5"> <b>
This is a QA model for answering medical questions<br /> </b></font>
<br><b>Foundation Model : https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1 <br />
Dataset : https://huggingface.co/datasets/Laurent1/MedQuad-MedicalQnADataset_128tokens_max <br /></b>
The model has been fine tuned with 2 x GPU T4 (RAM : 2 x 14.8GB) + CPU (RAM : 29GB). <br />
## <b>Model Details</b>
The model is based upon the foundation model : Mistral-7B-Instruct-v0.1.<br />
It has been tuned with Supervised Fine-tuning Trainer and PEFT LoRa.<br />
### Librairies
<ul>
<li>bitsandbytes</li>
<li>einops</li>
<li>peft</li>
<li>trl</li>
<li>datasets</li>
<li>transformers</li>
<li>torch</li>
</ul>
## <b>Bias, Risks, and Limitations</b>
In order to reduce training duration, the model has been trained only with the first 5100 rows of the dataset.<br />
<font color="FF0000">
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.<br />
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.<br />
</font>
## <b>Training Details</b>
<ul>
<li>per_device_train_batch_size = 1</li>
<li>gradient_accumulation_steps = 16</li>
<li>epoch = 5</li>
<li>2 x GPU T4 (RAM : 14.8GB) + CPU (RAM : 29GB)</li>
</ul>
### Notebook used for the training
You can find it in the files and versions tab
### Training Data
https://huggingface.co/datasets/Laurent1/MedQuad-MedicalQnADataset_128tokens_max
#### Training Hyperparameters
#### Times
Training duration : 6287.4s
|
Laurent1/mpt-7b-instruct2-QLoRa-medical-QA | Laurent1 | 2023-11-08T20:22:35Z | 1 | 0 | adapter-transformers | [
"adapter-transformers",
"medical",
"dataset:Laurent1/MedQuad-MedicalQnADataset_128tokens_max",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-04T07:29:40Z | ---
license: apache-2.0
datasets:
- Laurent1/MedQuad-MedicalQnADataset_128tokens_max
library_name: adapter-transformers
tags:
- medical
---
# Model Card for mpt-7b-instruct2-QLoRa-medical-QA
<font color="FF0000" size="5"> <b>
This is a QA model for answering medical questions<br /> </b></font>
<br><b>Foundation Model : https://huggingface.co/ibm/mpt-7b-instruct2 <br />
Dataset : https://huggingface.co/datasets/Laurent1/MedQuad-MedicalQnADataset_128tokens_max <br /></b>
The model has been fine tuned with 2 x GPU T4 (RAM : 2 x 14.8GB) + CPU (RAM : 29GB). <br />
## <b>Model Details</b>
The model is based upon the foundation model : ibm/mpt-7b-instruct2 (Apache 2.0 License).<br />
It has been tuned with Supervised Fine-tuning Trainer and PEFT LoRa.<br />
### Librairies
<ul>
<li>bitsandbytes</li>
<li>einops</li>
<li>peft</li>
<li>trl</li>
<li>datasets</li>
<li>transformers</li>
<li>torch</li>
</ul>
### Notebook used for the training
You can find it in the files and versions tab or :
https://colab.research.google.com/drive/14nxSP5UuJcnIJtEERyk5nehBL3W03FR3?hl=fr
=> Improvements can be achieved by increasing the number of steps and using the full dataset. <br />
### Direct Use
## <b>Bias, Risks, and Limitations</b>
In order to reduce training duration, the model has been trained only with the first 5100 rows of the dataset.<br />
<font color="FF0000">
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.<br />
Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models.<br />
</font>
## <b>Training Details</b>
<ul>
<li>per_device_train_batch_size = 1</li>
<li>gradient_accumulation_steps = 16</li>
<li>epoch = 5</li>
<li>2 x GPU T4 (RAM : 14.8GB) + CPU (RAM : 29GB)</li>
</ul>
### Training Data
https://huggingface.co/datasets/Laurent1/MedQuad-MedicalQnADataset_128tokens_max
#### Training Hyperparameters
#### Times
Training duration : 6287.4s
 |
LazzeKappa/L04 | LazzeKappa | 2023-11-08T20:14:46Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"region:us"
]
| null | 2023-11-02T09:57:21Z | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: L04
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# L04
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3320
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3716 | 1.0 | 71 | 0.3512 |
| 0.3324 | 2.0 | 142 | 0.3387 |
| 0.2808 | 3.0 | 213 | 0.3339 |
| 0.2974 | 4.0 | 284 | 0.3320 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
|
lmqg/mt5-small-dequad-qg-ae-trimmed-50000 | lmqg | 2023-11-08T20:12:10Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-08T01:25:14Z | # Vocabulary Trimmed [lmqg/mt5-small-dequad-qg-ae](https://huggingface.co/lmqg/mt5-small-dequad-qg-ae): `lmqg/mt5-small-dequad-qg-ae-trimmed-50000`
This model is a trimmed version of [lmqg/mt5-small-dequad-qg-ae](https://huggingface.co/lmqg/mt5-small-dequad-qg-ae) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-dequad-qg-ae | lmqg/mt5-small-dequad-qg-ae-trimmed-50000 |
|:---------------------------|:------------------------------|:--------------------------------------------|
| parameter_size_full | 300,165,504 | 95,264,128 |
| parameter_size_embedding | 256,103,424 | 51,202,048 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 31.74 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| de | vocabtrimmer/mc4_validation | text | de | validation | 50000 | 2 | |
kwagh20ite/pneumonia | kwagh20ite | 2023-11-08T20:09:59Z | 5 | 0 | diffusers | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
]
| text-to-image | 2023-11-08T18:22:59Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of pneumonia disease
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
kariver/detr-resnet-50_adafactor_finetuned_food-roboflow | kariver | 2023-11-08T20:08:44Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| object-detection | 2023-11-08T19:45:00Z | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: detr-resnet-50_adafactor_finetuned_food-roboflow
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50_adafactor_finetuned_food-roboflow
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5062
## 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
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.4038 | 1.52 | 50 | 5.7574 |
| 5.3942 | 3.03 | 100 | 4.7285 |
| 4.5081 | 4.55 | 150 | 3.8652 |
| 3.6495 | 6.06 | 200 | 3.0781 |
| 3.2792 | 7.58 | 250 | 2.8378 |
| 3.0286 | 9.09 | 300 | 2.6613 |
| 2.948 | 10.61 | 350 | 2.6172 |
| 2.8826 | 12.12 | 400 | 2.5483 |
| 2.7976 | 13.64 | 450 | 2.5062 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Youssef11/layoutlmv3-finetuned-cord_100 | Youssef11 | 2023-11-08T19:53:03Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:cord-layoutlmv3",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-11-08T18:30:41Z | ---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- cord-layoutlmv3
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-cord_100
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: cord-layoutlmv3
type: cord-layoutlmv3
config: cord
split: test
args: cord
metrics:
- name: Precision
type: precision
value: 0.9451851851851852
- name: Recall
type: recall
value: 0.9550898203592815
- name: F1
type: f1
value: 0.9501116902457185
- name: Accuracy
type: accuracy
value: 0.9596774193548387
---
<!-- 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-cord_100
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the cord-layoutlmv3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2033
- Precision: 0.9452
- Recall: 0.9551
- F1: 0.9501
- Accuracy: 0.9597
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 2500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.56 | 250 | 0.9547 | 0.7300 | 0.7912 | 0.7593 | 0.8065 |
| 1.2994 | 3.12 | 500 | 0.5497 | 0.8410 | 0.8630 | 0.8519 | 0.8714 |
| 1.2994 | 4.69 | 750 | 0.3688 | 0.8846 | 0.9064 | 0.8954 | 0.9189 |
| 0.3917 | 6.25 | 1000 | 0.3156 | 0.9152 | 0.9289 | 0.9220 | 0.9359 |
| 0.3917 | 7.81 | 1250 | 0.2468 | 0.9326 | 0.9424 | 0.9375 | 0.9457 |
| 0.2136 | 9.38 | 1500 | 0.2290 | 0.9299 | 0.9431 | 0.9365 | 0.9499 |
| 0.2136 | 10.94 | 1750 | 0.2101 | 0.9429 | 0.9513 | 0.9471 | 0.9571 |
| 0.1388 | 12.5 | 2000 | 0.2090 | 0.9380 | 0.9513 | 0.9446 | 0.9571 |
| 0.1388 | 14.06 | 2250 | 0.2049 | 0.9423 | 0.9528 | 0.9475 | 0.9580 |
| 0.111 | 15.62 | 2500 | 0.2033 | 0.9452 | 0.9551 | 0.9501 | 0.9597 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
fhieni/Vietnamese_VITS | fhieni | 2023-11-08T19:52:17Z | 0 | 1 | null | [
"region:us"
]
| null | 2023-11-08T19:47:19Z | # Vietnamese Voice Clone
## Data Preparation
***If you use custom data***
- Config your custom data follow this format:
- Create folder: DATA
- Subfolder: DATA/wavs -> which contain <audio_id>.wav files inside
- DATA/train.txt and DATA/val.txt: with format each line follow format: <audio_id><space>transcript
- If you dont have transcript, please check wav2vec inference script
***If you try with VIVOS***
```
wget http://ailab.hcmus.edu.vn/assets/vivos.tar.gz
tar xzf vivos.tar.gz
```
```
mkdir -p DATA/wavs
scp -v vivos/*/waves/*/*.wav DATA/wavs
```
```
cat vivos/test/prompts.txt > DATA/val.txt
cat vivos/test/prompts.txt > DATA/train.txt
cat vivos/train/prompts.txt >> DATA/train.txt
```
## Install environment
```
conda create -y -n viclone python=3.8
conda activate viclone
conda install cudatoolkit=11.3.1 cudnn=8.2.1
```
```
python -m pip install torch==1.12.0+cu116 torchvision==0.13.0+cu116 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu116
python -m pip install -r requirements.txt
```
```
cd vits/monotonic_align
mkdir monotonic_align
python setup.py build_ext --inplace
```
## Process data
```
python Step1_data_processing.py
```
## Extract feature
```
python Step2_extract_feature.py
```
## Train model
```
python train_ms.py -c configs/vivos.json -m vivos
```
## Demo
```python app.py```
Then check port: http://127.0.0.1:7860/ |
BramVanroy/falcon-40b-ft-alpaca-dolly-dutch | BramVanroy | 2023-11-08T19:43:19Z | 25 | 4 | transformers | [
"transformers",
"safetensors",
"falcon",
"text-generation",
"nl",
"dataset:BramVanroy/alpaca-dolly-dutch",
"base_model:tiiuae/falcon-40b",
"base_model:finetune:tiiuae/falcon-40b",
"doi:10.57967/hf/0864",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-07-06T10:22:49Z | ---
language:
- nl
license: cc-by-nc-4.0
datasets:
- BramVanroy/alpaca-dolly-dutch
inference: false
base_model: tiiuae/falcon-40b
model-index:
- name: falcon-7b-ft-alpaca-cleaned-dutch
results: []
---
# falcon-40b-ft-alpaca-dolly-dutch
## Model description
This model is a fine-tuned version of [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) on the [BramVanroy/alpaca-dolly-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-dolly-dutch) dataset.
See the original [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b) for more information, intended use, and biases.
## Intended uses & limitations
This model is intended as a (poor) baseline for Dutch generative LLMs. It by no means aims to provide SOTA performance and is specifically intended for research purposes and experimentation.
## Example usage
In the example below, you see a query `Wat hoort er niet in dit rijtje thuis? Leg ook uit waarom.` ("What does not belong in the list? Explain why.") with given input "aap, muis, auto, vogel" ("monkey, mouse, car, bird").
The model "replies" (cut off due to `max_new_tokens`):
> "Auto" hoort niet in het rijtje, omdat het geen levend wezen is.
> Een auto is een voertuig dat wordt aangedreven door een motor en wordt gebruikt om mensen en goederen van de ene plaats naar de andere te verplaatsen. Het is een machine gemaakt door mensen, in tegenstelling tot levende wezens zoals een aap, een muis of een vogel.
> Auto's zijn gemaakt van metalen, plastic en andere materialen, terwijl levende organismen bestaan uit cellen en weefsels. Auto's
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
def format_alpaca_sample(instruction: str, input_text: str):
if len(input_text) >= 2:
text = f'''Hieronder staat een instructie `Instruction` die een taak beschrijft, gecombineerd met een invoer `Input` die verdere context biedt. Schrijf een antwoord na `Response:` dat het verzoek op de juiste manier voltooit of beantwoordt.
### Instruction:
{instruction}
### Input:
{input_text}
### Response:
'''
else:
text = f'''Hieronder staat een instructie `Instruction` die een taak beschrijft. Schrijf een antwoord na `Response:` dat het verzoek op de juiste manier voltooit of beantwoordt.
### Instruction:
{instruction}
### Response:
'''
return text
@torch.no_grad()
def generate(model, tokenizer, instruction: str, input_text: str = ""):
input_prompt = format_alpaca_sample(instruction, input_text)
inputs = tokenizer([input_prompt], return_tensors="pt")
generated_ids = model.generate(
input_ids=inputs["input_ids"].to(model.device),
attention_mask=inputs["attention_mask"].to(model.device),
max_new_tokens=128,
temperature=0.4,
num_beams=3,
no_repeat_ngram_size=4,
length_penalty=0.9,
early_stopping=True,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
).detach().to("cpu")[0]
return tokenizer.decode(generated_ids)
model_name = "BramVanroy/falcon-40b-ft-alpaca-dolly-dutch"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
load_in_4bit=True,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto"
)
model.eval()
instruction = "Wat hoort er niet in dit rijtje thuis? Leg ook uit waarom."
input_text = "aap, muis, auto, vogel"
generation = generate(model, tokenizer, instruction, input_text)
```
## Citation
If you want to refer to this model, you can cite the following:
Vanroy, B. (2023). Falcon 40B Finetuned on Dutch Translations of Alpca and Dolly. https://doi.org/10.57967/hf/0864
```bibtext
@misc{vanroy2023falcon40b_instruct_dutch,
author = { Vanroy, Bram },
title = { Falcon 40B Finetuned on Dutch Translations of Alpaca and Dolly},
year = 2023,
url = { https://huggingface.co/BramVanroy/falcon-40b-ft-alpaca-dolly-dutch },
doi = { 10.57967/hf/0864 },
publisher = { Hugging Face }
}
```
## Training and evaluation data
Trained on the synthetic [BramVanroy/alpaca-dolly-dutch](https://huggingface.co/datasets/BramVanroy/alpaca-dolly-dutch) instruction dataset.
Therefore, commercial use of this model is forbidden. The model is intended for research purposes only.
- [Dolly 15k](https://huggingface.co/datasets/BramVanroy/dolly-15k-dutch) (translated to Dutch)
- [Alpaca cleaned](https://huggingface.co/datasets/BramVanroy/alpaca-cleaned-dutch) (translated to Dutch)
## Training procedure
Trained with LoRA and merged before upload. The adapters are in the `adapters` branch.
Prompt template (where the input is optional and can be left out):
```
Hieronder staat een instructie `Instruction` die een taak beschrijft, gecombineerd met een invoer `Input` die verdere context biedt. Schrijf een antwoord na `Response:` dat het verzoek op de juiste manier voltooit of beantwoordt.
### Instruction:
{instruction}
### Input:
{input}
### Response:
{response}
```
The loss was only calculated on the response prediction.
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 5 (but with early stopping)
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1656 | 0.16 | 20 | 1.0107 |
| 0.9778 | 0.32 | 40 | 0.9711 |
| 1.0424 | 0.49 | 60 | 0.9512 |
| 0.9858 | 0.65 | 80 | 0.9415 |
| 0.9457 | 0.81 | 100 | 0.9341 |
| 1.0584 | 0.97 | 120 | 0.9277 |
| 1.0284 | 1.14 | 140 | 0.9372 |
| 0.8781 | 1.3 | 160 | 0.9295 |
| 0.9531 | 1.46 | 180 | 0.9267 |
| 0.9496 | 1.62 | 200 | 0.9226 |
| 0.9178 | 1.78 | 220 | 0.9192 |
| 1.0763 | 1.95 | 240 | 0.9154 |
| 0.9561 | 2.11 | 260 | 0.9423 |
| 0.7991 | 2.27 | 280 | 0.9368 |
| 0.8503 | 2.43 | 300 | 0.9363 |
| 0.8749 | 2.6 | 320 | 0.9299 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.1+cu117
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Pinguin/Vanellope | Pinguin | 2023-11-08T19:31:16Z | 23 | 5 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:cc",
"region:us"
]
| text-to-image | 2023-11-08T19:30:47Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/image - 2023-09-26T161247.587.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/image - 2023-09-26T152137.826.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/image - 2023-09-26T145205.906.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/image - 2023-09-26T143938.981.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/image - 2023-09-26T143934.329.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/image - 2023-09-26T143722.444.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Vanellope von Schweetz
license: cc
---
# Vanellope Von
<Gallery />
## Model description
Vanellope von Schweetz from Disney.
## Trigger words
You should use `Vanellope von Schweetz` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Pinguin/Vanellope/tree/main) them in the Files & versions tab.
|
ravisv73/mistral_7b-instruct-knowthyself | ravisv73 | 2023-11-08T19:28:02Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"endpoints_compatible",
"region:us"
]
| null | 2023-11-08T17:03:23Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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: bfloat16
### Framework versions
- PEFT 0.6.0
|
bartowski/LLaMA2-13B-TiefighterLR-exl2 | bartowski | 2023-11-08T19:22:11Z | 0 | 0 | null | [
"license:llama2",
"region:us"
]
| null | 2023-11-08T06:25:18Z | ---
license: llama2
quantized_by: bartowski
---
## Exllama v2 Quantizations of LLaMA2-13B-TiefighterLR
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.7">turboderp's ExLlamaV2 v0.0.7</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using wikitext-103-raw-v1-test.parquet as calibration dataset.
Original model: https://huggingface.co/KoboldAI/LLaMA2-13B-TiefighterLR
<a href="https://huggingface.co/bartowski/LLaMA2-13B-TiefighterLR-exl2/tree/3.75">3.75 bits per weight</a>
<a href="https://huggingface.co/bartowski/LLaMA2-13B-TiefighterLR-exl2/tree/4.0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/LLaMA2-13B-TiefighterLR-exl2/tree/4.25">4.25 bits per weight</a>
<a href="https://huggingface.co/bartowski/LLaMA2-13B-TiefighterLR-exl2/tree/5.0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/LLaMA2-13B-TiefighterLR-exl2/tree/6.0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/LLaMA2-13B-TiefighterLR-exl2/tree/7.0">7.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/LLaMA2-13B-TiefighterLR-exl2/tree/8.0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4.0 https://huggingface.co/bartowski/LLaMA2-13B-TiefighterLR-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `LLaMA2-13B-TiefighterLR-exl2`:
```shell
mkdir LLaMA2-13B-TiefighterLR-exl2
huggingface-cli download bartowski/LLaMA2-13B-TiefighterLR-exl2 --local-dir LLaMA2-13B-TiefighterLR-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir LLaMA2-13B-TiefighterLR-exl2
huggingface-cli download bartowski/LLaMA2-13B-TiefighterLR-exl2 --revision 4.0 --local-dir LLaMA2-13B-TiefighterLR-exl2 --local-dir-use-symlinks False
```
|
speechGenius/whisper-tiny-dv | speechGenius | 2023-11-08T19:20:14Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-11-06T22:22:35Z | ---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
model-index:
- name: whisper-tiny-dv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-dv
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the common_voice_13_0 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:--------:|
| No log | 0.71 | 5 | 3.2382 | 178.0488 | 176.0976 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hiba66/my-pet-dog-xzg | hiba66 | 2023-11-08T19:11:50Z | 0 | 0 | null | [
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-11-08T19:09:05Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-xzg Dreambooth model trained by hiba66 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: KMCT-158
Sample pictures of this concept:
.jpg)
|
mrmegatelo/q-FrozenLake-v1-4x4-noSlippery | mrmegatelo | 2023-11-08T19:09:59Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T19:09:56Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="mrmegatelo/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
hpandana/rl_course_vizdoom_health_gathering_supreme | hpandana | 2023-11-08T18:58:09Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T17:47:40Z | ---
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: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.26 +/- 5.54
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** 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 hpandana/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
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 .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --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.
|
toddwilson147/ppo-Huggy | toddwilson147 | 2023-11-08T18:49:02Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-11-08T18:48:57Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: toddwilson147/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
lmqg/mt5-base-jaquad-qg-ae-trimmed-50000 | lmqg | 2023-11-08T18:32:36Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-08T15:17:36Z | # Vocabulary Trimmed [lmqg/mt5-base-jaquad-qg-ae](https://huggingface.co/lmqg/mt5-base-jaquad-qg-ae): `lmqg/mt5-base-jaquad-qg-ae-trimmed-50000`
This model is a trimmed version of [lmqg/mt5-base-jaquad-qg-ae](https://huggingface.co/lmqg/mt5-base-jaquad-qg-ae) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-base-jaquad-qg-ae | lmqg/mt5-base-jaquad-qg-ae-trimmed-50000 |
|:---------------------------|:-----------------------------|:-------------------------------------------|
| parameter_size_full | 582,384,384 | 275,032,320 |
| parameter_size_embedding | 384,155,136 | 76,803,072 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 47.23 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ja | vocabtrimmer/mc4_validation | text | ja | validation | 50000 | 2 | |
maxwell-pi/effort | maxwell-pi | 2023-11-08T18:27:22Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
]
| null | 2023-11-08T18:27:18Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0
|
Oleg1963/55 | Oleg1963 | 2023-11-08T18:07:14Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"art",
"ru",
"dataset:fka/awesome-chatgpt-prompts",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-08T18:04:22Z | ---
license: apache-2.0
datasets:
- fka/awesome-chatgpt-prompts
language:
- ru
metrics:
- accuracy
library_name: adapter-transformers
tags:
- art
--- |
elemosynov/ppo-SnowballTarget | elemosynov | 2023-11-08T17:58:17Z | 3 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-11-08T16:08:11Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: elemosynov/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
HarrisShen/llama2-compressed-notes-impression-50-50-epoch-4 | HarrisShen | 2023-11-08T17:54:16Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
]
| null | 2023-11-08T17:54:14Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0
|
lmqg/mt5-small-esquad-qg-ae-trimmed-50000 | lmqg | 2023-11-08T17:29:58Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-08T01:30:41Z | # Vocabulary Trimmed [lmqg/mt5-small-esquad-qg-ae](https://huggingface.co/lmqg/mt5-small-esquad-qg-ae): `lmqg/mt5-small-esquad-qg-ae-trimmed-50000`
This model is a trimmed version of [lmqg/mt5-small-esquad-qg-ae](https://huggingface.co/lmqg/mt5-small-esquad-qg-ae) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-esquad-qg-ae | lmqg/mt5-small-esquad-qg-ae-trimmed-50000 |
|:---------------------------|:------------------------------|:--------------------------------------------|
| parameter_size_full | 300,165,504 | 95,264,128 |
| parameter_size_embedding | 256,103,424 | 51,202,048 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 31.74 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| es | vocabtrimmer/mc4_validation | text | es | validation | 50000 | 2 | |
lukasdrg/clinical_longformer_same_tokens_1epochs_50k | lukasdrg | 2023-11-08T17:27:01Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"longformer",
"fill-mask",
"generated_from_trainer",
"base_model:lukasdrg/clinical_longformer_same_tokens_1epochs",
"base_model:finetune:lukasdrg/clinical_longformer_same_tokens_1epochs",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-11-08T14:16:37Z | ---
base_model: lukasdrg/clinical_longformer_same_tokens_1epochs
tags:
- generated_from_trainer
model-index:
- name: clinical_longformer_same_tokens_1epochs_50k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clinical_longformer_same_tokens_1epochs_50k
This model is a fine-tuned version of [lukasdrg/clinical_longformer_same_tokens_1epochs](https://huggingface.co/lukasdrg/clinical_longformer_same_tokens_1epochs) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7273
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9394 | 0.18 | 65 | 1.7768 |
| 2.0258 | 0.37 | 130 | 1.7716 |
| 2.0671 | 0.55 | 195 | 1.7761 |
| 2.064 | 0.74 | 260 | 1.7355 |
| 1.8052 | 0.92 | 325 | 1.7273 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
lmqg/mt5-small-itquad-qg-ae-trimmed-50000 | lmqg | 2023-11-08T17:26:29Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-08T05:51:56Z | # Vocabulary Trimmed [lmqg/mt5-small-itquad-qg-ae](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae): `lmqg/mt5-small-itquad-qg-ae-trimmed-50000`
This model is a trimmed version of [lmqg/mt5-small-itquad-qg-ae](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-small-itquad-qg-ae | lmqg/mt5-small-itquad-qg-ae-trimmed-50000 |
|:---------------------------|:------------------------------|:--------------------------------------------|
| parameter_size_full | 300,165,504 | 95,264,128 |
| parameter_size_embedding | 256,103,424 | 51,202,048 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 31.74 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| it | vocabtrimmer/mc4_validation | text | it | validation | 50000 | 2 | |
waldie/Trion-M-7b-8bpw-h8-exl2 | waldie | 2023-11-08T17:26:05Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"Mistral",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-11-08T16:58:36Z | ---
license: apache-2.0
language:
- en
tags:
- Mistral
---
This is a gradient blockmerge (0.8,0.2) of two Mistral models.
The logic model is a SLERP merge of https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B and https://huggingface.co/openchat/openchat_3.5
The prose model is a SLERP merge of https://huggingface.co/NousResearch/Nous-Capybara-7B-V1.9 and https://huggingface.co/HuggingFaceH4/zephyr-7b-beta |
llama-lang-adapt/llama-7b-wechsel-yo | llama-lang-adapt | 2023-11-08T17:17:32Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-11-08T15:54:58Z | Base model: https://huggingface.co/meta-llama/Llama-2-7b-hf
Embedding layer has been extended to account for added Yoruba vocabulary i.e. 32000 -> 46721.
Embeddings initialized with Wechsel strategy. |
crystal-technologies/CRYSTAL-R1 | crystal-technologies | 2023-11-08T17:16:58Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-10-25T20:18:30Z | Run crystal.py
Train LLM `pip install -e .` inside finetuning folder
Install Speaker Identification `pip install .` and `pip install -r requirements/requirements_lightning.txt requirements/requirements_asr.txt` |
Aioreus12/q-FrozenLake-v1-4x4-noSlippery | Aioreus12 | 2023-11-08T17:02:27Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T17:02:24Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="Aioreus12/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
nipet/q-FrozenLake-v1-4x4-noSlippery | nipet | 2023-11-08T17:00:08Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T17:00:03Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="nipet/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
hpandana/sb3ppo-LunarLander-v2 | hpandana | 2023-11-08T16:59:46Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T16:59:23Z | ---
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: 253.68 +/- 25.22
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
lmqg/mt5-base-frquad-qg-ae-trimmed-50000 | lmqg | 2023-11-08T16:46:15Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-08T10:11:37Z | # Vocabulary Trimmed [lmqg/mt5-base-frquad-qg-ae](https://huggingface.co/lmqg/mt5-base-frquad-qg-ae): `lmqg/mt5-base-frquad-qg-ae-trimmed-50000`
This model is a trimmed version of [lmqg/mt5-base-frquad-qg-ae](https://huggingface.co/lmqg/mt5-base-frquad-qg-ae) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-base-frquad-qg-ae | lmqg/mt5-base-frquad-qg-ae-trimmed-50000 |
|:---------------------------|:-----------------------------|:-------------------------------------------|
| parameter_size_full | 582,384,384 | 275,032,320 |
| parameter_size_embedding | 384,155,136 | 76,803,072 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 47.23 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| fr | vocabtrimmer/mc4_validation | text | fr | validation | 50000 | 2 | |
Nooney27/ppo-Huggy | Nooney27 | 2023-11-08T16:39:44Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-11-08T16:39:37Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Nooney27/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
IMSyPP/hate_speech_en | IMSyPP | 2023-11-08T16:32:03Z | 863 | 15 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2022-03-02T23:29:04Z | ---
widget:
- text: "My name is Mark and I live in London. I am a postgraduate student at Queen Mary University."
language:
- en
license: mit
---
# Hate Speech Classifier for Social Media Content in English Language
A monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model.
## Please cite:
Kralj Novak, P., Scantamburlo, T., Pelicon, A., Cinelli, M., Mozetič, I., & Zollo, F. (2022, July). __Handling disagreement in hate speech modelling__. In International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (pp. 681-695). Cham: Springer International Publishing.
https://link.springer.com/chapter/10.1007/978-3-031-08974-9_54
## Tokenizer
During training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference.
## Model output
The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent
Details on data acquisition and labeling including the Annotation guidelines:
http://imsypp.ijs.si/wp-content/uploads/2021/12/IMSyPP_D2.2_multilingual-dataset.pdf
|
isek-ai/SDPrompt-RetNet-300M | isek-ai | 2023-11-08T16:23:36Z | 18 | 14 | transformers | [
"transformers",
"pytorch",
"safetensors",
"retnet",
"text-generation",
"generated_from_trainer",
"custom_code",
"en",
"dataset:Gustavosta/Stable-Diffusion-Prompts",
"dataset:FredZhang7/anime-prompts-180K",
"license:mit",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-11-08T15:26:28Z | ---
tags:
- generated_from_trainer
- retnet
model-index:
- name: sdprompt-retnet-300m
results: []
license: mit
datasets:
- Gustavosta/Stable-Diffusion-Prompts
- FredZhang7/anime-prompts-180K
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
<!-- 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. -->
# SDPrompt-RetNet-300M
This model is a RetNet model trained from scratch using https://github.com/syncdoth/RetNet.
It achieves the following results on the evaluation set:
- Loss: 0.3616
## Usage
```
pip install transformers safetensors timm
```
```py
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
MODEL_NAME = "isek-ai/SDPrompt-RetNet-300M"
DEVICE = "cuda"
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
trust_remote_code=True,
).to(DEVICE)
streamer = TextStreamer(tokenizer)
prompt = "<s>1girl"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
_ = model.generate(
inputs["input_ids"],
max_new_tokens=256,
do_sample=True,
top_p=0.9,
top_k=20,
temperature=0.9,
streamer=streamer,
)
# <s> 1girl, absurdres, animal ear fluff, animal ears, bangs, bare shoulders, black hair, blue archive, blunt bangs, blush, closed mouth, collarbone, commentary request, eyes visible through hair, green eyes, hair between eyes, halo, hand on own face, hand up, highres, jacket, kisaki blue archive, long hair, long sleeves, looking at viewer, open clothes, open jacket, shinonome asu, simple background, solo, track jacket, upper body, white background, white jacket</s>
```
## Model description
This model is trained with Stable Diffusion prompts and Danbooru tags to generate prompts for image generation models.
## Training data
- [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts)
- [FredZhang7/anime-prompts-180K](https://huggingface.co/datasets/FredZhang7/anime-prompts-180K)
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0006
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.6714 | 0.03 | 1000 | 2.5787 |
| 2.1551 | 0.07 | 2000 | 2.3981 |
| 2.1439 | 0.1 | 3000 | 2.1160 |
| 1.8406 | 0.14 | 4000 | 1.9138 |
| 1.7485 | 0.17 | 5000 | 1.7847 |
| 1.6417 | 0.21 | 6000 | 1.7120 |
| 1.6084 | 0.24 | 7000 | 1.6055 |
| 1.4805 | 0.28 | 8000 | 1.5946 |
| 1.5524 | 0.31 | 9000 | 1.5027 |
| 1.4425 | 0.35 | 10000 | 1.4876 |
| 1.4007 | 0.38 | 11000 | 1.4364 |
| 1.4637 | 0.42 | 12000 | 1.3896 |
| 1.3211 | 0.45 | 13000 | 1.3968 |
| 1.3246 | 0.49 | 14000 | 1.3403 |
| 1.3461 | 0.52 | 15000 | 1.3156 |
| 1.2897 | 0.56 | 16000 | 1.2977 |
| 1.2748 | 0.59 | 17000 | 1.2823 |
| 1.2424 | 0.62 | 18000 | 1.2649 |
| 1.348 | 0.66 | 19000 | 1.2134 |
| 1.1797 | 0.69 | 20000 | 1.2030 |
| 1.2116 | 0.73 | 21000 | 1.2033 |
| 1.1702 | 0.76 | 22000 | 1.1453 |
| 1.1027 | 0.8 | 23000 | 1.1597 |
| 1.1932 | 0.83 | 24000 | 1.1506 |
| 1.3669 | 0.87 | 25000 | 1.1428 |
| 1.0705 | 0.9 | 26000 | 1.1239 |
| 1.1474 | 0.94 | 27000 | 1.1239 |
| 1.0879 | 0.97 | 28000 | 1.1168 |
| 0.9879 | 1.01 | 29000 | 1.0848 |
| 0.9928 | 1.04 | 30000 | 1.0953 |
| 0.9095 | 1.08 | 31000 | 1.1043 |
| 1.0423 | 1.11 | 32000 | 1.0823 |
| 0.9478 | 1.15 | 33000 | 1.0840 |
| 0.9979 | 1.18 | 34000 | 1.0387 |
| 1.0316 | 1.22 | 35000 | 1.0282 |
| 1.0531 | 1.25 | 36000 | 1.0369 |
| 0.919 | 1.28 | 37000 | 1.0398 |
| 1.0596 | 1.32 | 38000 | 1.0410 |
| 0.9076 | 1.35 | 39000 | 0.9889 |
| 0.9698 | 1.39 | 40000 | 1.0004 |
| 0.9633 | 1.42 | 41000 | 1.0038 |
| 0.9622 | 1.46 | 42000 | 0.9933 |
| 0.9809 | 1.49 | 43000 | 0.9805 |
| 0.9496 | 1.53 | 44000 | 0.9755 |
| 0.9435 | 1.56 | 45000 | 0.9759 |
| 0.9337 | 1.6 | 46000 | 0.9615 |
| 0.8844 | 1.63 | 47000 | 0.9524 |
| 0.9039 | 1.67 | 48000 | 0.9567 |
| 0.905 | 1.7 | 49000 | 0.9430 |
| 0.9491 | 1.74 | 50000 | 0.9205 |
| 0.8464 | 1.77 | 51000 | 0.9109 |
| 0.9384 | 1.81 | 52000 | 0.9056 |
| 0.8121 | 1.84 | 53000 | 0.8969 |
| 0.8381 | 1.88 | 54000 | 0.8869 |
| 0.8171 | 1.91 | 55000 | 0.8946 |
| 0.9024 | 1.94 | 56000 | 0.8993 |
| 0.84 | 1.98 | 57000 | 0.9011 |
| 0.6702 | 2.01 | 58000 | 0.8876 |
| 0.6278 | 2.05 | 59000 | 0.8716 |
| 0.6876 | 2.08 | 60000 | 0.8546 |
| 0.6754 | 2.12 | 61000 | 0.8639 |
| 0.6479 | 2.15 | 62000 | 0.8425 |
| 0.698 | 2.19 | 63000 | 0.8533 |
| 0.708 | 2.22 | 64000 | 0.8407 |
| 0.7021 | 2.26 | 65000 | 0.8160 |
| 0.5881 | 2.29 | 66000 | 0.8251 |
| 0.6181 | 2.33 | 67000 | 0.8205 |
| 0.6789 | 2.36 | 68000 | 0.8066 |
| 0.6452 | 2.4 | 69000 | 0.8037 |
| 0.6483 | 2.43 | 70000 | 0.7915 |
| 0.5868 | 2.47 | 71000 | 0.7864 |
| 0.6257 | 2.5 | 72000 | 0.7895 |
| 0.6593 | 2.53 | 73000 | 0.7718 |
| 0.5957 | 2.57 | 74000 | 0.7490 |
| 0.6351 | 2.6 | 75000 | 0.7481 |
| 0.699 | 2.64 | 76000 | 0.7628 |
| 0.566 | 2.67 | 77000 | 0.7590 |
| 0.5892 | 2.71 | 78000 | 0.7628 |
| 0.6052 | 2.74 | 79000 | 0.7633 |
| 0.6494 | 2.78 | 80000 | 0.7588 |
| 0.5917 | 2.81 | 81000 | 0.7118 |
| 0.508 | 2.85 | 82000 | 0.6857 |
| 0.523 | 2.88 | 83000 | 0.6738 |
| 0.4894 | 2.92 | 84000 | 0.6713 |
| 0.5096 | 2.95 | 85000 | 0.6625 |
| 0.352 | 2.99 | 86000 | 0.6802 |
| 0.3927 | 3.02 | 87000 | 0.6606 |
| 0.3468 | 3.06 | 88000 | 0.6546 |
| 0.3368 | 3.09 | 89000 | 0.6520 |
| 0.352 | 3.12 | 90000 | 0.6495 |
| 0.3613 | 3.16 | 91000 | 0.6324 |
| 0.3501 | 3.19 | 92000 | 0.6227 |
| 0.3269 | 3.23 | 93000 | 0.6091 |
| 0.3583 | 3.26 | 94000 | 0.6153 |
| 0.3278 | 3.3 | 95000 | 0.6178 |
| 0.3216 | 3.33 | 96000 | 0.6208 |
| 0.3383 | 3.37 | 97000 | 0.6195 |
| 0.3326 | 3.4 | 98000 | 0.6088 |
| 0.3081 | 3.44 | 99000 | 0.5956 |
| 0.3459 | 3.47 | 100000 | 0.5840 |
| 0.3139 | 3.51 | 101000 | 0.5712 |
| 0.3087 | 3.54 | 102000 | 0.5677 |
| 0.2798 | 3.58 | 103000 | 0.5566 |
| 0.3166 | 3.61 | 104000 | 0.5332 |
| 0.2981 | 3.65 | 105000 | 0.5333 |
| 0.3027 | 3.68 | 106000 | 0.5276 |
| 0.2815 | 3.72 | 107000 | 0.5024 |
| 0.2294 | 3.75 | 108000 | 0.5081 |
| 0.2452 | 3.78 | 109000 | 0.4824 |
| 0.2733 | 3.82 | 110000 | 0.4695 |
| 0.3001 | 3.85 | 111000 | 0.4627 |
| 0.2322 | 3.89 | 112000 | 0.4580 |
| 0.2362 | 3.92 | 113000 | 0.4402 |
| 0.2488 | 3.96 | 114000 | 0.4263 |
| 0.2449 | 3.99 | 115000 | 0.3999 |
| 0.1798 | 4.03 | 116000 | 0.4038 |
| 0.1956 | 4.06 | 117000 | 0.4037 |
| 0.1831 | 4.1 | 118000 | 0.4040 |
| 0.1802 | 4.13 | 119000 | 0.4039 |
| 0.1641 | 4.17 | 120000 | 0.4029 |
| 0.1769 | 4.2 | 121000 | 0.4016 |
| 0.1564 | 4.24 | 122000 | 0.4026 |
| 0.1552 | 4.27 | 123000 | 0.3988 |
| 0.1806 | 4.31 | 124000 | 0.3995 |
| 0.1783 | 4.34 | 125000 | 0.3995 |
| 0.1736 | 4.38 | 126000 | 0.3940 |
| 0.1657 | 4.41 | 127000 | 0.3913 |
| 0.1598 | 4.44 | 128000 | 0.3871 |
| 0.1599 | 4.48 | 129000 | 0.3831 |
| 0.1606 | 4.51 | 130000 | 0.3776 |
| 0.1639 | 4.55 | 131000 | 0.3754 |
| 0.1736 | 4.58 | 132000 | 0.3742 |
| 0.1653 | 4.62 | 133000 | 0.3703 |
| 0.1708 | 4.65 | 134000 | 0.3681 |
| 0.1729 | 4.69 | 135000 | 0.3674 |
| 0.1564 | 4.72 | 136000 | 0.3660 |
| 0.1734 | 4.76 | 137000 | 0.3641 |
| 0.163 | 4.79 | 138000 | 0.3632 |
| 0.1585 | 4.83 | 139000 | 0.3626 |
| 0.1603 | 4.86 | 140000 | 0.3619 |
| 0.1751 | 4.9 | 141000 | 0.3617 |
| 0.1622 | 4.93 | 142000 | 0.3617 |
| 0.161 | 4.97 | 143000 | 0.3617 |
| 0.1541 | 5.0 | 144000 | 0.3616 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0 |
blossominkyung/ppo-Huggy | blossominkyung | 2023-11-08T16:04:46Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-11-08T16:04:41Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: blossominkyung/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sandeeprao/Reinforce-polebalance | sandeeprao | 2023-11-08T15:53:27Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T15:53:18Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-polebalance
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
---
# **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
|
Vedarutvija/QnA | Vedarutvija | 2023-11-08T15:42:29Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-classification",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-08T15:19:15Z | ---
license: mit
pipeline_tag: text-classification
---
GPT2 model is fine tuned to train on the test set of the wiki_qa dataset for text classification. |
Praga-6000/unit4b | Praga-6000 | 2023-11-08T15:33:09Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-27T06:30:28Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: unit4b
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 58.70 +/- 39.86
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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
|
toddwilson147/ppo-LunarLander-v2 | toddwilson147 | 2023-11-08T15:26:21Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T15:19:45Z | ---
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: 264.86 +/- 18.85
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
LoneStriker/MistralLite-5.0bpw-h6-exl2 | LoneStriker | 2023-11-08T15:21:16Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-10-19T12:05:55Z | ---
license: apache-2.0
inference: false
---
# MistralLite Model
MistralLite is a fine-tuned [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) language model, with enhanced capabilities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to **perform significantly better on several long context retrieve and answering tasks**, while keeping the simple model structure of the original model. MistralLite is useful for applications such as long context line and topic retrieval, summarization, question-answering, and etc. MistralLite can be deployed on a single AWS `g5.2x` instance with Sagemaker [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) endpoint, making it suitable for applications that require high performance in resource-constrained environments. You can also serve the MistralLite model directly using TGI docker containers. Also, MistralLite supports other ways of serving like [vLLM](https://github.com/vllm-project/vllm), and you can use MistralLite in Python by using the [HuggingFace transformers](https://huggingface.co/docs/transformers/index) and [FlashAttention-2](https://github.com/Dao-AILab/flash-attention) library.
MistralLite is similar to [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), and their similarities and differences are summarized below:
|Model|Fine-tuned on long contexts| Max context length| RotaryEmbedding adaptation| Sliding Window Size|
|----------|-------------:|------------:|-----------:|-----------:|
| Mistral-7B-Instruct-v0.1 | up to 8K tokens | 32K | rope_theta = 10000 | 4096 |
| MistralLite | up to 16K tokens | 32K | **rope_theta = 1000000** | **16384** |
## Motivation of Developing MistralLite
Since the release of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), the model became increasingly popular because its strong performance
on a wide range of benchmarks. But most of the benchmarks are evaluated on `short context`, and not much has been investigated on its performance on long context tasks.
Then We evaluated `Mistral-7B-Instruct-v0.1` against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer context.
Although the performance of the models on long context was fairly competitive on long context less than 4096 tokens,
there were some limitations on its performance on longer context. Motivated by improving its performance on longer context, we finetuned the Mistral 7B model, and produced `Mistrallite`. The model managed to `significantly boost the performance of long context handling` over Mistral-7B-Instruct-v0.1. The detailed `long context evalutaion results` are as below:
1. [Topic Retrieval](https://lmsys.org/blog/2023-06-29-longchat/)
|Model Name|Input length| Input length | Input length| Input length| Input length|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|
| | 2851| 5568 |8313 | 11044 | 13780
| Mistral-7B-Instruct-v0.1 | 100% | 50% | 2% | 0% | 0% |
| MistralLite | **100%** | **100%** | **100%** | **100%** | **98%** |
2. [Line Retrieval](https://lmsys.org/blog/2023-06-29-longchat/#longeval-results)
|Model Name|Input length| Input length | Input length| Input length| Input length|Input length|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|-----------:|
| | 3818| 5661 |7505 | 9354 | 11188 | 12657
| Mistral-7B-Instruct-v0.1 | **98%** | 62% | 42% | 42% | 32% | 30% |
| MistralLite | **98%** | **92%** | **88%** | **76%** | **70%** | **60%** |
3. [Pass key Retrieval](https://github.com/epfml/landmark-attention/blob/main/llama/run_test.py#L101)
|Model Name|Input length| Input length | Input length| Input length|
|----------|-------------:|-------------:|------------:|-----------:|
| | 3264| 5396 |8329 | 10197 |
| Mistral-7B-Instruct-v0.1 | **100%** | 50% | 20% | 30% |
| MistralLite | **100%** | **100%** | **100%** | **100%** |
4. [Question Answering with Long Input Texts](https://nyu-mll.github.io/quality/)
|Model Name| Test set Accuracy | Hard subset Accuracy|
|----------|-------------:|-------------:|
| Mistral-7B-Instruct-v0.1 | 44.3% | 39.7% |
| MistralLite | **64.4%** | **56.2%** |
### Example test on long context of 13400 tokens
- Context is from [Amazon Aurora FAQs](https://aws.amazon.com/rds/aurora/faqs/)
- Question: *please tell me how does pgvector help with Generative AI and give me some examples.*
- Answer from MistralLite:
```code
pgvector is an open-source extension for PostgreSQL supported by Amazon Aurora PostgreSQL-Compatible Edition.
You can use pgvector to store, search, index, and query billions of embeddings that are generated from machine learning (ML) and artificial intelligence (AI) models in your database, such as those from Amazon Bedrock (limited preview) or Amazon SageMaker. A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video.
With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems for new types of applications such as personalized recommendations based on similar text descriptions or images, candidate match based on interview notes, customer service next best action recommendations based on successful transcripts or chat session dialogs, and more.
```
## Model Details
- **Developed by:** [AWS Contributors](https://github.com/orgs/aws-samples/teams/aws-prototype-ml-apac)
- **Model type:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Language:** English
- **Finetuned from weights:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Finetuned on data:**
- [SLidingEncoder and Decoder (SLED)](https://huggingface.co/datasets/tau/sled)
- [(Long) Natural Questions (NQ)](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections#multi-passage-qa-from-natural-questions)
- [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- **Supported Serving Framework:**
- [Text-Generation-Inference 1.1.0](https://github.com/huggingface/text-generation-inference/tree/v1.1.0)
- [vLLM](https://github.com/vllm-project/vllm)
- [HuggingFace transformers](https://huggingface.co/docs/transformers/index)
- [HuggingFace Text Generation Inference (TGI) container on SageMaker](https://github.com/awslabs/llm-hosting-container)
- **Model License:** Apache 2.0
- **Contact:** [GitHub issues](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/issues)
- **Inference Code** [Github Repo](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/)
## How to Use MistralLite from Python Code (HuggingFace transformers) ##
**Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/huggingface-transformers/example_usage.ipynb).
### Install the necessary packages
Requires: [transformers](https://pypi.org/project/transformers/) 4.34.0 or later, [flash-attn](https://pypi.org/project/flash-attn/) 2.3.1.post1 or later,
and [accelerate](https://pypi.org/project/accelerate/) 0.23.0 or later.
```shell
pip install transformers==4.34.0
pip install flash-attn==2.3.1.post1 --no-build-isolation
pip install accelerate==0.23.0
```
### You can then try the following example code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch
model_id = "amazon/MistralLite"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
torch_dtype=torch.bfloat16,
use_flash_attention_2=True,
device_map="auto",)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
sequences = pipeline(
prompt,
max_new_tokens=400,
do_sample=False,
return_full_text=False,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"{seq['generated_text']}")
```
**Important** - Use the prompt template below for MistralLite:
```
<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>
```
## How to Serve MistralLite on TGI ##
**Important:**
- For an end-to-end example Jupyter notebook using the native TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi/example_usage.ipynb).
- If the **input context length is greater than 12K tokens**, it is recommended using a custom TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi-custom/example_usage.ipynb).
### Start TGI server ###
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
docker run -d --gpus all --shm-size 1g -p 443:80 -v $(pwd)/models:/data ghcr.io/huggingface/text-generation-inference:1.1.0 \
--model-id amazon/MistralLite \
--max-input-length 16000 \
--max-total-tokens 16384 \
--max-batch-prefill-tokens 16384 \
--trust-remote-code
```
### Perform Inference ###
Example Python code for inference with TGI (requires `text_generation` 0.6.1 or later):
```shell
pip install text_generation==0.6.1
```
```python
from text_generation import Client
SERVER_PORT = 443
SERVER_HOST = "localhost"
SERVER_URL = f"{SERVER_HOST}:{SERVER_PORT}"
tgi_client = Client(f"http://{SERVER_URL}", timeout=60)
def invoke_tgi(prompt,
random_seed=1,
max_new_tokens=400,
print_stream=True,
assist_role=True):
if (assist_role):
prompt = f"<|prompter|>{prompt}</s><|assistant|>"
output = ""
for response in tgi_client.generate_stream(
prompt,
do_sample=False,
max_new_tokens=max_new_tokens,
return_full_text=False,
#temperature=None,
#truncate=None,
#seed=random_seed,
#typical_p=0.2,
):
if hasattr(response, "token"):
if not response.token.special:
snippet = response.token.text
output += snippet
if (print_stream):
print(snippet, end='', flush=True)
return output
prompt = "What are the main challenges to support a long context for LLM?"
result = invoke_tgi(prompt)
```
**Important** - When using MistralLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed.
## How to Deploy MistralLite on Amazon SageMaker ##
**Important:**
- For an end-to-end example Jupyter notebook using the SageMaker built-in container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi/example_usage.ipynb).
- If the **input context length is greater than 12K tokens**, it is recommended using a custom docker container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi-custom/example_usage.ipynb).
### Install the necessary packages
Requires: [sagemaker](https://pypi.org/project/sagemaker/) 2.192.1 or later.
```shell
pip install sagemaker==2.192.1
```
### Deploy the Model as A SageMaker Endpoint ###
To deploy MistralLite on a SageMaker endpoint, please follow the example code as below.
```python
import sagemaker
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
import time
sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()
image_uri = get_huggingface_llm_image_uri(
backend="huggingface", # or lmi
region=region,
version="1.1.0"
)
model_name = "MistralLite-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
hub = {
'HF_MODEL_ID':'amazon/MistralLite',
'HF_TASK':'text-generation',
'SM_NUM_GPUS':'1',
"MAX_INPUT_LENGTH": '16000',
"MAX_TOTAL_TOKENS": '16384',
"MAX_BATCH_PREFILL_TOKENS": '16384',
"MAX_BATCH_TOTAL_TOKENS": '16384',
}
model = HuggingFaceModel(
name=model_name,
env=hub,
role=role,
image_uri=image_uri
)
predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
endpoint_name=model_name,
)
```
### Perform Inference ###
To call the endpoint, please follow the example code as below:
```python
input_data = {
"inputs": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
"parameters": {
"do_sample": False,
"max_new_tokens": 400,
"return_full_text": False,
#"typical_p": 0.2,
#"temperature":None,
#"truncate":None,
#"seed": 1,
}
}
result = predictor.predict(input_data)[0]["generated_text"]
print(result)
```
or via [boto3](https://pypi.org/project/boto3/), and the example code is shown as below:
```python
import boto3
import json
def call_endpoint(client, prompt, endpoint_name, paramters):
client = boto3.client("sagemaker-runtime")
payload = {"inputs": prompt,
"parameters": parameters}
response = client.invoke_endpoint(EndpointName=endpoint_name,
Body=json.dumps(payload),
ContentType="application/json")
output = json.loads(response["Body"].read().decode())
result = output[0]["generated_text"]
return result
client = boto3.client("sagemaker-runtime")
parameters = {
"do_sample": False,
"max_new_tokens": 400,
"return_full_text": False,
#"typical_p": 0.2,
#"temperature":None,
#"truncate":None,
#"seed": 1,
}
endpoint_name = predictor.endpoint_name
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
result = call_endpoint(client, prompt, endpoint_name, parameters)
print(result)
```
## How to Serve MistralLite on vLLM ##
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
**Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/vllm/example_usage.ipynb).
### Using vLLM as a server ###
When using vLLM as a server, pass the --model amazon/MistralLite parameter, for example:
```shell
python3 -m vllm.entrypoints.api_server --model amazon/MistralLite
```
### Using vLLM in Python Code ###
When using vLLM from Python code, Please see the example code as below:
```python
from vllm import LLM, SamplingParams
prompts = [
"<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
]
sampling_params = SamplingParams(temperature=0, max_tokens=100)
llm = LLM(model="amazon/MistralLite",)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
## Limitations ##
Before using the MistralLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content. |
LoneStriker/MistralLite-6.0bpw-h6-exl2 | LoneStriker | 2023-11-08T15:21:04Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-10-19T12:12:33Z | ---
license: apache-2.0
inference: false
---
# MistralLite Model
MistralLite is a fine-tuned [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) language model, with enhanced capabilities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to **perform significantly better on several long context retrieve and answering tasks**, while keeping the simple model structure of the original model. MistralLite is useful for applications such as long context line and topic retrieval, summarization, question-answering, and etc. MistralLite can be deployed on a single AWS `g5.2x` instance with Sagemaker [Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) endpoint, making it suitable for applications that require high performance in resource-constrained environments. You can also serve the MistralLite model directly using TGI docker containers. Also, MistralLite supports other ways of serving like [vLLM](https://github.com/vllm-project/vllm), and you can use MistralLite in Python by using the [HuggingFace transformers](https://huggingface.co/docs/transformers/index) and [FlashAttention-2](https://github.com/Dao-AILab/flash-attention) library.
MistralLite is similar to [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), and their similarities and differences are summarized below:
|Model|Fine-tuned on long contexts| Max context length| RotaryEmbedding adaptation| Sliding Window Size|
|----------|-------------:|------------:|-----------:|-----------:|
| Mistral-7B-Instruct-v0.1 | up to 8K tokens | 32K | rope_theta = 10000 | 4096 |
| MistralLite | up to 16K tokens | 32K | **rope_theta = 1000000** | **16384** |
## Motivation of Developing MistralLite
Since the release of [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1), the model became increasingly popular because its strong performance
on a wide range of benchmarks. But most of the benchmarks are evaluated on `short context`, and not much has been investigated on its performance on long context tasks.
Then We evaluated `Mistral-7B-Instruct-v0.1` against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer context.
Although the performance of the models on long context was fairly competitive on long context less than 4096 tokens,
there were some limitations on its performance on longer context. Motivated by improving its performance on longer context, we finetuned the Mistral 7B model, and produced `Mistrallite`. The model managed to `significantly boost the performance of long context handling` over Mistral-7B-Instruct-v0.1. The detailed `long context evalutaion results` are as below:
1. [Topic Retrieval](https://lmsys.org/blog/2023-06-29-longchat/)
|Model Name|Input length| Input length | Input length| Input length| Input length|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|
| | 2851| 5568 |8313 | 11044 | 13780
| Mistral-7B-Instruct-v0.1 | 100% | 50% | 2% | 0% | 0% |
| MistralLite | **100%** | **100%** | **100%** | **100%** | **98%** |
2. [Line Retrieval](https://lmsys.org/blog/2023-06-29-longchat/#longeval-results)
|Model Name|Input length| Input length | Input length| Input length| Input length|Input length|
|----------|-------------:|-------------:|------------:|-----------:|-----------:|-----------:|
| | 3818| 5661 |7505 | 9354 | 11188 | 12657
| Mistral-7B-Instruct-v0.1 | **98%** | 62% | 42% | 42% | 32% | 30% |
| MistralLite | **98%** | **92%** | **88%** | **76%** | **70%** | **60%** |
3. [Pass key Retrieval](https://github.com/epfml/landmark-attention/blob/main/llama/run_test.py#L101)
|Model Name|Input length| Input length | Input length| Input length|
|----------|-------------:|-------------:|------------:|-----------:|
| | 3264| 5396 |8329 | 10197 |
| Mistral-7B-Instruct-v0.1 | **100%** | 50% | 20% | 30% |
| MistralLite | **100%** | **100%** | **100%** | **100%** |
4. [Question Answering with Long Input Texts](https://nyu-mll.github.io/quality/)
|Model Name| Test set Accuracy | Hard subset Accuracy|
|----------|-------------:|-------------:|
| Mistral-7B-Instruct-v0.1 | 44.3% | 39.7% |
| MistralLite | **64.4%** | **56.2%** |
### Example test on long context of 13400 tokens
- Context is from [Amazon Aurora FAQs](https://aws.amazon.com/rds/aurora/faqs/)
- Question: *please tell me how does pgvector help with Generative AI and give me some examples.*
- Answer from MistralLite:
```code
pgvector is an open-source extension for PostgreSQL supported by Amazon Aurora PostgreSQL-Compatible Edition.
You can use pgvector to store, search, index, and query billions of embeddings that are generated from machine learning (ML) and artificial intelligence (AI) models in your database, such as those from Amazon Bedrock (limited preview) or Amazon SageMaker. A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video.
With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems for new types of applications such as personalized recommendations based on similar text descriptions or images, candidate match based on interview notes, customer service next best action recommendations based on successful transcripts or chat session dialogs, and more.
```
## Model Details
- **Developed by:** [AWS Contributors](https://github.com/orgs/aws-samples/teams/aws-prototype-ml-apac)
- **Model type:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Language:** English
- **Finetuned from weights:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Finetuned on data:**
- [SLidingEncoder and Decoder (SLED)](https://huggingface.co/datasets/tau/sled)
- [(Long) Natural Questions (NQ)](https://huggingface.co/datasets/togethercomputer/Long-Data-Collections#multi-passage-qa-from-natural-questions)
- [OpenAssistant Conversations Dataset (OASST1)](https://huggingface.co/datasets/OpenAssistant/oasst1)
- **Supported Serving Framework:**
- [Text-Generation-Inference 1.1.0](https://github.com/huggingface/text-generation-inference/tree/v1.1.0)
- [vLLM](https://github.com/vllm-project/vllm)
- [HuggingFace transformers](https://huggingface.co/docs/transformers/index)
- [HuggingFace Text Generation Inference (TGI) container on SageMaker](https://github.com/awslabs/llm-hosting-container)
- **Model License:** Apache 2.0
- **Contact:** [GitHub issues](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/issues)
- **Inference Code** [Github Repo](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/)
## How to Use MistralLite from Python Code (HuggingFace transformers) ##
**Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/huggingface-transformers/example_usage.ipynb).
### Install the necessary packages
Requires: [transformers](https://pypi.org/project/transformers/) 4.34.0 or later, [flash-attn](https://pypi.org/project/flash-attn/) 2.3.1.post1 or later,
and [accelerate](https://pypi.org/project/accelerate/) 0.23.0 or later.
```shell
pip install transformers==4.34.0
pip install flash-attn==2.3.1.post1 --no-build-isolation
pip install accelerate==0.23.0
```
### You can then try the following example code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch
model_id = "amazon/MistralLite"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
torch_dtype=torch.bfloat16,
use_flash_attention_2=True,
device_map="auto",)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
sequences = pipeline(
prompt,
max_new_tokens=400,
do_sample=False,
return_full_text=False,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"{seq['generated_text']}")
```
**Important** - Use the prompt template below for MistralLite:
```
<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>
```
## How to Serve MistralLite on TGI ##
**Important:**
- For an end-to-end example Jupyter notebook using the native TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi/example_usage.ipynb).
- If the **input context length is greater than 12K tokens**, it is recommended using a custom TGI container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/tgi-custom/example_usage.ipynb).
### Start TGI server ###
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
docker run -d --gpus all --shm-size 1g -p 443:80 -v $(pwd)/models:/data ghcr.io/huggingface/text-generation-inference:1.1.0 \
--model-id amazon/MistralLite \
--max-input-length 16000 \
--max-total-tokens 16384 \
--max-batch-prefill-tokens 16384 \
--trust-remote-code
```
### Perform Inference ###
Example Python code for inference with TGI (requires `text_generation` 0.6.1 or later):
```shell
pip install text_generation==0.6.1
```
```python
from text_generation import Client
SERVER_PORT = 443
SERVER_HOST = "localhost"
SERVER_URL = f"{SERVER_HOST}:{SERVER_PORT}"
tgi_client = Client(f"http://{SERVER_URL}", timeout=60)
def invoke_tgi(prompt,
random_seed=1,
max_new_tokens=400,
print_stream=True,
assist_role=True):
if (assist_role):
prompt = f"<|prompter|>{prompt}</s><|assistant|>"
output = ""
for response in tgi_client.generate_stream(
prompt,
do_sample=False,
max_new_tokens=max_new_tokens,
return_full_text=False,
#temperature=None,
#truncate=None,
#seed=random_seed,
#typical_p=0.2,
):
if hasattr(response, "token"):
if not response.token.special:
snippet = response.token.text
output += snippet
if (print_stream):
print(snippet, end='', flush=True)
return output
prompt = "What are the main challenges to support a long context for LLM?"
result = invoke_tgi(prompt)
```
**Important** - When using MistralLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed.
## How to Deploy MistralLite on Amazon SageMaker ##
**Important:**
- For an end-to-end example Jupyter notebook using the SageMaker built-in container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi/example_usage.ipynb).
- If the **input context length is greater than 12K tokens**, it is recommended using a custom docker container, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/sagemaker-tgi-custom/example_usage.ipynb).
### Install the necessary packages
Requires: [sagemaker](https://pypi.org/project/sagemaker/) 2.192.1 or later.
```shell
pip install sagemaker==2.192.1
```
### Deploy the Model as A SageMaker Endpoint ###
To deploy MistralLite on a SageMaker endpoint, please follow the example code as below.
```python
import sagemaker
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
import time
sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()
image_uri = get_huggingface_llm_image_uri(
backend="huggingface", # or lmi
region=region,
version="1.1.0"
)
model_name = "MistralLite-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())
hub = {
'HF_MODEL_ID':'amazon/MistralLite',
'HF_TASK':'text-generation',
'SM_NUM_GPUS':'1',
"MAX_INPUT_LENGTH": '16000',
"MAX_TOTAL_TOKENS": '16384',
"MAX_BATCH_PREFILL_TOKENS": '16384',
"MAX_BATCH_TOTAL_TOKENS": '16384',
}
model = HuggingFaceModel(
name=model_name,
env=hub,
role=role,
image_uri=image_uri
)
predictor = model.deploy(
initial_instance_count=1,
instance_type="ml.g5.2xlarge",
endpoint_name=model_name,
)
```
### Perform Inference ###
To call the endpoint, please follow the example code as below:
```python
input_data = {
"inputs": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
"parameters": {
"do_sample": False,
"max_new_tokens": 400,
"return_full_text": False,
#"typical_p": 0.2,
#"temperature":None,
#"truncate":None,
#"seed": 1,
}
}
result = predictor.predict(input_data)[0]["generated_text"]
print(result)
```
or via [boto3](https://pypi.org/project/boto3/), and the example code is shown as below:
```python
import boto3
import json
def call_endpoint(client, prompt, endpoint_name, paramters):
client = boto3.client("sagemaker-runtime")
payload = {"inputs": prompt,
"parameters": parameters}
response = client.invoke_endpoint(EndpointName=endpoint_name,
Body=json.dumps(payload),
ContentType="application/json")
output = json.loads(response["Body"].read().decode())
result = output[0]["generated_text"]
return result
client = boto3.client("sagemaker-runtime")
parameters = {
"do_sample": False,
"max_new_tokens": 400,
"return_full_text": False,
#"typical_p": 0.2,
#"temperature":None,
#"truncate":None,
#"seed": 1,
}
endpoint_name = predictor.endpoint_name
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
result = call_endpoint(client, prompt, endpoint_name, parameters)
print(result)
```
## How to Serve MistralLite on vLLM ##
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
**Important** - For an end-to-end example Jupyter notebook, please refer to [this link](https://github.com/awslabs/extending-the-context-length-of-open-source-llms/blob/main/MistralLite/vllm/example_usage.ipynb).
### Using vLLM as a server ###
When using vLLM as a server, pass the --model amazon/MistralLite parameter, for example:
```shell
python3 -m vllm.entrypoints.api_server --model amazon/MistralLite
```
### Using vLLM in Python Code ###
When using vLLM from Python code, Please see the example code as below:
```python
from vllm import LLM, SamplingParams
prompts = [
"<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
]
sampling_params = SamplingParams(temperature=0, max_tokens=100)
llm = LLM(model="amazon/MistralLite",)
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
## Limitations ##
Before using the MistralLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content. |
Vedarutvija/output | Vedarutvija | 2023-11-08T15:18:24Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-classification",
"generated_from_trainer",
"dataset:wiki_qa",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-08T15:18:05Z | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
datasets:
- wiki_qa
model-index:
- name: output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the wiki_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8781
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9106 | 0.08 | 200 | 0.7699 |
| 0.9505 | 0.16 | 400 | 0.6965 |
| 0.8446 | 0.24 | 600 | 0.7000 |
| 0.8765 | 0.31 | 800 | 0.6573 |
| 0.7792 | 0.39 | 1000 | 0.7359 |
| 0.9293 | 0.47 | 1200 | 0.6926 |
| 0.9715 | 0.55 | 1400 | 0.7032 |
| 0.8898 | 0.63 | 1600 | 0.7208 |
| 1.0288 | 0.71 | 1800 | 0.6954 |
| 0.7782 | 0.79 | 2000 | 0.6629 |
| 0.9419 | 0.86 | 2200 | 0.7061 |
| 0.7138 | 0.94 | 2400 | 0.7086 |
| 0.9334 | 1.02 | 2600 | 0.6752 |
| 0.9274 | 1.1 | 2800 | 0.7142 |
| 0.7217 | 1.18 | 3000 | 0.7227 |
| 0.74 | 1.26 | 3200 | 0.6896 |
| 0.9408 | 1.34 | 3400 | 0.7039 |
| 0.8503 | 1.41 | 3600 | 0.7456 |
| 0.8816 | 1.49 | 3800 | 0.7226 |
| 0.7751 | 1.57 | 4000 | 0.7182 |
| 0.8669 | 1.65 | 4200 | 0.6904 |
| 1.059 | 1.73 | 4400 | 0.7131 |
| 0.8442 | 1.81 | 4600 | 0.7063 |
| 0.9162 | 1.89 | 4800 | 0.7128 |
| 0.9022 | 1.96 | 5000 | 0.7249 |
| 0.9427 | 2.04 | 5200 | 0.7333 |
| 0.9122 | 2.12 | 5400 | 0.6852 |
| 0.8159 | 2.2 | 5600 | 0.6950 |
| 0.9489 | 2.28 | 5800 | 0.7137 |
| 0.9976 | 2.36 | 6000 | 0.7101 |
| 0.9305 | 2.44 | 6200 | 0.7059 |
| 0.6405 | 2.51 | 6400 | 0.7167 |
| 0.9515 | 2.59 | 6600 | 0.6875 |
| 0.7186 | 2.67 | 6800 | 0.7057 |
| 0.9221 | 2.75 | 7000 | 0.6805 |
| 0.9118 | 2.83 | 7200 | 0.7011 |
| 0.9784 | 2.91 | 7400 | 0.6936 |
| 0.7532 | 2.99 | 7600 | 0.7046 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
AntoineD/camembert_causal_language_modeling_tools | AntoineD | 2023-11-08T15:16:03Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"text-generation",
"generated_from_trainer",
"base_model:almanach/camembert-base",
"base_model:finetune:almanach/camembert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-11-08T14:40:06Z | ---
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
model-index:
- name: camembert_causal_language_modeling_tools
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert_causal_language_modeling_tools
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8117
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 5 | 2.8891 |
| No log | 2.0 | 10 | 2.1063 |
| No log | 3.0 | 15 | 1.8117 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
blossominkyung/ppo-LunarLander-v2 | blossominkyung | 2023-11-08T15:09:04Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T15:06:20Z | ---
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: 253.50 +/- 25.62
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
KDenis/bert-base-banking77-pt2 | KDenis | 2023-11-08T15:01:04Z | 0 | 0 | keras | [
"keras",
"ru",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-08T14:17:36Z | ---
license: apache-2.0
language:
- ru
library_name: keras
--- |
KoboldAI/llama2-tokenizer | KoboldAI | 2023-11-08T15:00:47Z | 0 | 2 | null | [
"license:llama2",
"region:us"
]
| null | 2023-11-08T14:56:08Z | ---
license: llama2
---
This is a copy of the llama2 tokenizer for use as a fallback tokenizer for KoboldAI, optimized with defaults for text completion.
We aim to keep this copy functional / identical to the upstream llama2 tokenizer with minor differences in its defaults.
In case of differences a more functional copy is chosen. |
Abhinandpv/dog | Abhinandpv | 2023-11-08T15:00:38Z | 15 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-08T14:55:22Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### DOG Dreambooth model trained by Abhinandpv following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: LBS-575
Sample pictures of this concept:
|
DarkyMan/un-captcha | DarkyMan | 2023-11-08T14:52:59Z | 6 | 2 | tf-keras | [
"tf-keras",
"ocr",
"computer vision",
"object detection",
"image-to-text",
"license:cc0-1.0",
"region:us"
]
| image-to-text | 2023-06-22T06:52:40Z | ---
tags:
- ocr
- computer vision
- object detection
- image-to-text
license:
- cc0-1.0
---
|
microsoft/speecht5_tts | microsoft | 2023-11-08T14:37:23Z | 139,858 | 728 | transformers | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"audio",
"text-to-speech",
"dataset:libritts",
"arxiv:2110.07205",
"arxiv:1910.09700",
"license:mit",
"endpoints_compatible",
"region:us"
]
| text-to-speech | 2023-02-02T12:56:54Z | ---
license: mit
tags:
- audio
- text-to-speech
datasets:
- libritts
---
# SpeechT5 (TTS task)
SpeechT5 model fine-tuned for speech synthesis (text-to-speech) on LibriTTS.
This model was introduced in [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
SpeechT5 was first released in [this repository](https://github.com/microsoft/SpeechT5/), [original weights](https://huggingface.co/mechanicalsea/speecht5-tts). The license used is [MIT](https://github.com/microsoft/SpeechT5/blob/main/LICENSE).
## Model Description
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning. The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets. After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder.
Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text. To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder.
Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification.
- **Developed by:** Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
- **Shared by [optional]:** [Matthijs Hollemans](https://huggingface.co/Matthijs)
- **Model type:** text-to-speech
- **Language(s) (NLP):** [More Information Needed]
- **License:** [MIT](https://github.com/microsoft/SpeechT5/blob/main/LICENSE)
- **Finetuned from model [optional]:** [More Information Needed]
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/microsoft/SpeechT5/]
- **Paper:** [https://arxiv.org/pdf/2110.07205.pdf]
- **Blog Post:** [https://huggingface.co/blog/speecht5]
- **Demo:** [https://huggingface.co/spaces/Matthijs/speecht5-tts-demo]
# 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. -->
## 🤗 Transformers Usage
You can run SpeechT5 TTS locally with the 🤗 Transformers library.
1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers), sentencepiece, soundfile and datasets(optional):
```
pip install --upgrade pip
pip install --upgrade transformers sentencepiece datasets[audio]
```
2. Run inference via the `Text-to-Speech` (TTS) pipeline. You can access the SpeechT5 model via the TTS pipeline in just a few lines of code!
```python
from transformers import pipeline
from datasets import load_dataset
import soundfile as sf
synthesiser = pipeline("text-to-speech", "microsoft/speecht5_tts")
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embedding = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
# You can replace this embedding with your own as well.
speech = synthesiser("Hello, my dog is cooler than you!", forward_params={"speaker_embeddings": speaker_embedding})
sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
```
3. Run inference via the Transformers modelling code - You can use the processor + generate code to convert text into a mono 16 kHz speech waveform for more fine-grained control.
```python
from transformers import SpeechT5Processor, SpeechT5ForTextToSpeech, SpeechT5HifiGan
from datasets import load_dataset
import torch
import soundfile as sf
from datasets import load_dataset
processor = SpeechT5Processor.from_pretrained("microsoft/speecht5_tts")
model = SpeechT5ForTextToSpeech.from_pretrained("microsoft/speecht5_tts")
vocoder = SpeechT5HifiGan.from_pretrained("microsoft/speecht5_hifigan")
inputs = processor(text="Hello, my dog is cute.", return_tensors="pt")
# load xvector containing speaker's voice characteristics from a dataset
embeddings_dataset = load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")
speaker_embeddings = torch.tensor(embeddings_dataset[7306]["xvector"]).unsqueeze(0)
speech = model.generate_speech(inputs["input_ids"], speaker_embeddings, vocoder=vocoder)
sf.write("speech.wav", speech.numpy(), samplerate=16000)
```
### Fine-tuning the Model
Refer to [this Colab notebook](https://colab.research.google.com/drive/1i7I5pzBcU3WDFarDnzweIj4-sVVoIUFJ) for an example of how to fine-tune SpeechT5 for TTS on a different dataset or a new language.
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
You can use this model for speech synthesis. See the [model hub](https://huggingface.co/models?search=speecht5) to look for fine-tuned versions on a task that interests you.
## 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.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
LibriTTS
## 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]
Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text.
### Training hyperparameters
- **Precision:** [More Information Needed] <!--fp16, bf16, fp8, fp32 -->
- **Regime:** [More Information Needed] <!--mixed precision or not -->
### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
### Summary
# Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification.
# 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
The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets.
After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder.
## 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:**
```bibtex
@inproceedings{ao-etal-2022-speecht5,
title = {{S}peech{T}5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing},
author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu},
booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {May},
year = {2022},
pages={5723--5738},
}
```
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
- **text-to-speech** to synthesize audio
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
Disclaimer: The team releasing SpeechT5 did not write a model card for this model so this model card has been written by the Hugging Face team.
# Model Card Contact
[More Information Needed]
|
CODR/dog | CODR | 2023-11-08T14:21:20Z | 0 | 0 | null | [
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-11-08T14:20:06Z | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Dog Dreambooth model trained by CODR following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: LBS-275
Sample pictures of this concept:
|
owanr/ghc-google-t5-v1_1-large-inter_model-dataset-frequency-human_annots_str | owanr | 2023-11-08T14:17:40Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:google/t5-v1_1-large",
"base_model:finetune:google/t5-v1_1-large",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-08T14:17:39Z | ---
license: apache-2.0
base_model: google/t5-v1_1-large
tags:
- generated_from_trainer
model-index:
- name: ghc-google-t5-v1_1-large-inter_model-dataset-frequency-human_annots_str
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ghc-google-t5-v1_1-large-inter_model-dataset-frequency-human_annots_str
This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4160
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5863 | 1.0 | 345 | 2.2862 |
| 1.9673 | 2.0 | 690 | 2.0705 |
| 1.7865 | 3.0 | 1035 | 1.8048 |
| 0.0714 | 4.0 | 1380 | 0.0459 |
| 0.0618 | 5.0 | 1725 | 0.0456 |
| 0.0596 | 6.0 | 2070 | 0.0476 |
| 0.0532 | 7.0 | 2415 | 0.0438 |
| 0.0503 | 8.0 | 2760 | 0.0405 |
| 0.048 | 9.0 | 3105 | 0.0377 |
| 0.0462 | 10.0 | 3450 | 0.0455 |
| 0.036 | 11.0 | 3795 | 0.0358 |
| 0.0447 | 12.0 | 4140 | 0.0355 |
| 0.0416 | 13.0 | 4485 | 0.0351 |
| 0.0413 | 14.0 | 4830 | 0.0331 |
| 0.0409 | 15.0 | 5175 | 0.0320 |
| 0.0411 | 16.0 | 5520 | 0.0333 |
| 0.0363 | 17.0 | 5865 | 0.0322 |
| 0.0378 | 18.0 | 6210 | 0.0329 |
| 0.0345 | 19.0 | 6555 | 0.0312 |
| 0.0328 | 20.0 | 6900 | 0.0311 |
| 0.0392 | 21.0 | 7245 | 0.0303 |
| 0.0392 | 22.0 | 7590 | 0.0296 |
| 0.0353 | 23.0 | 7935 | 0.0300 |
| 0.0331 | 24.0 | 8280 | 0.0299 |
| 0.0306 | 25.0 | 8625 | 0.0290 |
| 0.0313 | 26.0 | 8970 | 0.0294 |
| 0.0303 | 27.0 | 9315 | 0.0296 |
| 0.0378 | 28.0 | 9660 | 0.0292 |
| 0.0358 | 29.0 | 10005 | 0.0292 |
| 0.0328 | 30.0 | 10350 | 0.0292 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
just097/roberta-base-lora-comma-placement-r-8-alpha-32 | just097 | 2023-11-08T14:16:05Z | 3 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:FacebookAI/roberta-base",
"base_model:adapter:FacebookAI/roberta-base",
"region:us"
]
| null | 2023-11-08T14:16:03Z | ---
library_name: peft
base_model: roberta-base
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0
|
GabSo/santacoder-finetuned-robot3 | GabSo | 2023-11-08T14:05:31Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:bigcode/santacoder",
"base_model:finetune:bigcode/santacoder",
"license:bigcode-openrail-m",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-11-08T13:52:57Z | ---
license: bigcode-openrail-m
base_model: bigcode/santacoder
tags:
- generated_from_trainer
model-index:
- name: santacoder-finetuned-robot3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# santacoder-finetuned-robot3
This model is a fine-tuned version of [bigcode/santacoder](https://huggingface.co/bigcode/santacoder) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5689
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1
- training_steps: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.05 | 1 | 1.6250 |
| No log | 0.1 | 2 | 2.1620 |
| No log | 0.15 | 3 | 1.7060 |
| No log | 0.2 | 4 | 1.7167 |
| No log | 0.25 | 5 | 1.0462 |
| No log | 0.3 | 6 | 1.2153 |
| No log | 0.35 | 7 | 1.0301 |
| No log | 0.4 | 8 | 0.9399 |
| No log | 0.45 | 9 | 1.0030 |
| 1.4139 | 0.5 | 10 | 0.8322 |
| 1.4139 | 0.55 | 11 | 0.7111 |
| 1.4139 | 0.6 | 12 | 0.7151 |
| 1.4139 | 0.65 | 13 | 0.6482 |
| 1.4139 | 0.7 | 14 | 0.6228 |
| 1.4139 | 0.75 | 15 | 0.6105 |
| 1.4139 | 0.8 | 16 | 0.5827 |
| 1.4139 | 0.85 | 17 | 0.5791 |
| 1.4139 | 0.9 | 18 | 0.5726 |
| 1.4139 | 0.95 | 19 | 0.5696 |
| 0.5282 | 1.0 | 20 | 0.5689 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
AliMokh/q-FrozenLake-v1-4x4-noSlippery | AliMokh | 2023-11-08T13:55:37Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T13:55:35Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="AliMokh/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
lukasdrg/clinical_longformer_same_tokens_1epochs | lukasdrg | 2023-11-08T13:51:49Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"longformer",
"fill-mask",
"generated_from_trainer",
"base_model:lukasdrg/clinical_longformer_same_tokens_240k",
"base_model:finetune:lukasdrg/clinical_longformer_same_tokens_240k",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-11-08T12:44:18Z | ---
base_model: lukasdrg/clinical_longformer_same_tokens_240k
tags:
- generated_from_trainer
model-index:
- name: clinical_longformer_same_tokens_1epochs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clinical_longformer_same_tokens_1epochs
This model is a fine-tuned version of [lukasdrg/clinical_longformer_same_tokens_240k](https://huggingface.co/lukasdrg/clinical_longformer_same_tokens_240k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8395
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2181 | 0.49 | 65 | 1.8425 |
| 2.0426 | 0.97 | 130 | 1.8395 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
owanr/ghc-google-t5-v1_1-large-intra_model-frequency-model_annots_str_mse | owanr | 2023-11-08T13:50:14Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:google/t5-v1_1-large",
"base_model:finetune:google/t5-v1_1-large",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-08T13:50:12Z | ---
license: apache-2.0
base_model: google/t5-v1_1-large
tags:
- generated_from_trainer
model-index:
- name: ghc-google-t5-v1_1-large-intra_model-frequency-model_annots_str_mse
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ghc-google-t5-v1_1-large-intra_model-frequency-model_annots_str_mse
This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.6351
- Loss: nan
- Losses: [4, 5, 3, 5, 4, 2, 4, 4, 5, 5, 3, 4, 4, 2, 4, 9, 3, 5, 4, 3, 6, 5, 4, 5, 4, 6, 5, 6, 5, 4, 4, 2, 5, 4, 5, 4, 4, 3, 5, 5, 5, 4, 2, 6, 4, 5, 2, 4, 4, 3, 5, 4, 5, 4, 4, 4, 4, 4, 4, 4, 5, 4, 2, 3, 2, 5, 16, 4, 3, 6, 5, 4, 3, 4, 2, 3, 5, 4, 2, 5, 2, 4, 4, 5, 4, 5, 4, 2, 4, 6, 4, 5, 6, 5, 5, 5, 4, 4, 4, 6, 4, 4, 4, 7, 2, 3, 3, 2, 5, 6, 6, 5, 2, 5, 3, 4, 4, 3, 4, 5, 2, 4, 9, 3, 5, 3, 3, 2, 3, 5, 5, 6, 4, 2, 4, 3, 7, 3, 3, 3, 2, 3, 2, 4, 4, 6, 4, 4, 2, 4, 6, 4, 4, 5, 5, 5, 4, 2, 4, 5, 4, 4, 5, 4, 2, 5, 3, 2, 5, 6, 5, 4, 2, 4, 5, 3, 4, 2, 4, 4, 3, 4, 5, 3, 7, 5, 5, 5, 2, 3, 3, 5, 2, 3, 2, 3, 4, 3, 5, 4, 5, 3, 3, 4, 5, 2, 4, 4, 4, 2, 2, 29, 4, 3, 3, 5, 4, 3, 4, 3, 4, 5, 2, 4, 3, 5, 2, 6, 2, 5, 3, 3, 4, 5, 6, 5, 5, 4, 5, 3, 2, 2, 5, 4, 3, 2, 4, 4, 4, 4, 5, 5, 5, 3, 4, 4, 5, 4, 9, 3, 4, 4, 4, 1, 5, 13.5, 4, 5, 4, 4, 4, 4, 4, 5, 5, 9, 6, 2, 5, 4, 4, 5, 6, 5, 5, 3, 4, 3, 3, 3, 4, 2, 4, 2, 3, 3, 2, 2, 4, 2, 3, 1, 3, 2, 5, 3, 5, 4, 3, 4, 3, 2, 4, 4, 3, 2, 4, 4, 5, 3, 5, 4, 4, 3, 3, 4, 3, 3, 2, 5, 5, 2, 6, 5, 3, 4, 4, 4, 3, 2, 3, 3, 3, 3, 4, 2, 1, 4, 4, 4, 3, 4, 3, 4, 2, 3, 4, 3, 4, 7, 3, 4, 4, 3, 2, 2, 4, 3, 4, 3, 4, 2, 4, 3, 2, 2, 4, 1, 4, 1, 4, 3, 4, 5, 3, 3, 2, 4, 1, 5, 4, 4, 3, 2, 3, 2, 4, 3, 5, 2, 1, 2, 2, 4, 4, 3, 3, 3, 4, 3, 3, 3, 3, 3, 5, 4, 2, 2, 4, 2, 3, 3, 4, 3, 2, 4, 5, 4, 3, 4, 4, 1, 3, 3, 3, 4, 5, 4, 3, 2, 2, 2, 2, 4, 2, 2, 4, 3, 3, 4, 3, 9, 3, 3, 3, 4, 5, 3, 1, 3, 4, 1, 3, 2, 3, 2, 5, 2, 3, 2, 2, 3, 2, 2, 1, 3, 4, 4, 3, 3, 2, 3, 3, 3, 5, 1, 5, 5, 4, 9, 3, 4, 3, 4, 5, 3, 2, 3, 7, 3, 3, 2, 5, 3, 3, 5, 3, 3, 3, 3, 2, 2, 3, 1, 3, 2, 5, 4, 3, 4, 4, 2, 2, 3, 3, 2, 4, 4, 3, 4, 3, 2, 4, 4, 0, 3, 4, 2, 3, 3, 2, 5, 4, 2, 2, 4, 1, 2, 4, 2, 5, 3, 2, 2, 5, 3, 4, 9, 5, 2, 2, 5, 4, 2, 2, 4, 4, 5, 5, 2, 4, 3, 4, 4, 6, 5, 2, 2, 4, 4, 4, 3, 4, 4, 4, 3, 3, 3, 2, 4, 2, 4, 4, 4, 3, 3, 4, 4, 4, 4, 4, 4, 2, 5, 3, 4, 3, 4, 4, 5, 3, 2, 4, 4, 3, 3, 2, 2, 4, 3, 4, 4, 3, 4, 4, 3, 3, 3, 4, 4, 4, 2, 4, 3, 4, 2, 5, 2, 4, 2, 4, 4, 3, 2, 4, 5, 4, 4, 4, 5, 3, 4, 4, 2, 3, 4, 3, 3, 4, 5, 2, 4, 4, 4, 2, 4, 2, 3, 4, 4, 4, 5, 4, 2, 4, 4, 4, 3, 2, 4, 3, 2, 3, 4, 3, 4, 2, 12, 4, 2, 3, 4, 4, 4, 9, 3, 5, 2, 4, 2, 2, 4, 4, 4, 4, 4, 3, 4, 4, 2, 4, 4, 4, 2, 3, 3, 4, 4, 4, 3, 2, 5, 4, 2, 4, 5, 3, 4, 3, 2, 4, 4, 2, 4, 4, 9, 3, 3, 4, 2, 4, 2, 2, 2, 3, 2, 4, 2, 4, 9, 2, 4, 2, 2, 4, 3, 4, 4, 2, 3, 4, 6, 4, 2, 3, 2, 3, 3, 3, 3, 4, 2, 4, 2, 4, 3, 4, 4, 4, 4, 2, 3, 4, 4, 4, 4, 4, 5, 4, 2, 4, 5, 4, 2, 2, 3, 3, 3, 5, 5, 4, 3, 4, 4, 2, 2, 4, 4, 4, 2, 4, 4, 3, 4, 3, 6, 5, 3, 3, 4, 4, 5, 5, 4, 4, 4, 3, 4, 3, 2, 4, 5, 3, 6, 5, 9, 4, 4, 4, 2, 4, 6, 5, 3, 4, 4, 4, 4, 4, 4, 5, 3, 3, 5, 5, 4, 6, 4, 4, 5, 4, 3, 5, 9, 2, 5, 4, 3, 4, 2, 4, 4, 4, 4, 2, 5, 5, 4, 2, 2, 5, 5, 5, 5, 3, 5, 3, 3, 3, 2, 2, 4, 4, 9, 4, 9, 5, 4, 5, 4, 5, 4, 3, 4, 2, 5, 5, 5, 4, 4, 3, 2, 4, 4, 3, 2, 4, 5, 5, 4, 5, 4, 2, 2, 5, 4, 2, 4, 6, 4, 2, 5, 2, 3, 4, 2, 4, 4, 4, 2, 4, 4, 4, 4, 3, 3, 2, 3, 3, 5, 3, 29, 6, 4, 3, 4, 4, 4, 4, 9, 4, 4, 9, 5, 4, 5, 2, 5, 5, 6, 3, 3, 4, 2, 4, 2, 2, 3, 4, 5, 2, 2, 2, 4, 4, 4, 3, 5, 4, 3, 4, 4, 3, 4, 3, 3, 3, 2, 4, 2, 5, 2, 5, 6, 4, 4, 2, 3, 3, 3, 4, 2, 3, 4, 6, 4, 5, 4, 3, 3, 3, 4, 4, 2, 2, 4, 2, 4, 3, 4, 4, 5, 4, 4, 3, 4, 4, 4, 2, 4, 3, 2, 2, 3, 2, 3, 3, 4, 4, 4, 3, 3, 3, 2, 4, 4, 5, 7, 4, 6, 9, 3, 2, 5, 4, 4, 5, 2, 3, 3, 3, 4, 5, 2, 4, 5, 4, 4, 4, 6, 4, 3, 5, 3, 2, 4, 4, 3, 2, 5, 6, 6, 4, 5, 4, 4, 4, 3, 2, 2, 5, 5, 1, 2, 4, 3, 4, 2, 2, 4, 1, 5, 2, 5, 4, 2, 2, 3, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 4, 3, 4, 4, 4, 4, 2, 4, 0, 2, 2, 3, 2, 3, 4, 2, 4, 3, 4, 4, 4, 2, 4, 5, 3, 3, 2, 1, 2, 2, 4, 4, 3, 5, 2, 35, 2, 2, 4, 2, 2, 4, 2, 4, 2, 0, 4, 4, 3, 2, 3, 5, 3, 1, 2, 4, 3, 4, 3, 4, 4, 2, 3, 4, 4, 2, 4, 3, 2, 3, 3, 2, 4, 2, 3, 2, 4, 1, 2, 4, 5, 4, 3, 4, 2, 2, 3, 3, 4, 4, 4, 3, 2, 2, 2, 4, 0, 4, 4, 3, 4, 2, 2, 0, 4, 3, 4, 2, 3, 3, 1, 2, 3, 3, 2, 9, 4, 4, 2, 5, 4, 4, 4, 3, 1, 9, 2, 0, 2, 3, 5, 3, 5, 2, 2, 4, 2, 3, 3, 3, 3, 3, 2, 9, 4, 4, 4, 4, 3, 2, 3, 3, 3, 1, 2, 5, 24, 4, 4, 4, 2, 4, 3, 3, 2, 2, 2, 3, 2, 2, 4, 2, 2, 4, 2, 2, 3, 3, 2, 4, 3, 3, 1, 3, 2, 2, 3, 4, 2, 4, 3, 2, 4, 2, 3, 2, 3, 3, 2, 4, 2, 2, 2, 3, 1, 2, 1, 5, 4, 5, 4, 4, 2, 2, 3, 3, 3, 4, 1, 2, 3, 4, 4, 4, 2, 2, 4, 5, 3, 3, 4, 2, 4, 5, 3, 2, 3, 5, 2, 2, 2, 3, 2, 2, 3, 3, 3, 3, 4]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Train Loss | Validation Loss | Losses |
|:-------------:|:-----:|:----:|:----------:|:---------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 6.2864 | 1.0 | 345 | 8.2298 | 6.5479 | [3, 9.0, 9.9, 4, 5, 4, 2, 13.5, 13.5, 16.200000000000003, 9.9, 13.5, 14.4, 4, 8, 14.4, 15.3, 12.600000000000001, 8, 6, 8, 10.8, 10.8, 13.5, 4, 4, 4, 4, 4, 11.700000000000001, 5, 13.5, 3, 11.700000000000001, 14.4, 4, 4, 9.900000000000002, 10.8, 4, 12.600000000000001, 5, 13.5, 4, 13.5, 4, 4, 3, 14.4, 4, 13.5, 4, 14.4, 9.900000000000002, 9.9, 14.4, 3, 11.7, 9.900000000000002, 4, 4, 3, 10, 7, 4, 9.9, 4, 4, 13.5, 3, 5, 14.4, 5, 4, 3, 4, 13.5, 4, 16.200000000000003, 15.3, 6.3, 9.9, 4, 8, 14.4, 14.4, 3, 2, 5, 4, 4, 16.200000000000003, 13.5, 11.700000000000001, 13.5, 5, 13.5, 4, 13.5, 4, 13.5, 13.5, 14.4, 4, 9.9, 4, 4, 4, 2, 12.600000000000001, 15.3, 12.600000000000001, 9.900000000000002, 10.8, 10.8, 14.4, 4, 5, 4, 13.5, 15.3, 4, 4, 4, 9.900000000000002, 5, 13.5, 8, 4, 9, 14.4, 10.8, 4, 12.600000000000001, 2, 7, 14.4, 13.5, 9.9, 6, 9, 13.5, 15, 3, 8, 4, 10.8, 4, 4, 4, 5, 14.4, 7, 5, 12.600000000000001, 9, 14.4, 4, 14.4, 4, 9.0, 9, 14.4, 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| 5.331 | 2.0 | 690 | 11.4411 | 5.4505 | [9.900000000000002, 5, 13.5, 4, 12.600000000000001, 13.5, 11.700000000000001, 13.5, 13.5, 16.200000000000003, 13.5, 13.5, 14.4, 14.4, 13.5, 14.4, 15.3, 12.600000000000001, 14.4, 15.3, 15.3, 10.8, 14.4, 4, 14.4, 13.5, 14.4, 13.5, 14.4, 11.700000000000001, 12.600000000000001, 13.5, 9.900000000000002, 11.700000000000001, 4, 13.5, 13.5, 3, 10.8, 14.4, 5, 10.8, 13.5, 14.4, 13.5, 13.5, 13.5, 9.900000000000002, 14.4, 13.5, 13.5, 14.4, 14.4, 3, 4, 14.4, 9.900000000000002, 4, 9.900000000000002, 13.5, 14.4, 9.900000000000002, 13.5, 13.5, 4, 13.5, 13.5, 14.4, 13.5, 3, 12.600000000000001, 4, 5, 14.4, 9.900000000000002, 15.3, 13.5, 4, 16.200000000000003, 15.3, 3, 13.5, 13.5, 14.4, 14.4, 14.4, 9.900000000000002, 10.8, 10.8, 15.3, 12.600000000000001, 16.200000000000003, 13.5, 11.700000000000001, 13.5, 5, 13.5, 14.4, 13.5, 13.5, 13.5, 13.5, 14.4, 4, 13.5, 13.5, 13.5, 14.4, 2, 12.600000000000001, 15.3, 5, 9.900000000000002, 4, 7, 14.4, 14.4, 12.600000000000001, 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| 0.2588 | 20.0 | 6900 | 3.6125 | 0.2387 | [3, 5, 4, 4, 5, 4, 0, 3, 4, 4, 4, 4, 2, 4, 4, 4, 2, 5, 4, 6, 4, 2, 2, 4, 4, 4, 2, 4, 2, 0, 5, 4, 3, 6, 4, 4, 4, 3, 2, 4, 5, 2, 4, 2, 4, 3, 4, 3, 4, 4, 4, 2, 4, 3, 4, 2, 3, 2, 3, 4, 2, 3, 3, 4, 3, 4, 3, 2, 3, 3, 5, 4, 5, 4, 3, 4, 4, 4, 5, 4, 3, 4, 4, 4, 4, 2, 3, 2, 1, 2, 5, 5, 4, 6, 4, 5, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 2, 5, 4, 5, 3, 4, 6, 4, 2, 4, 4, 4, 4, 4, 3, 2, 3, 5, 4, 2, 4, 5, 2, 2, 4, 5, 1, 5, 4, 4, 4, 6, 3, 3, 5, 3, 4, 3, 2, 4, 3, 4, 4, 2, 5, 5, 5, 2, 2, 4, 2, 4, 5, 3, 4, 3, 5, 4, 4, 2, 2, 4, 5, 4, 4, 4, 2, 4, 3, 4, 6, 3, 5, 3, 3, 4, 5, 2, 4, 4, 3, 2, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 2, 3, 1, 2, 5, 2, 3, 5, 2, 5, 4, 4, 2, 3, 4, 4, 4, 2, 4, 4, 4, 3, 4, 1, 2, 4, 4, 3, 1, 3, 4, 2, 3, 2, 2, 4, 5, 2, 4, 5, 3, 4, 2, 4, 4, 4, 3, 4, 2, 2, 3, 5, 3, 4, 2, 3, 4, 4, 4, 2, 4, 2, 4, 3, 4, 5, 3, 2, 4, 4, 5, 1, 5, 2, 4, 2, 4, 2, 5, 3, 3, 2, 5, 4, 4, 5, 4, 6, 2, 3, 4, 3, 4, 3, 2, 3, 4, 3, 4, 2, 4, 4, 4, 3, 5, 5, 4, 4, 1, 5, 2, 5, 4, 2, 4, 5, 4, 4, 3, 4, 4, 4, 4, 4, 4, 4, 3, 2, 4, 5, 2, 3, 5, 2, 4, 4, 2, 3, 4, 5, 2, 3, 4, 2, 2, 2, 4, 1, 4, 3, 5, 3, 2, 3, 5, 3, 5, 3, 4, 4, 2, 6, 2, 4, 4, 5, 4, 2, 3, 5, 4, 2, 3, 5, 2, 4, 2, 4, 3, 4, 2, 6, 2, 3, 4, 5, 4, 4, 4, 3, 6, 2, 5, 4, 4, 5, 5, 3, 2, 5, 5, 5, 4, 5, 4, 5, 3, 4, 4, 2, 2, 1, 2, 4, 4, 4, 4, 4, 4, 4, 5, 5, 3, 2, 2, 4, 4, 3, 5, 4, 5, 4, 3, 4, 4, 4, 3, 3, 2, 3, 3, 2, 5, 4, 5, 4, 5, 4, 3, 2, 1, 4, 2, 4, 0, 3, 4, 5, 2, 4, 3, 3, 4, 2, 5, 5, 3, 2, 6, 4, 2, 2, 2, 5, 1, 4, 4, 4, 2, 4, 4, 4, 4, 4, 2, 4, 4, 4, 3, 4, 1, 5, 4, 4, 3, 3, 4, 5, 5, 4, 5, 4, 6, 5, 2, 2, 4, 3, 5, 4, 5, 6, 4, 6, 2, 4, 5, 4, 1, 4, 2, 4, 4, 2, 3, 5, 5, 3, 4, 5, 2, 6, 5, 3, 4, 2, 4, 3, 0, 4, 5, 4, 5, 4, 5, 3, 4, 2, 5, 4, 4, 5, 3, 2, 2, 4, 4, 5, 2, 4, 5, 4, 4, 2, 4, 4, 2, 3, 5, 5, 3, 4, 3, 4, 2, 4, 2, 4, 5, 5, 5, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 2, 2, 2, 4, 4, 4, 4, 4, 2, 5, 4, 5, 5, 3, 4, 4, 5, 2, 5, 5, 6, 4, 3, 4, 4, 5, 4, 6, 5, 2, 5, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 5, 2, 4, 4, 4, 5, 3, 4, 1, 4, 3, 5, 2, 2, 7, 4, 3, 3, 4, 2, 4, 2, 5, 4, 2, 4, 4, 3, 4, 5, 4, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 2, 4, 4, 5, 2, 2, 5, 4, 3, 2, 1, 4, 2, 4, 3, 3, 4, 2, 4, 4, 3, 3, 4, 2, 2, 3, 5, 4, 2, 4, 4, 5, 4, 5, 4, 4, 3, 3, 4, 5, 6, 4, 3, 5, 3, 4, 3, 4, 2, 3, 3, 2, 5, 4, 2, 4, 4, 5, 4, 3, 5, 4, 5, 4, 3, 3, 4, 2, 2, 5, 4, 5, 4, 2, 2, 5, 2, 2, 4, 3, 4, 3, 4, 4, 4, 5, 3, 4, 2, 3, 4, 4, 3, 3, 3, 2, 0, 4, 3, 2, 4, 4, 2, 4, 4, 5, 4, 2, 5, 4, 4, 3, 4, 5, 2, 4, 3, 2, 2, 5, 4, 4, 4, 4, 3, 0, 4, 4, 4, 4, 3, 4, 2, 5, 4, 4, 4, 4, 2, 4, 3, 6, 3, 6, 4, 4, 4, 4, 4, 3, 4, 5, 4, 4, 5, 4, 4, 2, 3, 2, 5, 2, 4, 4, 4, 2, 4, 5, 4, 4, 4, 4, 2, 3, 2, 4, 5, 2, 4, 4, 5, 5, 4, 4, 5, 4, 4, 6, 3, 4, 4, 2, 4, 5, 5, 2, 5, 4, 2, 2, 4, 3, 4, 4, 2, 3, 4, 4, 4, 2, 2, 4, 4, 4, 4, 4, 3, 4, 2, 4, 4, 4, 4, 4, 2, 4, 5, 4, 3, 4, 7, 4, 4, 4, 4, 3, 3, 4, 5, 2, 4, 4, 3, 4, 3, 3, 1, 3, 4, 4, 2, 4, 3, 4, 1, 4, 4, 3, 4, 5, 3, 0, 4, 4, 4, 3, 2, 4, 3, 3, 4, 5, 5, 5, 3, 2, 3, 4, 4, 4, 4, 5, 5, 4, 4, 2, 1, 4, 6, 4, 4, 4, 2, 4, 2, 3, 5, 3, 5, 4, 3, 0, 4, 4, 6, 4, 4, 3, 4, 4, 4, 2, 5, 4, 4, 6, 2, 4, 2, 6, 2, 4, 2, 4, 5, 4, 4, 5, 2, 4, 2, 4, 4, 4, 4, 5, 4, 4, 3, 4, 5, 2, 4, 5, 4, 5, 3, 5, 4, 4, 4, 4, 4, 2, 5, 5, 2, 4, 4, 7, 4, 0, 4, 4, 2, 4, 4, 2, 5, 5, 2, 4, 4, 4, 4, 4, 3, 3, 5, 3, 4, 5, 2, 2, 2, 2, 4, 2, 2, 4, 3, 5, 3, 2, 3, 4, 2, 2, 4, 4, 5, 3, 2, 2, 2, 3, 2, 5, 5, 4, 4, 4, 5, 4, 5, 4, 0, 5, 4, 4, 4, 4, 4, 6, 5, 4, 4, 2, 4, 2, 2, 2, 3, 4, 2, 2, 4, 5, 3, 4, 4, 4, 4, 4, 2, 3, 3, 4, 4, 2, 2, 3, 3, 3, 2, 4, 4, 4, 4, 3, 4, 5, 5, 4, 4, 5, 2, 4, 4, 2, 3, 4, 2, 4, 5, 4, 4, 3, 4, 4, 3, 4, 4, 3, 0, 4, 3, 5, 4, 2, 1, 2, 4, 4, 2, 4, 3, 4, 4, 4, 4, 4, 5, 4, 4, 2, 4, 2, 3, 3, 4, 4, 5, 4, 4, 4, 4, 5, 2, 2, 6, 2, 4, 5, 5, 6, 5, 3, 2, 3, 6, 4, 5, 4, 3, 4, 2, 2, 4, 2, 4, 4, 2, 2, 6, 4, 3, 4, 3, 3, 1, 4, 3, 4, 4, 2, 2, 5, 4, 4, 4, 4, 5, 5, 5, 3, 2, 4, 4, 5, 0, 2, 4, 3, 5, 4, 6, 4, 3, 2, 4, 5, 2, 4, 4, 3, 3, 4, 2, 2, 4, 4, 2, 4, 4, 4, 4, 4, 5, 3, 4, 4, 6, 5, 4, 4, 4, 3, 4, 3, 5, 5, 1, 2, 4, 4, 4, 4, 5, 4, 2, 5, 2, 4, 4, 4, 3, 2, 4, 3, 4, 4, 4, 4, 5, 4, 2, 6, 4, 4, 4, 4, 4, 2, 4, 4, 6, 4, 4, 4, 3, 2, 5, 4, 5, 5, 4, 4, 4, 4, 3, 4, 2, 5, 3, 4, 2, 4, 2, 4, 2, 4, 4, 4, 2, 5, 4, 4, 4, 4, 4, 4, 5, 2, 4, 5, 5, 4, 4, 3, 4, 3, 4, 5, 4, 4, 1, 2, 5] |
| 0.2311 | 21.0 | 7245 | 3.6125 | 0.2377 | [3, 5, 4, 4, 5, 4, 0, 3, 4, 4, 4, 4, 4, 4, 4, 4, 2, 5, 4, 6, 4, 2, 2, 4, 4, 4, 2, 4, 2, 0, 5, 4, 3, 6, 4, 4, 4, 3, 2, 4, 5, 2, 4, 2, 4, 3, 4, 3, 4, 4, 4, 2, 4, 3, 4, 2, 3, 2, 3, 4, 2, 3, 3, 4, 3, 4, 3, 2, 3, 3, 5, 4, 5, 4, 3, 4, 4, 4, 5, 4, 3, 4, 4, 4, 4, 2, 3, 2, 1, 2, 5, 5, 4, 6, 4, 5, 4, 2, 4, 4, 4, 4, 4, 4, 4, 4, 3, 4, 2, 5, 4, 5, 3, 4, 6, 4, 2, 4, 4, 4, 2, 4, 3, 2, 3, 5, 4, 2, 4, 5, 2, 2, 4, 5, 1, 5, 4, 4, 4, 6, 3, 3, 5, 3, 4, 3, 2, 4, 3, 4, 4, 2, 5, 5, 5, 2, 2, 4, 4, 4, 5, 3, 4, 3, 5, 4, 3, 2, 2, 4, 5, 4, 4, 3, 2, 4, 3, 4, 6, 3, 5, 3, 3, 4, 5, 2, 4, 4, 3, 2, 4, 4, 4, 5, 4, 4, 4, 4, 4, 4, 4, 2, 3, 1, 2, 5, 2, 3, 5, 2, 5, 4, 4, 2, 3, 4, 4, 4, 2, 4, 4, 4, 3, 4, 1, 2, 4, 4, 3, 1, 3, 4, 2, 3, 2, 2, 4, 5, 2, 4, 5, 3, 4, 2, 4, 4, 4, 3, 4, 2, 2, 3, 5, 3, 4, 2, 3, 4, 4, 4, 2, 4, 2, 4, 3, 4, 5, 3, 2, 4, 4, 5, 1, 5, 3, 4, 2, 4, 2, 5, 3, 3, 2, 5, 4, 4, 5, 3, 6, 2, 4, 4, 3, 4, 3, 2, 3, 4, 3, 4, 2, 4, 4, 4, 3, 5, 5, 4, 4, 1, 5, 2, 5, 4, 2, 4, 5, 4, 4, 3, 4, 4, 4, 4, 4, 2, 4, 3, 2, 4, 5, 2, 3, 5, 2, 4, 4, 2, 3, 4, 5, 2, 3, 4, 2, 2, 2, 4, 1, 4, 3, 5, 3, 2, 3, 5, 3, 5, 3, 4, 4, 2, 6, 2, 4, 4, 5, 4, 2, 3, 5, 4, 2, 3, 5, 2, 4, 2, 4, 3, 4, 2, 6, 2, 3, 4, 5, 4, 4, 4, 4, 6, 2, 5, 4, 5, 5, 5, 3, 2, 5, 5, 5, 4, 5, 4, 5, 3, 4, 4, 2, 2, 1, 2, 4, 4, 4, 4, 2, 4, 4, 5, 5, 3, 2, 2, 4, 3, 3, 5, 4, 5, 4, 3, 4, 4, 4, 3, 3, 2, 3, 3, 2, 5, 4, 5, 4, 5, 4, 3, 2, 1, 4, 2, 4, 0, 3, 4, 5, 2, 4, 3, 3, 4, 2, 5, 5, 3, 2, 6, 4, 2, 2, 2, 5, 1, 4, 4, 4, 2, 4, 4, 4, 4, 4, 2, 4, 4, 4, 3, 4, 1, 5, 3, 4, 3, 3, 4, 5, 5, 4, 5, 4, 6, 5, 2, 2, 4, 3, 5, 4, 5, 6, 4, 6, 2, 4, 5, 4, 1, 4, 2, 4, 4, 2, 3, 5, 5, 3, 4, 5, 2, 6, 5, 3, 4, 2, 4, 3, 0, 4, 5, 4, 5, 4, 5, 3, 4, 2, 5, 4, 4, 4, 3, 2, 2, 4, 4, 5, 2, 4, 5, 4, 4, 2, 4, 4, 2, 3, 5, 5, 3, 4, 3, 4, 2, 4, 2, 4, 5, 5, 5, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 2, 2, 2, 2, 4, 4, 4, 4, 2, 5, 4, 5, 5, 3, 4, 4, 5, 2, 5, 5, 6, 4, 3, 4, 4, 5, 4, 6, 5, 2, 5, 4, 4, 4, 4, 4, 6, 4, 4, 4, 4, 5, 2, 2, 4, 4, 5, 3, 4, 1, 4, 3, 5, 2, 2, 7, 4, 3, 3, 4, 4, 4, 4, 5, 4, 2, 4, 4, 3, 4, 5, 4, 5, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 2, 2, 5, 4, 3, 2, 1, 4, 2, 4, 3, 3, 4, 2, 4, 4, 3, 3, 4, 2, 2, 3, 5, 4, 2, 4, 4, 5, 4, 5, 4, 4, 3, 3, 4, 5, 6, 4, 3, 5, 3, 4, 3, 4, 2, 3, 3, 2, 5, 4, 2, 4, 4, 5, 4, 4, 5, 4, 5, 4, 3, 3, 4, 2, 2, 5, 4, 5, 4, 2, 2, 5, 2, 2, 4, 3, 4, 3, 4, 4, 4, 5, 3, 4, 2, 3, 4, 2, 3, 3, 3, 2, 0, 4, 3, 2, 4, 4, 2, 4, 4, 5, 4, 2, 5, 4, 4, 3, 4, 5, 2, 4, 3, 2, 2, 5, 4, 4, 4, 4, 3, 0, 4, 4, 4, 4, 3, 4, 2, 5, 4, 4, 4, 3, 2, 4, 3, 6, 3, 6, 4, 4, 4, 4, 4, 3, 4, 5, 4, 4, 5, 4, 4, 2, 3, 2, 5, 2, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 2, 3, 2, 4, 5, 2, 4, 4, 5, 5, 4, 4, 5, 4, 4, 6, 3, 4, 4, 2, 4, 5, 5, 2, 5, 4, 2, 2, 4, 3, 4, 3, 2, 3, 4, 4, 4, 2, 2, 4, 4, 4, 4, 4, 3, 4, 2, 4, 4, 4, 4, 4, 2, 4, 5, 4, 3, 4, 7, 4, 4, 4, 4, 3, 3, 4, 5, 2, 4, 4, 3, 4, 3, 3, 1, 3, 4, 4, 4, 4, 3, 4, 1, 4, 4, 3, 4, 5, 3, 0, 4, 4, 4, 3, 2, 4, 3, 3, 4, 5, 5, 5, 3, 2, 3, 4, 4, 4, 3, 5, 5, 4, 4, 2, 1, 4, 6, 4, 4, 4, 2, 4, 4, 3, 5, 3, 5, 4, 3, 0, 4, 4, 6, 4, 4, 3, 4, 4, 4, 2, 5, 4, 4, 6, 2, 4, 2, 6, 2, 4, 2, 4, 5, 4, 4, 5, 2, 4, 2, 4, 4, 4, 4, 5, 4, 4, 3, 4, 5, 2, 4, 5, 4, 5, 3, 5, 4, 4, 4, 4, 4, 2, 5, 5, 2, 4, 4, 7, 4, 0, 4, 4, 2, 4, 4, 2, 5, 5, 2, 4, 4, 4, 4, 4, 4, 3, 5, 3, 4, 5, 2, 2, 2, 2, 4, 2, 2, 4, 3, 5, 3, 2, 3, 4, 2, 2, 4, 4, 5, 3, 2, 2, 2, 3, 2, 5, 5, 4, 4, 4, 5, 4, 5, 4, 0, 5, 4, 4, 4, 4, 4, 6, 5, 4, 4, 2, 4, 2, 2, 2, 3, 4, 2, 2, 4, 5, 3, 4, 4, 4, 4, 4, 2, 3, 3, 4, 4, 2, 2, 3, 3, 3, 2, 4, 4, 4, 4, 3, 4, 5, 5, 4, 4, 5, 2, 4, 4, 2, 3, 4, 2, 4, 5, 4, 4, 3, 4, 4, 3, 4, 4, 3, 0, 4, 3, 5, 4, 2, 1, 2, 4, 4, 2, 4, 3, 4, 4, 4, 4, 4, 5, 4, 4, 2, 4, 2, 3, 3, 4, 4, 5, 4, 4, 4, 4, 5, 2, 2, 6, 2, 4, 5, 5, 6, 5, 3, 2, 3, 6, 4, 5, 4, 3, 4, 2, 2, 4, 4, 4, 4, 2, 2, 6, 4, 3, 4, 3, 3, 1, 4, 3, 4, 4, 2, 2, 5, 4, 4, 4, 4, 5, 5, 5, 3, 2, 4, 4, 5, 0, 2, 4, 3, 5, 4, 6, 4, 3, 2, 4, 5, 2, 3, 4, 3, 3, 4, 2, 2, 4, 4, 2, 4, 4, 4, 4, 4, 5, 3, 4, 4, 6, 5, 4, 4, 4, 3, 4, 3, 5, 5, 1, 2, 4, 4, 4, 4, 5, 4, 2, 5, 2, 4, 4, 4, 3, 2, 4, 3, 4, 4, 4, 4, 5, 4, 2, 6, 4, 4, 4, 4, 4, 2, 4, 4, 6, 4, 4, 4, 3, 2, 4, 4, 5, 5, 4, 4, 4, 4, 3, 4, 2, 5, 3, 4, 2, 4, 2, 4, 2, 4, 4, 4, 2, 5, 4, 4, 4, 3, 4, 4, 5, 2, 4, 5, 5, 4, 4, 3, 4, 3, 4, 5, 4, 4, 1, 2, 5] |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.6.1
- Tokenizers 0.14.1
|
botcon/LUKE_squad_finetuned_qa_tf32 | botcon | 2023-11-08T13:44:07Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"luke",
"question-answering",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-10-31T06:25:38Z | Source code attached with training optimization.
Trained on 3 epochs without PEFT (took about 2 hours). |
Anton-k/my_awesome_opus_books_model | Anton-k | 2023-11-08T13:44:03Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-08T13:36:53Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: en-ru
split: train
args: en-ru
metrics:
- name: Bleu
type: bleu
value: 0.0895
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2508
- Bleu: 0.0895
- Gen Len: 18.564
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 2.6604 | 1.0 | 875 | 2.2957 | 0.0676 | 18.5823 |
| 2.4471 | 2.0 | 1750 | 2.2508 | 0.0895 | 18.564 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
gh1407/llama_7B_finetuned | gh1407 | 2023-11-08T13:35:23Z | 0 | 0 | null | [
"safetensors",
"en",
"license:mit",
"region:us"
]
| null | 2023-11-08T13:27:32Z | ---
license: mit
language:
- en
--- |
AntoineD/camembert_classification_tools_qlora | AntoineD | 2023-11-08T13:25:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"generated_from_trainer",
"base_model:almanach/camembert-base",
"base_model:finetune:almanach/camembert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-07T14:14:50Z | ---
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: camembert_classification_tools_qlora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert_classification_tools_qlora
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7844
- Accuracy: 0.7
## 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: 24
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 2.0913 | 0.05 |
| No log | 2.0 | 10 | 2.1000 | 0.05 |
| No log | 3.0 | 15 | 2.0985 | 0.1 |
| No log | 4.0 | 20 | 2.0854 | 0.15 |
| No log | 5.0 | 25 | 2.0667 | 0.275 |
| No log | 6.0 | 30 | 2.0459 | 0.35 |
| No log | 7.0 | 35 | 2.0092 | 0.325 |
| No log | 8.0 | 40 | 1.9610 | 0.375 |
| No log | 9.0 | 45 | 1.9182 | 0.4 |
| No log | 10.0 | 50 | 1.8769 | 0.425 |
| No log | 11.0 | 55 | 1.8349 | 0.425 |
| No log | 12.0 | 60 | 1.7894 | 0.425 |
| No log | 13.0 | 65 | 1.7395 | 0.425 |
| No log | 14.0 | 70 | 1.6914 | 0.425 |
| No log | 15.0 | 75 | 1.6472 | 0.45 |
| No log | 16.0 | 80 | 1.6029 | 0.45 |
| No log | 17.0 | 85 | 1.5619 | 0.475 |
| No log | 18.0 | 90 | 1.5190 | 0.5 |
| No log | 19.0 | 95 | 1.4621 | 0.575 |
| No log | 20.0 | 100 | 1.4180 | 0.55 |
| No log | 21.0 | 105 | 1.3786 | 0.575 |
| No log | 22.0 | 110 | 1.3384 | 0.575 |
| No log | 23.0 | 115 | 1.2975 | 0.625 |
| No log | 24.0 | 120 | 1.2561 | 0.65 |
| No log | 25.0 | 125 | 1.2164 | 0.675 |
| No log | 26.0 | 130 | 1.1839 | 0.675 |
| No log | 27.0 | 135 | 1.1602 | 0.65 |
| No log | 28.0 | 140 | 1.1304 | 0.625 |
| No log | 29.0 | 145 | 1.1029 | 0.625 |
| No log | 30.0 | 150 | 1.0744 | 0.625 |
| No log | 31.0 | 155 | 1.0482 | 0.625 |
| No log | 32.0 | 160 | 1.0197 | 0.675 |
| No log | 33.0 | 165 | 0.9967 | 0.725 |
| No log | 34.0 | 170 | 0.9793 | 0.725 |
| No log | 35.0 | 175 | 0.9640 | 0.725 |
| No log | 36.0 | 180 | 0.9502 | 0.675 |
| No log | 37.0 | 185 | 0.9390 | 0.65 |
| No log | 38.0 | 190 | 0.9183 | 0.7 |
| No log | 39.0 | 195 | 0.8987 | 0.725 |
| No log | 40.0 | 200 | 0.8817 | 0.775 |
| No log | 41.0 | 205 | 0.8684 | 0.725 |
| No log | 42.0 | 210 | 0.8611 | 0.7 |
| No log | 43.0 | 215 | 0.8607 | 0.7 |
| No log | 44.0 | 220 | 0.8592 | 0.7 |
| No log | 45.0 | 225 | 0.8471 | 0.725 |
| No log | 46.0 | 230 | 0.8306 | 0.725 |
| No log | 47.0 | 235 | 0.8189 | 0.75 |
| No log | 48.0 | 240 | 0.8136 | 0.725 |
| No log | 49.0 | 245 | 0.8142 | 0.7 |
| No log | 50.0 | 250 | 0.8092 | 0.7 |
| No log | 51.0 | 255 | 0.8053 | 0.75 |
| No log | 52.0 | 260 | 0.7995 | 0.75 |
| No log | 53.0 | 265 | 0.7917 | 0.75 |
| No log | 54.0 | 270 | 0.7901 | 0.725 |
| No log | 55.0 | 275 | 0.7910 | 0.7 |
| No log | 56.0 | 280 | 0.7904 | 0.7 |
| No log | 57.0 | 285 | 0.7884 | 0.7 |
| No log | 58.0 | 290 | 0.7863 | 0.7 |
| No log | 59.0 | 295 | 0.7851 | 0.7 |
| No log | 60.0 | 300 | 0.7844 | 0.7 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
minhnb/ssbc_model_spearman_6_labels | minhnb | 2023-11-08T13:23:49Z | 4 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-05T07:38:59Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: minhnb/ssbc_model_spearman_6_labels
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# minhnb/ssbc_model_spearman_6_labels
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5172
- Validation Loss: 0.8063
- Train Spearmanr: 0.7720
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2480, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Spearmanr | Epoch |
|:----------:|:---------------:|:---------------:|:-----:|
| 1.0413 | 0.8895 | 0.7040 | 0 |
| 0.7772 | 0.7950 | 0.7667 | 1 |
| 0.6662 | 0.7876 | 0.7737 | 2 |
| 0.5827 | 0.8054 | 0.7721 | 3 |
| 0.5172 | 0.8063 | 0.7720 | 4 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
alfredowh/Reinforce-CartPole-v1 | alfredowh | 2023-11-08T13:20:28Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T13:20:18Z | ---
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
---
# **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
|
Daniil-plotnikov/deepvision-5-0 | Daniil-plotnikov | 2023-11-08T13:11:52Z | 6 | 2 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-11-06T18:26:59Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### DeepVision-5.0
Good base model! More realistic!
|
alperengozeten/distilbert-turkish-emotion | alperengozeten | 2023-11-08T12:51:52Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:dbmdz/distilbert-base-turkish-cased",
"base_model:finetune:dbmdz/distilbert-base-turkish-cased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-08T12:51:36Z | ---
license: mit
base_model: dbmdz/distilbert-base-turkish-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [dbmdz/distilbert-base-turkish-cased](https://huggingface.co/dbmdz/distilbert-base-turkish-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1938
- Accuracy: 0.9592
## 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: 16
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9211 | 0.07 | 100 | 1.8456 | 0.3225 |
| 1.6177 | 0.14 | 200 | 1.2261 | 0.5982 |
| 0.8794 | 0.21 | 300 | 0.4865 | 0.8638 |
| 0.428 | 0.27 | 400 | 0.3350 | 0.8991 |
| 0.3189 | 0.34 | 500 | 0.2934 | 0.9128 |
| 0.2869 | 0.41 | 600 | 0.2728 | 0.9219 |
| 0.2776 | 0.48 | 700 | 0.2529 | 0.9267 |
| 0.2334 | 0.55 | 800 | 0.2609 | 0.9303 |
| 0.2314 | 0.62 | 900 | 0.2157 | 0.9369 |
| 0.2381 | 0.69 | 1000 | 0.1924 | 0.9431 |
| 0.2574 | 0.75 | 1100 | 0.2476 | 0.9260 |
| 0.2068 | 0.82 | 1200 | 0.1919 | 0.9429 |
| 0.241 | 0.89 | 1300 | 0.1865 | 0.9417 |
| 0.1894 | 0.96 | 1400 | 0.2022 | 0.9453 |
| 0.1791 | 1.03 | 1500 | 0.2078 | 0.9448 |
| 0.1131 | 1.1 | 1600 | 0.1995 | 0.9493 |
| 0.1082 | 1.17 | 1700 | 0.2074 | 0.9498 |
| 0.1088 | 1.23 | 1800 | 0.2139 | 0.9467 |
| 0.1123 | 1.3 | 1900 | 0.2086 | 0.9481 |
| 0.1083 | 1.37 | 2000 | 0.1964 | 0.9498 |
| 0.1318 | 1.44 | 2100 | 0.1872 | 0.9503 |
| 0.1016 | 1.51 | 2200 | 0.2005 | 0.9486 |
| 0.1415 | 1.58 | 2300 | 0.1918 | 0.9507 |
| 0.1292 | 1.64 | 2400 | 0.1848 | 0.9520 |
| 0.0939 | 1.71 | 2500 | 0.1870 | 0.9539 |
| 0.1301 | 1.78 | 2600 | 0.1950 | 0.9525 |
| 0.1415 | 1.85 | 2700 | 0.1955 | 0.9501 |
| 0.1474 | 1.92 | 2800 | 0.1797 | 0.9556 |
| 0.1169 | 1.99 | 2900 | 0.1767 | 0.9577 |
| 0.0562 | 2.06 | 3000 | 0.1847 | 0.9563 |
| 0.0653 | 2.12 | 3100 | 0.1839 | 0.9584 |
| 0.0431 | 2.19 | 3200 | 0.1853 | 0.9565 |
| 0.0289 | 2.26 | 3300 | 0.1922 | 0.9572 |
| 0.0507 | 2.33 | 3400 | 0.1989 | 0.9582 |
| 0.0475 | 2.4 | 3500 | 0.2009 | 0.9573 |
| 0.0434 | 2.47 | 3600 | 0.1959 | 0.9580 |
| 0.0479 | 2.54 | 3700 | 0.1942 | 0.9585 |
| 0.0421 | 2.6 | 3800 | 0.1986 | 0.9578 |
| 0.0496 | 2.67 | 3900 | 0.1947 | 0.9577 |
| 0.0452 | 2.74 | 4000 | 0.1938 | 0.9594 |
| 0.0329 | 2.81 | 4100 | 0.1936 | 0.9594 |
| 0.0568 | 2.88 | 4200 | 0.1934 | 0.9584 |
| 0.0441 | 2.95 | 4300 | 0.1938 | 0.9592 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
kwwww/bert-base-uncased-test_1_200 | kwwww | 2023-11-08T12:48:34Z | 0 | 0 | peft | [
"peft",
"pytorch",
"region:us"
]
| null | 2023-11-08T07:44:58Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
robkayinto/t5-large_PREFIX_TUNING_SEQ2SEQ | robkayinto | 2023-11-08T12:47:49Z | 1 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:google-t5/t5-large",
"base_model:adapter:google-t5/t5-large",
"region:us"
]
| null | 2023-11-08T12:12:29Z | ---
library_name: peft
base_model: t5-large
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0
|
EMaghakyan/fashion-clip | EMaghakyan | 2023-11-08T12:44:50Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"safetensors",
"clip",
"zero-shot-image-classification",
"vision",
"language",
"fashion",
"ecommerce",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
]
| zero-shot-image-classification | 2023-11-08T09:52:35Z | ---
license: mit
tags:
- vision
- language
- fashion
- ecommerce
library_name: transformers
language:
- en
widget:
- src: https://cdn-images.farfetch-contents.com/19/76/05/56/19760556_44221665_1000.jpg
candidate_labels: black shoe, red shoe, a cat
example_title: Black Shoe
---
[](https://www.youtube.com/watch?v=uqRSc-KSA1Y) [](https://huggingface.co/patrickjohncyh/fashion-clip) [](https://colab.research.google.com/drive/1Z1hAxBnWjF76bEi9KQ6CMBBEmI_FVDrW?usp=sharing) [](https://towardsdatascience.com/teaching-clip-some-fashion-3005ac3fdcc3) [](https://huggingface.co/spaces/vinid/fashion-clip-app)
# This is a fork of patrickjohncyh/fashion-clip
# Model Card: Fashion CLIP
Disclaimer: The model card adapts the model card from [here](https://huggingface.co/openai/clip-vit-base-patch32).
## Model Details
UPDATE (10/03/23): We have updated the model! We found that [laion/CLIP-ViT-B-32-laion2B-s34B-b79K](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K) checkpoint (thanks [Bin](https://www.linkedin.com/in/bin-duan-56205310/)!) worked better than original OpenAI CLIP on Fashion. We thus fine-tune a newer (and better!) version of FashionCLIP (henceforth FashionCLIP 2.0), while keeping the architecture the same. We postulate that the perofrmance gains afforded by `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` are due to the increased training data (5x OpenAI CLIP data). Our [thesis](https://www.nature.com/articles/s41598-022-23052-9), however, remains the same -- fine-tuning `laion/CLIP` on our fashion dataset improved zero-shot perofrmance across our benchmarks. See the below table comparing weighted macro F1 score across models.
| Model | FMNIST | KAGL | DEEP |
| ------------- | ------------- | ------------- | ------------- |
| OpenAI CLIP | 0.66 | 0.63 | 0.45 |
| FashionCLIP | 0.74 | 0.67 | 0.48 |
| Laion CLIP | 0.78 | 0.71 | 0.58 |
| FashionCLIP 2.0 | __0.83__ | __0.73__ | __0.62__ |
---
FashionCLIP is a CLIP-based model developed to produce general product representations for fashion concepts. Leveraging the pre-trained checkpoint (ViT-B/32) released by [OpenAI](https://github.com/openai/CLIP), we train FashionCLIP on a large, high-quality novel fashion dataset to study whether domain specific fine-tuning of CLIP-like models is sufficient to produce product representations that are zero-shot transferable to entirely new datasets and tasks. FashionCLIP was not developed for model deplyoment - to do so, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
### Model Date
March 2023
### Model Type
The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained, starting from a pre-trained checkpoint, to maximize the similarity of (image, text) pairs via a contrastive loss on a fashion dataset containing 800K products.
### Documents
- [FashionCLIP Github Repo](https://github.com/patrickjohncyh/fashion-clip)
- [FashionCLIP Paper](https://www.nature.com/articles/s41598-022-23052-9)
## Data
The model was trained on (image, text) pairs obtained from the Farfecth dataset[^1 Awaiting official release.], an English dataset comprising over 800K fashion products, with more than 3K brands across dozens of object types. The image used for encoding is the standard product image, which is a picture of the item over a white background, with no humans. The text used is a concatenation of the _highlight_ (e.g., “stripes”, “long sleeves”, “Armani”) and _short description_ (“80s styled t-shirt”)) available in the Farfetch dataset.
## Limitations, Bias and Fiarness
We acknowledge certain limitations of FashionCLIP and expect that it inherits certain limitations and biases present in the original CLIP model. We do not expect our fine-tuning to significantly augment these limitations: we acknowledge that the fashion data we use makes explicit assumptions about the notion of gender as in "blue shoes for a woman" that inevitably associate aspects of clothing with specific people.
Our investigations also suggest that the data used introduces certain limitations in FashionCLIP. From the textual modality, given that most captions derived from the Farfetch dataset are long, we observe that FashionCLIP may be more performant in longer queries than shorter ones. From the image modality, FashionCLIP is also biased towards standard product images (centered, white background).
Model selection, i.e. selecting an appropariate stopping critera during fine-tuning, remains an open challenge. We observed that using loss on an in-domain (i.e. same distribution as test) validation dataset is a poor selection critera when out-of-domain generalization (i.e. across different datasets) is desired, even when the dataset used is relatively diverse and large.
## Citation
```
@Article{Chia2022,
title="Contrastive language and vision learning of general fashion concepts",
author="Chia, Patrick John
and Attanasio, Giuseppe
and Bianchi, Federico
and Terragni, Silvia
and Magalh{\~a}es, Ana Rita
and Goncalves, Diogo
and Greco, Ciro
and Tagliabue, Jacopo",
journal="Scientific Reports",
year="2022",
month="Nov",
day="08",
volume="12",
number="1",
abstract="The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from general and transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model adapted for the fashion industry. We demonstrate the effectiveness of the representations learned by FashionCLIP with extensive tests across a variety of tasks, datasets and generalization probes. We argue that adaptations of large pre-trained models such as CLIP offer new perspectives in terms of scalability and sustainability for certain types of players in the industry. Finally, we detail the costs and environmental impact of training, and release the model weights and code as open source contribution to the community.",
issn="2045-2322",
doi="10.1038/s41598-022-23052-9",
url="https://doi.org/10.1038/s41598-022-23052-9"
}
``` |
nickapch/distilbert-base-uncased-finetuned-imdb | nickapch | 2023-11-08T12:43:25Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-08T11:16:45Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value:
accuracy: 0.93148
- name: F1
type: f1
value:
f1: 0.9314719475700824
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2799
- Accuracy: {'accuracy': 0.93148}
- F1: {'f1': 0.9314719475700824}
## 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------------------:|:--------------------------:|
| 0.2376 | 1.0 | 1563 | 0.2966 | {'accuracy': 0.8966} | {'f1': 0.8959598583205258} |
| 0.1671 | 2.0 | 3126 | 0.2331 | {'accuracy': 0.92996} | {'f1': 0.9299430382567873} |
| 0.0993 | 3.0 | 4689 | 0.2799 | {'accuracy': 0.93148} | {'f1': 0.9314719475700824} |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
TheBloke/LLaMA2-13B-TiefighterLR-GGUF | TheBloke | 2023-11-08T12:42:04Z | 261 | 5 | transformers | [
"transformers",
"gguf",
"llama",
"base_model:KoboldAI/LLaMA2-13B-TiefighterLR",
"base_model:quantized:KoboldAI/LLaMA2-13B-TiefighterLR",
"license:llama2",
"region:us"
]
| null | 2023-11-08T11:57:20Z | ---
base_model: KoboldAI/LLaMA2-13B-TiefighterLR
inference: false
license: llama2
model_creator: KoboldAI
model_name: Llama2 13B TiefighterLR
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
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# Llama2 13B TiefighterLR - GGUF
- Model creator: [KoboldAI](https://huggingface.co/KoboldAI)
- Original model: [Llama2 13B TiefighterLR](https://huggingface.co/KoboldAI/LLaMA2-13B-TiefighterLR)
<!-- description start -->
## Description
This repo contains GGUF format model files for [KoboldAI's Llama2 13B TiefighterLR](https://huggingface.co/KoboldAI/LLaMA2-13B-TiefighterLR).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF)
* [KoboldAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/KoboldAI/LLaMA2-13B-TiefighterLR)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [llama2-13b-tiefighterlr.Q2_K.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [llama2-13b-tiefighterlr.Q3_K_S.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [llama2-13b-tiefighterlr.Q3_K_M.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [llama2-13b-tiefighterlr.Q3_K_L.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [llama2-13b-tiefighterlr.Q4_0.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [llama2-13b-tiefighterlr.Q4_K_S.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [llama2-13b-tiefighterlr.Q4_K_M.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [llama2-13b-tiefighterlr.Q5_0.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [llama2-13b-tiefighterlr.Q5_K_S.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [llama2-13b-tiefighterlr.Q5_K_M.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [llama2-13b-tiefighterlr.Q6_K.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [llama2-13b-tiefighterlr.Q8_0.gguf](https://huggingface.co/TheBloke/LLaMA2-13B-TiefighterLR-GGUF/blob/main/llama2-13b-tiefighterlr.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/LLaMA2-13B-TiefighterLR-GGUF and below it, a specific filename to download, such as: llama2-13b-tiefighterlr.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/LLaMA2-13B-TiefighterLR-GGUF llama2-13b-tiefighterlr.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/LLaMA2-13B-TiefighterLR-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/LLaMA2-13B-TiefighterLR-GGUF llama2-13b-tiefighterlr.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m llama2-13b-tiefighterlr.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/LLaMA2-13B-TiefighterLR-GGUF", model_file="llama2-13b-tiefighterlr.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: KoboldAI's Llama2 13B TiefighterLR
# LLaMA2-13B-TiefighterLR
TiefighterLR is a merged model achieved trough merging two different lora's on top of a well established existing merge.
This LR version contains Less Rodeo, merged at 3% from the original 5% reducing its second person adventure bias.
Testers found this model to understand your own character and instruction prompts better, at the sacrifice of lowering its own writing bias/style.
To achieve this the following recipe was used:
* We begin with the base model Undi95/Xwin-MLewd-13B-V0.2 which is a well established merge, contrary to the name this model does not have a strong NSFW bias.
* Then we applied the PocketDoc/Dans-RetroRodeo-13b lora which is a finetune on the Choose your own Adventure datasets from our Skein model.
* After applying this lora we merged the original model with the newly created PocketDoc/Dans-RetroRodeo-13b merge at 3% to weaken the newly introduced adventure bias.
* The resulting merge was used as a new base model to which we applied Blackroot/Llama-2-13B-Storywriter-LORA and repeated the same trick, this time at 10%.
This means this model contains the following ingredients from their upstream models for as far as we can track them:
- Undi95/Xwin-MLewd-13B-V0.2
- - Undi95/ReMM-S-Light (base/private)
- Undi95/CreativeEngine
- Brouz/Slerpeno
- - elinas/chronos-13b-v2
- jondurbin/airoboros-l2-13b-2.1
- NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2 LORA
- CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2 LORA
- - KoboldAI/LLAMA2-13B-Holodeck-1
- NousResearch/Nous-Hermes-13b
- OpenAssistant/llama2-13b-orca-8k-3319
- ehartford/WizardLM-1.0-Uncensored-Llama2-13b
- Henk717/spring-dragon
- The-Face-Of-Goonery/Huginn-v3-13b
- zattio770/120-Days-of-LORA-v2-13B
- PygmalionAI/pygmalion-2-13b
- Undi95/StoryTelling
- TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter
- nRuaif/Kimiko-v2-13B
- The-Face-Of-Goonery/Huginn-13b-FP16
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Xwin-LM/Xwin-LM-13B-V0.2
- PocketDoc/Dans-RetroRodeo-13b
- Blackroot/Llama-2-13B-Storywriter-LORA
# Usage
This model is meant to be creative, If you let it improvise you get better results than if you drown it in details.
## Story Writing
Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story.
### Generate a story on demand
To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong.
## Chatbots and persona's
Unlike the original Tiefighter this model is better at handling existing Character Cards as long as they do not contain a lot of second person writing or second person introductions (You), setting > as a custom stop sequence can help fix potential mistakes, as well as turning multi-line replies off.
You can also use instructions to create your characters.
For example, you can put this in memory in regular chat mode:
```
### Instruction:
Generate a conversation between Alice and Henk where they discuss language models.
In this conversation Henk is excited to teach Alice about Tiefighter.
### Response:
```
Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format.
## Instruct Prompting
This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work.
During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias.
Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up.
## Adventuring and Adventure Games
This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode).
It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns.
## Discovered something cool and want to engage with us?
Join our community at https://koboldai.org/discord !
### This model would not be possible without the awesome work from:
Undi95, PocketDoc, Blackroot, Brouz, The Face of Goonery, zattio770, PygmalionAI, TokenBender, nRuaif, lemonilia and Xwin-LM.
<!-- original-model-card end -->
|
just097/roberta-base-lora-comma-placement | just097 | 2023-11-08T12:36:25Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:FacebookAI/roberta-base",
"base_model:adapter:FacebookAI/roberta-base",
"region:us"
]
| null | 2023-11-07T19:07:25Z | ---
library_name: peft
base_model: roberta-base
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0
|
1aurent/mobilenetv2_100.tiatoolbox-pcam | 1aurent | 2023-11-08T12:31:18Z | 16 | 0 | timm | [
"timm",
"safetensors",
"image-classification",
"feature-extraction",
"biology",
"cancer",
"histology",
"TIA",
"tiatoolbox",
"dataset:1aurent/PatchCamelyon",
"license:cc0-1.0",
"region:us"
]
| image-classification | 2023-11-08T12:28:13Z | ---
tags:
- image-classification
- feature-extraction
- timm
- biology
- cancer
- histology
- TIA
- tiatoolbox
library_name: timm
pipeline_tag: image-classification
license: cc0-1.0
datasets:
- 1aurent/PatchCamelyon
---
# Model card for mobilenetv2_100.tiatoolbox-pcam
A MobileNet-v2 image classification model. \
Trained by [Tissue Image Analytics (TIA) Centre](https://warwick.ac.uk/fac/cross_fac/tia/) on "pcam" histology patches.
## Model Details
- **Model Type:** Image classification / Feature backbone
- **Model Stats:**
- Params (M): 2.26
- Image size: 96 x 96 x 3
- **Dataset**: [Patch Camelyon (PCam)](https://github.com/basveeling/pcam/)
- **Original:** https://github.com/TissueImageAnalytics/tiatoolbox
- **License**: [Creative Commons Zero v1.0 Universal](https://creativecommons.org/publicdomain/zero/1.0/legalcode)
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import torch.nn as nn
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/mobilenetv2_100.tiatoolbox-pcam",
pretrained=True,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import torch.nn as nn
import timm
# get example histology image
img = Image.open(
urlopen(
"https://github.com/owkin/HistoSSLscaling/raw/main/assets/example.tif"
)
)
# load model from the hub
model = timm.create_model(
model_name="hf-hub:1aurent/mobilenetv2_100.tiatoolbox-pcam",
pretrained=True,
num_classes=0,
).eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
data = transforms(img).unsqueeze(0) # input is a (batch_size, num_channels, img_size, img_size) shaped tensor
output = model(data) # output is a (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{Pocock2022,
author = {Pocock, Johnathan and Graham, Simon and Vu, Quoc Dang and Jahanifar, Mostafa and Deshpande, Srijay and Hadjigeorghiou, Giorgos and Shephard, Adam and Bashir, Raja Muhammad Saad and Bilal, Mohsin and Lu, Wenqi and Epstein, David and Minhas, Fayyaz and Rajpoot, Nasir M and Raza, Shan E Ahmed},
doi = {10.1038/s43856-022-00186-5},
issn = {2730-664X},
journal = {Communications Medicine},
month = {sep},
number = {1},
pages = {120},
publisher = {Springer US},
title = {{TIAToolbox as an end-to-end library for advanced tissue image analytics}},
url = {https://www.nature.com/articles/s43856-022-00186-5},
volume = {2},
year = {2022}
}
``` |
MAGAer13/mplug-owl2-llama2-7b | MAGAer13 | 2023-11-08T12:17:33Z | 5,875 | 22 | transformers | [
"transformers",
"pytorch",
"mplug_owl2",
"transformer",
"mPLUG",
"Multimodal",
"ChatGPT",
"GPT",
"Alibaba",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2023-11-02T10:02:34Z | ---
tasks:
- multimodal-dialogue
studios:
- damo/mPLUG-Owl
model-type:
- mplug-owl2
domain:
- multi-modal
frameworks:
- pytorch
backbone:
- transformer
containers:
license: apache-2.0
language:
- en
tags:
- transformer
- mPLUG
- Multimodal
- ChatGPT
- GPT
- Alibaba
---
# mPLUG-Owl2
|
owanr/SChem5Labels-google-t5-v1_1-large-intra_model-dataset-frequency-model_annots_str | owanr | 2023-11-08T12:15:06Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:google/t5-v1_1-large",
"base_model:finetune:google/t5-v1_1-large",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-05T22:26:09Z | ---
license: apache-2.0
base_model: google/t5-v1_1-large
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-google-t5-v1_1-large-intra_model-dataset-frequency-model_annots_str
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SChem5Labels-google-t5-v1_1-large-intra_model-dataset-frequency-model_annots_str
This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6279
## 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 19.7945 | 1.0 | 25 | 24.1161 |
| 18.755 | 2.0 | 50 | 19.9773 |
| 18.2706 | 3.0 | 75 | 11.6905 |
| 16.8867 | 4.0 | 100 | 10.8195 |
| 15.645 | 5.0 | 125 | 10.3424 |
| 12.6568 | 6.0 | 150 | 9.3585 |
| 10.9942 | 7.0 | 175 | 9.1290 |
| 9.2494 | 8.0 | 200 | 8.8652 |
| 8.4954 | 9.0 | 225 | 8.5857 |
| 8.1042 | 10.0 | 250 | 8.4259 |
| 7.8977 | 11.0 | 275 | 8.3043 |
| 7.8384 | 12.0 | 300 | 8.1858 |
| 7.7411 | 13.0 | 325 | 7.9134 |
| 7.3565 | 14.0 | 350 | 7.6255 |
| 7.2074 | 15.0 | 375 | 7.3867 |
| 7.0111 | 16.0 | 400 | 7.2259 |
| 6.9705 | 17.0 | 425 | 7.1460 |
| 6.8314 | 18.0 | 450 | 7.0866 |
| 6.7505 | 19.0 | 475 | 7.0398 |
| 6.6081 | 20.0 | 500 | 6.9983 |
| 6.7054 | 21.0 | 525 | 6.9523 |
| 6.5904 | 22.0 | 550 | 6.9090 |
| 6.4272 | 23.0 | 575 | 6.5798 |
| 0.9712 | 24.0 | 600 | 0.7144 |
| 0.7214 | 25.0 | 625 | 0.6178 |
| 0.6687 | 26.0 | 650 | 0.6174 |
| 0.6565 | 27.0 | 675 | 0.6148 |
| 0.6602 | 28.0 | 700 | 0.6140 |
| 0.6449 | 29.0 | 725 | 0.6121 |
| 0.648 | 30.0 | 750 | 0.6133 |
| 0.6425 | 31.0 | 775 | 0.6154 |
| 0.6505 | 32.0 | 800 | 0.6115 |
| 0.661 | 33.0 | 825 | 0.6128 |
| 0.6482 | 34.0 | 850 | 0.6108 |
| 0.6501 | 35.0 | 875 | 0.6137 |
| 0.6436 | 36.0 | 900 | 0.6086 |
| 0.6377 | 37.0 | 925 | 0.6107 |
| 0.6275 | 38.0 | 950 | 0.6116 |
| 0.6254 | 39.0 | 975 | 0.6113 |
| 0.6357 | 40.0 | 1000 | 0.6091 |
| 0.6443 | 41.0 | 1025 | 0.6095 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
lmqg/mt5-base-koquad-qg-trimmed-50000 | lmqg | 2023-11-08T12:06:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-11-07T22:00:07Z | # Vocabulary Trimmed [lmqg/mt5-base-koquad-qg](https://huggingface.co/lmqg/mt5-base-koquad-qg): `lmqg/mt5-base-koquad-qg-trimmed-50000`
This model is a trimmed version of [lmqg/mt5-base-koquad-qg](https://huggingface.co/lmqg/mt5-base-koquad-qg) by [`vocabtrimmer`](https://github.com/asahi417/lm-vocab-trimmer), a tool for trimming vocabulary of language models to compress the model size.
Following table shows a summary of the trimming process.
| | lmqg/mt5-base-koquad-qg | lmqg/mt5-base-koquad-qg-trimmed-50000 |
|:---------------------------|:--------------------------|:----------------------------------------|
| parameter_size_full | 582,384,384 | 275,032,320 |
| parameter_size_embedding | 384,155,136 | 76,803,072 |
| vocab_size | 250,101 | 50,002 |
| compression_rate_full | 100.0 | 47.23 |
| compression_rate_embedding | 100.0 | 19.99 |
Following table shows the parameter used to trim vocabulary.
| language | dataset | dataset_column | dataset_name | dataset_split | target_vocab_size | min_frequency |
|:-----------|:----------------------------|:-----------------|:---------------|:----------------|--------------------:|----------------:|
| ko | vocabtrimmer/mc4_validation | text | ko | validation | 50000 | 2 | |
TheBloke/Psyfighter-13B-GGUF | TheBloke | 2023-11-08T12:04:44Z | 1,814 | 9 | transformers | [
"transformers",
"gguf",
"llama",
"base_model:jebcarter/Psyfighter-13B",
"base_model:quantized:jebcarter/Psyfighter-13B",
"license:llama2",
"region:us"
]
| null | 2023-11-08T11:55:53Z | ---
base_model: jebcarter/Psyfighter-13B
inference: false
license: llama2
model_creator: Jeb Carter
model_name: Psyfighter 13B
model_type: llama
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Psyfighter 13B - GGUF
- Model creator: [Jeb Carter](https://huggingface.co/jebcarter)
- Original model: [Psyfighter 13B](https://huggingface.co/jebcarter/Psyfighter-13B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jeb Carter's Psyfighter 13B](https://huggingface.co/jebcarter/Psyfighter-13B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Psyfighter-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Psyfighter-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF)
* [Jeb Carter's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jebcarter/Psyfighter-13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [psyfighter-13b.Q2_K.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [psyfighter-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [psyfighter-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [psyfighter-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [psyfighter-13b.Q4_0.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [psyfighter-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [psyfighter-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [psyfighter-13b.Q5_0.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [psyfighter-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [psyfighter-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [psyfighter-13b.Q6_K.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [psyfighter-13b.Q8_0.gguf](https://huggingface.co/TheBloke/Psyfighter-13B-GGUF/blob/main/psyfighter-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/Psyfighter-13B-GGUF and below it, a specific filename to download, such as: psyfighter-13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/Psyfighter-13B-GGUF psyfighter-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/Psyfighter-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Psyfighter-13B-GGUF psyfighter-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m psyfighter-13b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model in Python code, using ctransformers
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers
```
#### Simple ctransformers example code
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/Psyfighter-13B-GGUF", model_file="psyfighter-13b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Jeb Carter's Psyfighter 13B
```
merge_method: task_arithmetic
base_model: TheBloke/Llama-2-13B-fp16
models:
- model: TheBloke/Llama-2-13B-fp16
- model: KoboldAI/LLaMA2-13B-Tiefighter
parameters:
weight: 1.0
- model: chaoyi-wu/MedLLaMA_13B
parameters:
weight: 0.01
- model: Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged
parameters:
weight: 0.02
dtype: float16
```
This model was made possible thanks to the Compute provided by the KoboldAI community.
<!-- original-model-card end -->
|
marialcasimiro/tatoeba-opus-2021-02-22-eng-fra | marialcasimiro | 2023-11-08T11:44:55Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| translation | 2023-11-08T11:43:57Z | ---
language:
- en
- fr
tags:
- translation
license: apache-2.0
---
### eng-fra
* source language name: English
* target language name: French
* OPUS readme: [README.md](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fra/README.md)
* model: transformer-align
* source language code: en
* target language code: fr
* dataset: opus
* release date: 2021-02-22
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2021-02-22.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fra/opus-2021-02-22.zip/eng-fra/opus-2021-02-22.zip)
* Training data:
* fra-eng: Tatoeba-train (180923857)
* Validation data:
* eng-fra: Tatoeba-dev, 250098
* total-size-shuffled: 249757
* devset-selected: top 5000 lines of Tatoeba-dev.src.shuffled!
* Test data:
* newsdiscussdev2015-enfr.eng-fra: 1500/27986
* newsdiscusstest2015-enfr.eng-fra: 1500/28027
* newssyscomb2009.eng-fra: 502/12334
* news-test2008.eng-fra: 2051/52685
* newstest2009.eng-fra: 2525/69278
* newstest2010.eng-fra: 2489/66043
* newstest2011.eng-fra: 3003/80626
* newstest2012.eng-fra: 3003/78011
* newstest2013.eng-fra: 3000/70037
* Tatoeba-test.eng-fra: 10000/80769
* tico19-test.eng-fra: 2100/64655
* test set translations file: [test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fra/opus-2021-02-22.zip/eng-fra/opus-2021-02-22.test.txt)
* test set scores file: [eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fra/opus-2021-02-22.zip/eng-fra/opus-2021-02-22.eval.txt)
* BLEU-scores
|Test set|score|
|---|---|
|Tatoeba-test.eng-fra|50.8|
|tico19-test.eng-fra|41.8|
|newsdiscusstest2015-enfr.eng-fra|40.8|
|newstest2011.eng-fra|34.6|
|newsdiscussdev2015-enfr.eng-fra|33.9|
|newstest2013.eng-fra|33.5|
|newstest2010.eng-fra|33.0|
|newstest2012.eng-fra|32.0|
|newssyscomb2009.eng-fra|30.0|
|newstest2009.eng-fra|29.9|
|news-test2008.eng-fra|27.5|
* chr-F-scores
|Test set|score|
|---|---|
|Tatoeba-test.eng-fra|0.671|
|newsdiscusstest2015-enfr.eng-fra|0.649|
|tico19-test.eng-fra|0.638|
|newstest2011.eng-fra|0.614|
|newsdiscussdev2015-enfr.eng-fra|0.606|
|newstest2010.eng-fra|0.599|
|newstest2012.eng-fra|0.593|
|newstest2013.eng-fra|0.591|
|newssyscomb2009.eng-fra|0.587|
|newstest2009.eng-fra|0.58|
|news-test2008.eng-fra|0.556|
### System Info:
* hf_name: eng-fra
* source_languages: en
* target_languages: fr
* opus_readme_url: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-fra/opus-2021-02-22.zip/README.md
* original_repo: Tatoeba-Challenge
* tags: ['translation']
* languages: ['en', 'fr']
* src_constituents: ['eng']
* tgt_constituents: ['fra']
* src_multilingual: False
* tgt_multilingual: False
* helsinki_git_sha: 6faf2dab0b7b01a0e08a114dbacbb7deac54988d
* transformers_git_sha: e9a6c72b5edfb9561a981959b0e7c62d8ab9ef6c
* port_machine: 146-193-182-187.edr.inesc.pt
* port_time: 2023-11-08-11:42 |
AIBridgeEngine/Ben-3B-Biddinginformation-v0.1 | AIBridgeEngine | 2023-11-08T11:41:14Z | 0 | 0 | null | [
"license:lgpl-3.0",
"region:us"
]
| null | 2023-11-08T11:38:13Z | ---
license: lgpl-3.0
---
Neural network to predict distribution af hands in bridge based on the bidding. |
mangeshdiyewar/Llama-2-7b-chat-hf-fine-tuned-adapters_translation | mangeshdiyewar | 2023-11-08T11:40:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
]
| null | 2023-11-07T08:25:40Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
Felladrin/onnx-flan-alpaca-base | Felladrin | 2023-11-08T11:33:36Z | 9 | 0 | transformers.js | [
"transformers.js",
"onnx",
"t5",
"text2text-generation",
"license:apache-2.0",
"region:us"
]
| text2text-generation | 2023-11-08T11:13:33Z | ---
license: apache-2.0
library_name: "transformers.js"
---
INT8 ONNX version of [declare-lab/flan-alpaca-base](https://huggingface.co/declare-lab/flan-alpaca-base) to use with [Transformers.js](https://huggingface.co/docs/transformers.js). |
brightfarmns/taxi-v3 | brightfarmns | 2023-11-08T11:06:01Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T10:57:32Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="brightfarmns/taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
narensymb/mistral-finetune | narensymb | 2023-11-08T10:54:22Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"base_model:finetune:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-08T10:52:14Z | ---
license: apache-2.0
base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
tags:
- generated_from_trainer
model-index:
- name: mistral-finetune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-finetune
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
### Training results
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
juliajoanna/sdxl-flintstones_finetuning_on_lora_pretrained-one_hot_encoding_2 | juliajoanna | 2023-11-08T10:53:50Z | 0 | 0 | diffusers | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"base_model:juliajoanna/sdxl-one_hot_encoding",
"base_model:finetune:juliajoanna/sdxl-one_hot_encoding",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-11-08T02:06:25Z |
---
license: creativeml-openrail-m
base_model: juliajoanna/sdxl-one_hot_encoding
dataset: None
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - juliajoanna/sdxl-flintstones_finetuning_on_lora_pretrained-one_hot_encoding_2
This pipeline was finetuned from **juliajoanna/sdxl-one_hot_encoding** on the **None** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: Fred is driving a car:
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
ryantaw/distilbert-base-uncased-finetuned | ryantaw | 2023-11-08T10:50:50Z | 44 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-04T13:59:19Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0319
- Accuracy: 0.6038
- F1 Score: 0.5960
## 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: 1.0136026165598675e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|
| No log | 1.0 | 186 | 1.0319 | 0.6038 | 0.5960 |
| No log | 2.0 | 372 | 0.9585 | 0.5930 | 0.5890 |
| 1.0352 | 3.0 | 558 | 0.9438 | 0.5795 | 0.5791 |
| 1.0352 | 4.0 | 744 | 0.9726 | 0.5957 | 0.5966 |
| 1.0352 | 5.0 | 930 | 1.0109 | 0.5876 | 0.5870 |
| 0.6438 | 6.0 | 1116 | 1.1121 | 0.5795 | 0.5775 |
| 0.6438 | 7.0 | 1302 | 1.1804 | 0.5714 | 0.5711 |
| 0.6438 | 8.0 | 1488 | 1.2388 | 0.5741 | 0.5754 |
| 0.3747 | 9.0 | 1674 | 1.2941 | 0.5714 | 0.5708 |
| 0.3747 | 10.0 | 1860 | 1.3156 | 0.5714 | 0.5707 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
AntoineD/camembert_classification_tools | AntoineD | 2023-11-08T10:43:03Z | 90 | 0 | transformers | [
"transformers",
"pytorch",
"camembert",
"text-classification",
"generated_from_trainer",
"base_model:almanach/camembert-base",
"base_model:finetune:almanach/camembert-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-07T13:49:33Z | ---
license: mit
base_model: camembert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: camembert_classification_tools
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# camembert_classification_tools
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6969
- Accuracy: 0.775
## 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: 24
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 5 | 2.0733 | 0.2 |
| No log | 2.0 | 10 | 2.0478 | 0.275 |
| No log | 3.0 | 15 | 1.9161 | 0.45 |
| No log | 4.0 | 20 | 1.7607 | 0.425 |
| No log | 5.0 | 25 | 1.5895 | 0.575 |
| No log | 6.0 | 30 | 1.4201 | 0.625 |
| No log | 7.0 | 35 | 1.2944 | 0.675 |
| No log | 8.0 | 40 | 1.2193 | 0.75 |
| No log | 9.0 | 45 | 1.0974 | 0.775 |
| No log | 10.0 | 50 | 1.0429 | 0.825 |
| No log | 11.0 | 55 | 0.9602 | 0.8 |
| No log | 12.0 | 60 | 0.9059 | 0.8 |
| No log | 13.0 | 65 | 0.8365 | 0.825 |
| No log | 14.0 | 70 | 0.9396 | 0.725 |
| No log | 15.0 | 75 | 0.8271 | 0.8 |
| No log | 16.0 | 80 | 0.7762 | 0.8 |
| No log | 17.0 | 85 | 0.7847 | 0.8 |
| No log | 18.0 | 90 | 0.7012 | 0.8 |
| No log | 19.0 | 95 | 0.6971 | 0.8 |
| No log | 20.0 | 100 | 0.7186 | 0.775 |
| No log | 21.0 | 105 | 0.7946 | 0.725 |
| No log | 22.0 | 110 | 0.7721 | 0.725 |
| No log | 23.0 | 115 | 0.7642 | 0.725 |
| No log | 24.0 | 120 | 0.7298 | 0.75 |
| No log | 25.0 | 125 | 0.7191 | 0.75 |
| No log | 26.0 | 130 | 0.6978 | 0.775 |
| No log | 27.0 | 135 | 0.6913 | 0.8 |
| No log | 28.0 | 140 | 0.6949 | 0.775 |
| No log | 29.0 | 145 | 0.6961 | 0.775 |
| No log | 30.0 | 150 | 0.6969 | 0.775 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.1
|
owanr/SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str_mse | owanr | 2023-11-08T10:40:38Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:google/t5-v1_1-large",
"base_model:finetune:google/t5-v1_1-large",
"license:apache-2.0",
"region:us"
]
| null | 2023-11-08T10:40:37Z | ---
license: apache-2.0
base_model: google/t5-v1_1-large
tags:
- generated_from_trainer
model-index:
- name: SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str_mse
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SBIC-google-t5-v1_1-large-intra_model-frequency-human_annots_str_mse
This model is a fine-tuned version of [google/t5-v1_1-large](https://huggingface.co/google/t5-v1_1-large) on the None dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.6984
- Loss: 0.3735
- Losses: [3, 2, 5, 2, 2, 2, 4, 2, 2, 2, 2, 3, 5, 2, 3, 2, 4, 2, 3, 2, 5, 4, 5, 2, 2, 5, 4, 2, 2, 3, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 3, 2, 2, 2, 3, 3, 2, 4, 2, 2, 2, 2, 5, 3, 2, 2, 3, 2, 5, 2, 4, 2, 4, 2, 2, 3, 4, 2, 2, 4, 2, 3, 5, 2, 3, 2, 4, 2, 2, 2, 4, 2, 4, 5, 4, 5, 2, 5, 2, 2, 2, 2, 2, 2, 2, 3, 5, 2, 4, 2, 2, 2, 2, 4, 5, 3, 5, 2, 2, 2, 2, 2, 4, 4, 2, 2, 3, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 5, 2, 2, 2, 3, 2, 2, 2, 5, 2, 2, 4, 2, 2, 2, 2, 3, 2, 3, 5, 3, 2, 2, 4, 3, 2, 2, 2, 4, 2, 2, 2, 5, 5, 2, 2, 5, 4, 3, 2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 4, 5, 5, 2, 2, 2, 2, 2, 2, 2, 3, 4, 4, 4, 2, 3, 2, 5, 4, 2, 4, 2, 2, 4, 2, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 5, 5, 2, 4, 3, 2, 4, 2, 4, 4, 3, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 4, 3, 3, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 2, 2, 3, 3, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 3, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 5, 4, 5, 2, 2, 2, 4, 2, 2, 4, 5, 2, 2, 5, 3, 2, 2, 2, 2, 2, 3, 3, 5, 5, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 2, 3, 2, 3, 3, 3, 2, 2, 2, 5, 3, 5, 3, 3, 5, 2, 4, 2, 2, 3, 2, 4, 2, 4, 2, 2, 3, 3, 2, 2, 5, 2, 5, 2, 2, 2, 2, 3, 4, 4, 2, 3, 2, 4, 2, 2, 4, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 4, 4, 4, 2, 2, 2, 2, 2, 4, 4, 2, 2, 2, 2, 2, 4, 2, 5, 2, 5, 2, 5, 4, 2, 2, 2, 5, 2, 2, 2, 3, 4, 2, 2, 2, 2, 2, 4, 2, 2, 2, 5, 2, 4, 5, 3, 2, 2, 2, 4, 2, 2, 3, 2, 2, 2, 2, 3, 5, 2, 5, 2, 2, 2, 2, 4, 3, 3, 2, 2, 2, 2, 2, 5, 3, 4, 3, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 5, 3, 4, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 5, 3, 2, 2, 2, 2, 5, 2, 2, 4, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 4, 2, 4, 2, 2, 2, 4, 2, 5, 2, 2, 2, 5, 2, 5, 2, 2, 2, 4, 3, 2, 2, 2, 2, 2, 4, 2, 2, 2, 5, 2, 2, 3, 2, 3, 4, 2, 2, 5, 2, 3, 2, 4, 2, 5, 5, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 5, 4, 5, 2, 2, 2, 5, 5, 2, 4, 2, 2, 5, 2, 3, 2, 4, 2, 5, 2, 4, 2, 4, 2, 2, 2, 3, 2, 2, 2, 2, 5, 3, 2, 2, 2, 3, 2, 4, 2, 4, 4, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 5, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 3, 2, 2, 3, 3, 4, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 3, 4, 2, 2, 4, 2, 2, 2, 4, 2, 2, 2, 4, 3, 2, 2, 2, 2, 2, 4, 2, 3, 2, 5, 3, 5, 2, 2, 2, 2, 3, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 4, 5, 2, 3, 2, 5, 2, 2, 3, 3, 2, 2, 2, 2, 2, 4, 2, 4, 2, 2, 2, 4, 4, 2, 2, 2, 2, 3, 2, 2, 3, 4, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 5, 4, 2, 2, 5, 4, 2, 2, 2, 2, 2, 2, 2, 4, 2, 4, 4, 3, 5, 2, 3, 2, 2, 4, 3, 2, 3, 2, 2, 2, 3, 2, 5, 2, 5, 2, 2, 2, 2, 4, 2, 2, 5, 2, 3, 3, 2, 2, 5, 2, 2, 2, 2, 3, 4, 3, 4, 4, 2, 4, 2, 3, 3, 2, 2, 2, 2, 3, 5, 2, 2, 2, 5, 5, 2, 2, 2, 3, 5, 4, 5, 2, 5, 2, 2, 5, 4, 4, 2, 4, 4, 2, 2, 2, 2, 4, 3, 2, 2, 2, 2, 4, 2, 5, 2, 2, 2, 2, 5, 2, 3, 2, 2, 2, 2, 5, 3, 2, 5, 2, 2, 2, 2, 3, 2, 2, 4, 2, 2, 3, 5, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 2, 5, 5, 2, 3, 2, 2, 2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 4, 4, 2, 3, 2, 5, 3, 3, 2, 2, 5, 2, 4, 2, 2, 2, 2, 2, 5, 2, 4, 3, 2, 4, 2, 2, 3, 2, 3, 3, 3, 2, 2, 2, 2, 2, 4, 5, 3, 2, 4, 2, 2, 2, 2, 4, 3, 5, 2, 2, 2, 2, 2, 4, 2, 2, 4, 2, 2, 2, 5, 2, 2, 4, 2, 4, 2, 2, 3, 2, 2, 2, 5, 2, 5, 2, 2, 2, 2, 2, 5, 4, 2, 4, 4, 2, 2, 2, 2, 2, 2, 4, 3, 2, 3, 2, 2, 2, 2, 4, 4, 2, 2, 2, 5, 2, 2, 2, 5, 2, 3, 5, 4, 2, 2, 2, 3, 5, 2, 5, 2, 3, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 5, 2, 3, 4, 2, 2, 4, 2, 3, 5, 3, 5, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 4, 2, 3, 3, 5, 2, 2, 4, 5, 2, 2, 2, 2, 4, 2, 5, 2, 3, 2, 2, 2, 5, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 5, 4, 5, 2, 2, 2, 4, 2, 2, 2, 5, 5, 2, 2, 2, 2, 3, 5, 2, 2, 2, 2, 2, 3, 2, 2, 4, 4, 2, 2, 2, 2, 3, 2, 3, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 5, 2, 3, 2, 2, 2, 2, 5, 4, 3, 2, 3, 2, 5, 2, 2, 2, 5, 2, 5, 2, 2, 2, 4, 5, 5, 2, 3, 4, 2, 5, 2, 2, 2, 2, 2, 2, 2, 3, 5, 2, 5, 3, 2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 5, 2, 2, 5, 4, 2, 2, 2, 2, 4, 2, 5, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 5, 3, 2, 2, 2, 5, 2, 2, 2, 5, 4, 5, 2, 2, 4, 2, 2, 5, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 4, 2, 5, 2, 2, 3, 2, 2, 2, 3, 2, 5, 2, 2, 2, 2, 2, 5, 5, 5, 2, 4, 2, 5, 2, 2, 3, 3, 4, 4, 5, 2, 3, 2, 4, 3, 2, 4, 4, 2, 2, 4, 3, 2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 5, 2, 5, 2, 4, 2, 3, 3, 5, 3, 2, 4, 2, 2, 2, 4, 2, 2, 5, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 5, 2, 3, 4, 2, 4, 4, 3, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 3, 5, 2, 5, 2, 2, 4, 2, 2, 4, 3, 2, 2, 2, 3, 4, 2, 2, 2, 2, 3, 5, 2, 2, 2, 2, 3, 4, 5, 2, 2, 2, 5, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 5, 2, 3, 4, 2, 5, 2, 2, 5, 2, 2, 2, 2, 3, 2, 2, 3, 2, 4, 5, 2, 3, 2, 2, 2, 2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 5, 2, 2, 2, 2, 4, 5, 4, 4, 2, 3, 2, 5, 4, 2, 3, 2, 2, 2, 2, 3, 4, 2, 2, 5, 2, 4, 3, 2, 2, 2]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 200
### Training results
| Training Loss | Epoch | Step | Train Loss | Validation Loss | Losses |
|:-------------:|:-----:|:-----:|:----------:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 7.5029 | 1.0 | 392 | 5.7585 | 8.2091 | [2, 5.4, 5.4, 3, 2, 10.8, 0, 5.4, 5.4, 8.100000000000001, 7.2, 5.4, 9.0, 5.4, 7.2, 0, 6.300000000000001, 1.8, 5.4, 6.300000000000001, 5.4, 10.8, 7.2, 4, 5.4, 0, 3, 0, 9.0, 5.4, 5.4, 0, 8.100000000000001, 7.2, 4, 6.300000000000001, 9.9, 0, 9.9, 5.4, 10.8, 3, 5.4, 7.2, 5.4, 5.4, 6.300000000000001, 8.100000000000001, 5.4, 5.4, 3, 5.4, 5.4, 5.4, 10.8, 3, 3, 10.8, 7.2, 5.4, 8.100000000000001, 8.100000000000001, 5.4, 0, 7.2, 10.8, 3, 5.4, 9.0, 2, 5.4, 2, 5.4, 9.0, 3, 2, 5.4, 3, 7.2, 0, 3, 8.100000000000001, 5.4, 8.100000000000001, 10.8, 8.100000000000001, 3, 9.9, 5.4, 5.4, 3, 5.4, 3, 7.2, 3, 9.0, 5.4, 5, 2, 3, 3, 5.4, 10.8, 0, 3, 3, 5.4, 4, 10.8, 5.4, 10.8, 5.4, 5.4, 10.8, 5.4, 4, 1, 2, 5.4, 5.4, 5.4, 5.4, 2, 3, 5.4, 7.2, 9.0, 1, 7.2, 4, 10.8, 3, 10.8, 6.300000000000001, 5.4, 10.8, 2, 2, 7.2, 10.8, 2, 6.300000000000001, 9.9, 5.4, 9.0, 2, 5.4, 3, 3, 9.0, 5.4, 9.9, 3, 5.4, 9.9, 6.300000000000001, 5.4, 9.9, 5.4, 5.4, 0, 0, 6, 0, 5.4, 10.8, 10.8, 2, 5.4, 10.8, 5.4, 2, 5.4, 10.8, 0, 9.9, 5.4, 4, 8.100000000000001, 3, 9.0, 10.8, 10.8, 7.2, 9.0, 3, 2, 6.300000000000001, 10.8, 5.4, 0, 3, 10.8, 10.8, 3, 9.0, 7.2, 2, 9.0, 3, 10.8, 6.300000000000001, 2, 9.0, 2, 6.300000000000001, 3, 5.4, 5.4, 8.100000000000001, 5.4, 8.100000000000001, 2, 9.9, 9.0, 9.9, 10.8, 5.4, 10.8, 2, 5.4, 0, 6.300000000000001, 3, 4.5, 10.8, 8.100000000000001, 5.4, 7.2, 6.300000000000001, 10.8, 10.8, 5.4, 10.8, 5.4, 10.8, 5.4, 2, 10.8, 6.300000000000001, 9.9, 5, 3, 1, 3, 5.4, 3, 8.100000000000001, 7.2, 8.100000000000001, 2, 5.4, 3, 3, 2, 2, 9.0, 7.2, 5.4, 5.4, 6.300000000000001, 10.8, 5.4, 7.2, 5.4, 3, 0, 5.4, 5.4, 1, 5.4, 9.9, 5.4, 0, 3, 5.4, 5.4, 10.8, 7.2, 5.4, 3, 3, 6.300000000000001, 5.4, 0, 7.2, 7.2, 6.300000000000001, 5.4, 5.4, 5.4, 3, 10.8, 5.4, 6.300000000000001, 7.2, 7.2, 10.8, 3, 2, 9.9, 6, 10.8, 5.4, 0, 9.0, 3, 10.8, 2, 3, 5.4, 5.4, 9.0, 3, 2, 3, 5.4, 0, 2, 2, 0, 4, 2, 5.4, 1, 5.4, 6.300000000000001, 3, 2, 1, 7.2, 3, 5.4, 9.0, 10.8, 2, 5.4, 10.8, 5.4, 3, 5.4, 7.2, 10.8, 2, 6.300000000000001, 3, 3, 4, 5.4, 10.8, 0, 3, 6.300000000000001, 8.100000000000001, 5.4, 5.4, 0, 2, 1, 4, 5.4, 10.8, 10.8, 5.4, 5.4, 6.300000000000001, 10.8, 8.100000000000001, 8.100000000000001, 3, 5.4, 7.2, 10.8, 5.4, 9.9, 9.9, 6.300000000000001, 10.8, 3, 0, 8.100000000000001, 2, 9.9, 3, 10.8, 5.4, 5.4, 2, 10.8, 7.2, 9.0, 0, 9.0, 3, 10.8, 3, 7.2, 2, 5.4, 8.100000000000001, 7.2, 2, 9.0, 5.4, 2, 7.2, 4, 7.2, 10.8, 5.4, 3, 3, 5.4, 5.4, 9.0, 6.300000000000001, 6, 7.2, 5.4, 5.4, 3, 9.0, 5.4, 5.4, 2, 6, 5.4, 6.300000000000001, 5.4, 5.4, 5.4, 9.0, 9.0, 5.4, 10.8, 10.8, 9.0, 5.4, 8.100000000000001, 3, 4, 3, 1, 7.2, 5.4, 9.0, 6.300000000000001, 3, 9.0, 9.0, 9.9, 2, 10.8, 7.2, 9.9, 7.2, 5.4, 10.8, 9.0, 5.4, 0, 5.4, 10.8, 8.100000000000001, 10.8, 4, 5.4, 10.8, 5.4, 7.2, 5.4, 5.4, 2, 2, 9.9, 6.300000000000001, 3, 2, 3, 5.4, 3, 5.4, 5.4, 5.4, 10.8, 10.8, 3, 5.4, 9.9, 5.4, 9.0, 3, 16, 3, 1, 2, 6.300000000000001, 1, 8.100000000000001, 3, 5.4, 9.0, 3, 5.4, 5.4, 10.8, 3, 5.4, 5.4, 3, 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| 0.2806 | 28.0 | 10976 | 2.4520 | 0.2953 | [4, 2, 5, 2, 3, 2, 2, 2, 2, 3, 3, 2, 3, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 5, 3, 2, 4, 2, 2, 2, 2, 5, 2, 2, 2, 3, 2, 2, 5, 2, 5, 2, 2, 2, 3, 2, 4, 2, 4, 4, 2, 2, 5, 2, 5, 2, 3, 3, 2, 3, 2, 3, 2, 2, 2, 2, 4, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 2, 2, 2, 2, 5, 2, 3, 2, 2, 2, 2, 2, 5, 4, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 4, 2, 2, 4, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 3, 2, 3, 3, 2, 2, 2, 3, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 5, 3, 4, 2, 3, 2, 2, 2, 3, 2, 5, 5, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 4, 2, 2, 2, 4, 3, 2, 2, 2, 2, 2, 4, 2, 3, 2, 4, 3, 3, 2, 2, 2, 3, 2, 2, 5, 2, 3, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 5, 3, 4, 2, 3, 2, 2, 2, 3, 5, 4, 2, 2, 2, 2, 4, 3, 3, 4, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 5, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 3, 2, 3, 2, 3, 4, 2, 2, 2, 2, 2, 2, 5, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2, 2, 4, 2, 2, 4, 3, 2, 2, 2, 2, 4, 3, 3, 2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 2, 2, 5, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 2, 5, 5, 2, 2, 3, 3, 2, 2, 2, 4, 2, 4, 4, 4, 2, 3, 5, 2, 3, 2, 2, 3, 3, 3, 2, 2, 2, 4, 3, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 3, 2, 3, 2, 3, 2, 2, 3, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 5, 2, 4, 2, 4, 2, 2, 2, 3, 2, 4, 3, 2, 2, 2, 3, 2, 2, 2, 3, 4, 2, 3, 2, 2, 5, 4, 4, 2, 2, 5, 2, 3, 4, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 0, 3, 4, 2, 5, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 5, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 4, 2, 2, 2, 2, 4, 2, 3, 2, 3, 2, 2, 3, 3, 5, 5, 3, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 3, 2, 2, 2, 2, 4, 3, 4, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 4, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 4, 2, 3, 4, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 4, 3, 2, 3, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 4, 4, 2, 2, 4, 2, 4, 4, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 3, 2, 3, 2, 2, 3, 4, 2, 4, 2, 2, 2, 2, 4, 2, 5, 2, 2, 2, 2, 2, 2, 2, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 5, 2, 3, 2, 4, 2, 2, 4, 2, 2, 3, 2, 3, 2, 2, 5, 3, 4, 2, 2, 2, 2, 5, 2, 3, 2, 5, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 5, 2, 3, 2, 2, 2, 3, 2, 2, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 3, 4, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 3, 3, 2, 2, 3, 2, 2, 4, 5, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 3, 2, 5, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 2, 3, 4, 2, 2, 2, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 3, 2, 2, 2, 2, 3, 5, 2, 2, 5, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 4, 2, 5, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 5, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 5, 3, 3, 2, 2, 2, 2, 2, 5, 3, 2, 4, 2, 2, 2, 2, 2, 3, 5, 2, 4, 2, 5, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 4, 2, 2, 4, 0, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 5, 2, 4, 2, 2, 5, 2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 2, 3, 2, 4, 2, 2, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 5, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 5, 2, 2, 2, 4, 2, 5, 2, 5, 2, 2, 5, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 5, 4, 3, 2, 2, 2, 2, 4, 2, 2, 5, 2, 3, 3, 2, 5, 4, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 3, 2, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 3, 3, 5, 4, 2, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 5, 2, 4, 2, 2, 2, 2, 2, 3, 2, 2, 5] |
| 0.3133 | 29.0 | 11368 | 2.4520 | 0.2953 | [4, 2, 5, 2, 3, 2, 2, 2, 2, 3, 3, 2, 3, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 5, 3, 2, 4, 2, 2, 2, 2, 5, 2, 2, 2, 3, 2, 2, 5, 2, 5, 2, 2, 2, 3, 2, 4, 2, 4, 4, 2, 2, 5, 2, 5, 2, 3, 3, 2, 3, 2, 3, 2, 2, 2, 2, 4, 2, 2, 3, 2, 3, 2, 3, 2, 3, 2, 2, 2, 2, 2, 5, 2, 3, 2, 2, 2, 2, 2, 5, 4, 2, 3, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 4, 2, 2, 4, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 3, 2, 3, 3, 2, 2, 2, 3, 2, 3, 2, 2, 3, 2, 2, 3, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 5, 3, 4, 2, 3, 2, 2, 2, 3, 2, 5, 5, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 4, 2, 2, 2, 4, 3, 2, 2, 2, 2, 2, 4, 2, 3, 2, 4, 3, 3, 2, 2, 2, 3, 2, 2, 5, 2, 3, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 5, 3, 4, 2, 3, 2, 2, 2, 3, 5, 4, 2, 2, 2, 2, 4, 3, 3, 4, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 5, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 3, 3, 2, 3, 2, 3, 4, 2, 2, 2, 2, 2, 2, 5, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 3, 2, 2, 2, 2, 2, 2, 2, 2, 4, 4, 2, 2, 4, 2, 2, 4, 3, 2, 2, 2, 2, 4, 3, 3, 2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 2, 3, 2, 2, 2, 3, 3, 2, 3, 2, 2, 3, 2, 2, 2, 2, 5, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 2, 5, 5, 2, 2, 3, 3, 2, 2, 2, 4, 2, 4, 4, 4, 2, 3, 5, 2, 3, 2, 2, 3, 3, 3, 2, 2, 2, 4, 3, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 3, 2, 3, 2, 3, 2, 2, 3, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 5, 2, 4, 2, 4, 2, 2, 2, 3, 2, 4, 3, 2, 2, 2, 3, 2, 2, 2, 3, 4, 2, 3, 2, 2, 5, 4, 4, 2, 2, 5, 2, 3, 4, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 0, 3, 4, 2, 5, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 5, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 3, 2, 4, 2, 2, 2, 2, 4, 2, 3, 2, 3, 2, 2, 3, 3, 5, 5, 3, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 3, 2, 2, 2, 2, 4, 3, 4, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 2, 4, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 4, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 4, 2, 3, 4, 3, 3, 2, 2, 2, 2, 2, 2, 3, 2, 4, 3, 2, 3, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 4, 4, 2, 2, 4, 2, 4, 4, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 4, 3, 2, 3, 2, 2, 3, 4, 2, 4, 2, 2, 2, 2, 4, 2, 5, 2, 2, 2, 2, 2, 2, 2, 4, 4, 3, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 5, 2, 3, 2, 4, 2, 2, 4, 2, 2, 3, 2, 3, 2, 2, 5, 3, 4, 2, 2, 2, 2, 5, 2, 3, 2, 5, 2, 3, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 5, 2, 3, 2, 2, 2, 3, 2, 2, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 3, 4, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 3, 3, 2, 2, 3, 2, 2, 4, 5, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 3, 2, 5, 2, 2, 2, 2, 3, 2, 2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 2, 3, 4, 2, 2, 2, 3, 3, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 2, 2, 3, 2, 2, 2, 2, 3, 5, 2, 2, 5, 2, 3, 2, 2, 2, 2, 2, 2, 4, 2, 4, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 4, 2, 5, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 3, 0, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 5, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 5, 3, 3, 2, 2, 2, 2, 2, 5, 3, 2, 4, 2, 2, 2, 2, 2, 3, 5, 2, 4, 2, 5, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 2, 3, 2, 4, 2, 2, 4, 0, 4, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 2, 5, 2, 4, 2, 2, 5, 2, 3, 3, 2, 2, 2, 3, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 4, 2, 3, 2, 2, 3, 2, 4, 2, 2, 4, 2, 2, 4, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 2, 3, 2, 5, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 4, 2, 5, 2, 2, 2, 4, 2, 5, 2, 5, 2, 2, 5, 2, 2, 2, 2, 2, 2, 3, 3, 2, 2, 3, 2, 2, 2, 5, 4, 3, 2, 2, 2, 2, 4, 2, 2, 5, 2, 3, 3, 2, 5, 4, 5, 2, 2, 2, 2, 2, 2, 2, 2, 2, 5, 3, 2, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 3, 3, 3, 5, 4, 2, 4, 2, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 3, 2, 3, 2, 2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 3, 2, 2, 2, 2, 3, 4, 2, 2, 2, 2, 4, 2, 2, 2, 2, 2, 3, 3, 2, 2, 2, 2, 5, 2, 4, 2, 2, 2, 2, 2, 3, 2, 2, 5] |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.6.1
- Tokenizers 0.14.1
|
Acadys/PointCon-Vigogne33B | Acadys | 2023-11-08T10:40:03Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"lora",
"fr",
"dataset:IUseAMouse/POINTCON-QA-Light",
"base_model:bofenghuang/vigogne-33b-instruct",
"base_model:adapter:bofenghuang/vigogne-33b-instruct",
"license:openrail",
"region:us"
]
| null | 2023-11-08T09:26:21Z | ---
license: openrail
base_model: bofenghuang/vigogne-33b-instruct
tags:
- generated_from_trainer
- lora
model-index:
- name: PointCon-vigogne-33b-instruct-3
results: []
datasets:
- IUseAMouse/POINTCON-QA-Light
language:
- fr
---
<!-- 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. -->
# PointCon-vigogne-33b-instruct-3
This model is a fine-tuned version of [bofenghuang/vigogne-33b-instruct](https://huggingface.co/bofenghuang/vigogne-33b-instruct) on the .CON french satirical corpus.
It achieves the following results on the evaluation set:
- Loss: 1.8266
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0831 | 0.24 | 30 | 1.9738 |
| 1.9472 | 0.48 | 60 | 1.8989 |
| 1.8874 | 0.73 | 90 | 1.8626 |
| 1.8311 | 0.97 | 120 | 1.8403 |
| 1.7394 | 1.21 | 150 | 1.8423 |
| 1.6894 | 1.45 | 180 | 1.8373 |
| 1.6351 | 1.69 | 210 | 1.8295 |
| 1.7245 | 1.94 | 240 | 1.8266 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1 |
amaanbadure/GPT2QA_wikiqa | amaanbadure | 2023-11-08T10:29:28Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-classification",
"generated_from_trainer",
"dataset:wiki_qa",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-11-08T10:11:09Z | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
datasets:
- wiki_qa
metrics:
- accuracy
- f1
model-index:
- name: GPT2QA_wikiqa
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: wiki_qa
type: wiki_qa
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9578606158833063
- name: F1
type: f1
value: 0.0
---
<!-- 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. -->
# GPT2QA_wikiqa
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the wiki_qa dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2413
- Accuracy: 0.9579
- F1: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| 0.1963 | 1.0 | 1387 | 0.2651 | 0.9579 | 0.0 |
| 0.2095 | 2.0 | 2774 | 0.2413 | 0.9579 | 0.0 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
makwingchi/a2c-PandaReachDense-v3 | makwingchi | 2023-11-08T10:11:11Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-11-08T10:04:31Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.17 +/- 0.14
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
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