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kushgabani/bloom-1b1-lora-tagger | kushgabani | 2023-10-26T09:15:00Z | 31 | 0 | peft | [
"peft",
"text-generation",
"arxiv:1910.09700",
"base_model:bigscience/bloom-1b1",
"base_model:adapter:bigscience/bloom-1b1",
"region:us"
]
| text-generation | 2023-10-25T11:05:43Z | ---
library_name: peft
base_model: bigscience/bloom-1b1
pipeline_tag: text-generation
---
# 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:** Kush Gabani
- **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]:** bigscience/bloom-1b1
## 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 0.6.0.dev0
|
NanaEilish/t5_conll_ontonotes_en12 | NanaEilish | 2023-10-26T09:03:34Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"en",
"dataset:conll2012_ontonotesv5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-10-25T12:10:11Z | ---
datasets:
- conll2012_ontonotesv5
language:
- en
pipeline_tag: text2text-generation
---
Given a text, its output format is: `"{ENT_TYPE}:{span}; {ENT_TYPE}:{span}..."`\
For training speed, we only use the first 10,000 sentences (not documents) from train set; 1,000 sentences from validation set;\
we save the model when its val_loss (NLL) reaches the minimum.\
The model could be used as a pretrained backbone on downstream fine-tuning NER tasks.
|
limerooster/vietnamese-llama-2-7B-ngkplnltc-inst | limerooster | 2023-10-26T08:58:06Z | 1 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-10-26T08:57:28Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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 0.5.0
|
Rocinante/llama_merge | Rocinante | 2023-10-26T08:58:04Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-10-26T08:47:27Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0
|
pgze/sparsified_transformers | pgze | 2023-10-26T08:51:12Z | 0 | 0 | null | [
"region:us"
]
| null | 2023-10-08T09:01:22Z | # Experiment Logs of "A Theoretical Explanation of Activation Sparsity through Flat Minima and Adversarial Robustness"
This repo holds the experiment logs of our manuscript.
They include raw data of main results displayed in the paper, as well as some tentative experiments that are removed due to limit of length.
They can be cloned and viewed by TensorBoard opened on corresponding subdirectories, or directly viewed online on Huggingface by [clicking "Training metrics"](https://huggingface.co/docs/hub/tensorboard).
The terminology in log database may be different with those in the paper. For example, "concentration" is used instead of "sparsity", because in experiments for validation the activations are not sparse but concentrate around non-zero values.
## Main Results
The raw data of results displayed in the manuscript can be found in
- Experiments for Validation (Sec 6): `runs/manipulate_width_implicit_relu2/vit_(activation_layer=weird_relu2/*/dataset=cifar10/*/lr=0.0001/`
- Training ViT-Base/16 on ImageNet-1k from Scratch (Sec 7.1): `runs/imagenet1k/from_scratch5/`
- Finetuning ViT-Base/16 for Sparsity (Sec 7.2): `runs/imagenet1k/finetuning_hard_uplifting/`
- Training T5-Base on C4 from Scratch (Sec 7.1): `runs/T5/from_scratch/`
- Finetuning T5-Base for Sparsity (Sec 7.2): `runs/T5/finetuning/`
- Applicability of Theorem 8 (Sec 8.4): `runs/marchenko_pastur/`
## Tentative Experiments
Other subdirectories in `runs/manipulate_width_implicit_relu2/` show activation concentration manipulation with non-squared weird activation functions. It can be seen that the jumpy-squaring drastically strengthens the dominance of gradient sparsity, because under non-squared activation functions, significantly less activations concentrate in the expected flat interval.
Training ViT from scratch with non-refined Zeroth Biases, i.e., without ZB clamping and LayerNorm restricting, can be found in `runs/imagenet1k/from_scratch3` (from_scratch1, from_scratch2 and from_scratch4 are short training during which implementation bugs are found). The gradient clipping norm is 10 for both vanilla and modified training because we believed that this leads to more intense gradient noises and better flatness. These non-refined sparsifications bring about 20% sparsity improvements, showing the effectiveness of, even not refined, our modification. On the other hand, the improvements after the refinements reveal the critical role of LayerNorm when it comes to sparsity.
Finetuning ViT without LayerNorm uplifting can be found in `runs/imagenet1k/finetuning`. The sparsity hardly changed. This, again, indicates the significance of LayerNorm on sparsity.
Experiments using T5 happen after those of ViT so T5 does not have tentative experiments. |
lectura/hwangsaeyeon | lectura | 2023-10-26T08:40:26Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-26T06:56:08Z | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: hwangsaeyeon
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. -->
# hwangsaeyeon
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.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
MCFred/bert-base-swedish-uncased-certainly | MCFred | 2023-10-26T08:33:29Z | 9 | 1 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"feature-extraction",
"sv",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2023-04-20T14:05:27Z | ---
model-index:
- name: bert-base-swedish-uncased-certainly
results: []
language:
- sv
---
<!-- 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. -->
# bert-base-swedish-uncased-certainly
Model downloaded from https://github.com/certainlyio/nordic_bert |
yeaool/cppe5_use_data_finetuning | yeaool | 2023-10-26T08:14:00Z | 33 | 0 | transformers | [
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:cppe-5",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| object-detection | 2023-10-26T03:35:23Z | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
datasets:
- cppe-5
model-index:
- name: cppe5_use_data_finetuning
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. -->
# cppe5_use_data_finetuning
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on the cppe-5 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
juri17/whisper-small-100h-de | juri17 | 2023-10-26T08:01:05Z | 76 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"de",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-10-18T09:36:02Z | ---
language:
- de
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper small de
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: de
split: other
args: 'config: de, split: test'
metrics:
- name: Wer
type: wer
value: 11.98839498497565
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper small de
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2317
- Wer: 11.9884
## 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: 7.5e-06
- train_batch_size: 32
- 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: 220
- training_steps: 2200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2309 | 0.25 | 550 | 0.2681 | 12.4339 |
| 0.1415 | 0.5 | 1100 | 0.2456 | 11.4703 |
| 0.2352 | 0.75 | 1650 | 0.2360 | 11.2216 |
| 0.1589 | 1.0 | 2200 | 0.2317 | 11.9884 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.6.dev0
- Tokenizers 0.14.0
|
xjlulu/ntu_adl_span_selection_roberta | xjlulu | 2023-10-26T07:58:44Z | 22 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:hfl/chinese-roberta-wwm-ext",
"base_model:finetune:hfl/chinese-roberta-wwm-ext",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-10-22T00:45:55Z | ---
license: apache-2.0
base_model: hfl/chinese-roberta-wwm-ext
tags:
- generated_from_trainer
model-index:
- name: ntu_adl_span_selection_roberta
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. -->
# ntu_adl_span_selection_roberta
This model is a fine-tuned version of [hfl/chinese-roberta-wwm-ext](https://huggingface.co/hfl/chinese-roberta-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1714
- Em Accuracy: 0.7763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- 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 | Em Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------:|
| 0.9879 | 1.0 | 10857 | 0.9306 | 0.7441 |
| 0.5075 | 2.0 | 21714 | 0.9758 | 0.7723 |
| 0.2514 | 3.0 | 32571 | 1.1714 | 0.7763 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
haseong8012/whisper-large-v2_child10K_LoRA | haseong8012 | 2023-10-26T07:49:29Z | 7 | 0 | peft | [
"peft",
"tensorboard",
"dataset:haseong8012/child-10k",
"region:us"
]
| null | 2023-10-25T17:12:30Z | ---
library_name: peft
datasets:
- haseong8012/child-10k
---
## 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 0.5.0 |
jovalie/kitten-7b | jovalie | 2023-10-26T07:30:57Z | 73 | 0 | transformers | [
"transformers",
"pytorch",
"falcon",
"text-generation",
"Inference Endpoints",
"text-generation-inference",
"en",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2023-10-26T06:55:40Z | ---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- falcon
- Inference Endpoints
- text-generation-inference
--- |
Rushikesh97/my_awesome_wnut_model | Rushikesh97 | 2023-10-26T07:29:01Z | 58 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"token-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"
]
| token-classification | 2023-10-26T06:11:58Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Rushikesh97/my_awesome_wnut_model
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. -->
# Rushikesh97/my_awesome_wnut_model
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: 0.1179
- Validation Loss: 0.2582
- Train Precision: 0.5899
- Train Recall: 0.4318
- Train F1: 0.4986
- Train Accuracy: 0.9466
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 636, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.3464 | 0.3041 | 0.3929 | 0.1974 | 0.2627 | 0.9330 | 0 |
| 0.1563 | 0.2651 | 0.4992 | 0.3577 | 0.4167 | 0.9421 | 1 |
| 0.1179 | 0.2582 | 0.5899 | 0.4318 | 0.4986 | 0.9466 | 2 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
tingchih/1025_weight_3 | tingchih | 2023-10-26T07:16:15Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-10-26T05:47:21Z | ---
tags:
- generated_from_trainer
model-index:
- name: 1025_weight_3
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. -->
# 1025_weight_3
This model is a fine-tuned version of [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3632
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.3605 | 1.0 | 6207 | 1.3595 |
| 1.3471 | 2.0 | 12414 | 1.3632 |
### Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
arunptp/ppo-SnowballTarget | arunptp | 2023-10-26T06:38:31Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
]
| reinforcement-learning | 2023-07-03T09:37:35Z | ---
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 ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: arunptp/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
s3nh/likenneth-honest_llama2_chat_7B-GGUF | s3nh | 2023-10-26T06:38:17Z | 12 | 1 | transformers | [
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-24T12:41:48Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/likenneth/honest_llama2_chat_7B).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### Perplexity params
Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16
7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066
13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543
### inference
TODO
# Original model card
|
arunptp/ppo-pyramids | arunptp | 2023-10-26T06:23:28Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-10-26T05:24:36Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: arunptp/ppo-pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
SheldonSides/distilbert-base-uncased-finetuned-emotion | SheldonSides | 2023-10-26T06:22:30Z | 11 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"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 | 2023-10-21T20:27:49Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9244990073753523
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2143
- Accuracy: 0.9245
- F1: 0.9245
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5513 | 1.0 | 250 | 0.3057 | 0.908 | 0.9076 |
| 0.2227 | 2.0 | 500 | 0.2143 | 0.9245 | 0.9245 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Mahendrakharra/results | Mahendrakharra | 2023-10-26T06:21:38Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:finetune:NousResearch/Llama-2-7b-chat-hf",
"region:us"
]
| null | 2023-10-26T04:01:27Z | ---
base_model: NousResearch/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
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 [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- 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_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
|
LuisChDev/Reinforce-CartPole-v1 | LuisChDev | 2023-10-26T06:00:06Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-26T05:59:56Z | ---
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
|
DAMO-NLP-SG/rememo-large | DAMO-NLP-SG | 2023-10-26T05:55:05Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| null | 2023-10-25T06:19:53Z | ---
license: apache-2.0
---
## Model Description
Please refer to [our GitHub repo](https://github.com/DAMO-NLP-SG/RemeMo) for more details. |
DAMO-NLP-SG/roberta-time_identification | DAMO-NLP-SG | 2023-10-26T05:51:52Z | 221 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2023-10-25T09:01:33Z | ---
tags:
- generated_from_trainer
model-index:
- name: tim_expression_identify.2
results: []
---
## Model description
This model is a fine-tuned version of RoBERTa.
## Intended uses & limitations
For identifying time expressions in text. This model works in a NER-like manner but only focuses on time expressions.
- You may try an example sentence using the hosted inference API on HuggingFace:
*In Generation VII, Pokรฉmon Sun and Moon were released worldwide for the 3DS on November 18, 2016 and on November 23, 2016 in Europe.*
The JSON output would be like:
```
[
{
"entity_group": "TIME",
"score": 0.9959897994995117,
"word": " November 18",
"start": 79,
"end": 90
},
{
"entity_group": "TIME",
"score": 0.996467113494873,
"word": " 2016",
"start": 92,
"end": 96
},
{
"entity_group": "TIME",
"score": 0.9942433834075928,
"word": " November 23, 2016",
"start": 104,
"end": 121
}
]
```
## Training and evaluation data
TimeBank 1.2
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
|
1TuanPham/Instruct_en-vi_14000_1e_b60_lr2e-4_TheBloke_Mistralic-7B-1-GPTQ_LORA_CAUSAL_LM | 1TuanPham | 2023-10-26T05:46:12Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-10-25T17:05:48Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.1
- desc_act: True
- sym: True
- true_sequential: True
- use_cuda_fp16: True
- model_seqlen: 4096
- block_name_to_quantize: model.layers
- module_name_preceding_first_block: ['model.embed_tokens']
- batch_size: 1
- pad_token_id: None
- disable_exllama: False
- max_input_length: None
### Framework versions
- PEFT 0.5.0
|
QiiofRagnarok/my_awesome_eli5_clm-model | QiiofRagnarok | 2023-10-26T05:28:10Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-26T05:08:25Z | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: my_awesome_eli5_clm-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7672
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8722 | 1.0 | 1113 | 3.7843 |
| 3.7712 | 2.0 | 2226 | 3.7690 |
| 3.7279 | 3.0 | 3339 | 3.7672 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
quyanh/qwen-14b-neurips-v1 | quyanh | 2023-10-26T05:25:36Z | 3 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Qwen/Qwen-14B",
"base_model:adapter:Qwen/Qwen-14B",
"region:us"
]
| null | 2023-10-24T16:07:46Z | ---
library_name: peft
base_model: Qwen/Qwen-14B
---
# 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 0.6.0.dev0
|
reproductionguru/voicetest | reproductionguru | 2023-10-26T05:20:51Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-10-26T03:18:20Z | ---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
model-index:
- name: base
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. -->
# base
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the tutorial Voice 11.0 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
intanm/xlmrlarge-idkmrc-2 | intanm | 2023-10-26T05:15:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:intanm/xlmrlarge-idkmrc",
"base_model:finetune:intanm/xlmrlarge-idkmrc",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-10-26T04:53:38Z | ---
license: mit
base_model: intanm/xlmrlarge-idkmrc
tags:
- generated_from_trainer
model-index:
- name: xlmrlarge-idkmrc-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlmrlarge-idkmrc-2
This model is a fine-tuned version of [intanm/xlmrlarge-idkmrc](https://huggingface.co/intanm/xlmrlarge-idkmrc) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5737
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3687 | 1.0 | 1167 | 1.4416 |
| 0.1913 | 2.0 | 2334 | 1.5737 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
xjlulu/ntu_adl_span_selection_bert | xjlulu | 2023-10-26T05:10:54Z | 27 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-chinese",
"base_model:finetune:google-bert/bert-base-chinese",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-10-22T08:18:55Z | ---
base_model: bert-base-chinese
tags:
- generated_from_trainer
model-index:
- name: ntu_adl_span_selection_bert
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ntu_adl_span_selection_bert
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0552
- Em Accuracy: 0.7607
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Em Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------:|
| 1.161 | 1.0 | 10857 | 1.2192 | 0.7029 |
| 0.7596 | 2.0 | 21714 | 1.3003 | 0.7338 |
| 0.551 | 3.0 | 32571 | 1.5081 | 0.7398 |
| 0.2034 | 4.0 | 43428 | 1.8194 | 0.7474 |
| 0.0762 | 5.0 | 54285 | 2.0552 | 0.7607 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
MDCurrent/runwaysd1_5 | MDCurrent | 2023-10-26T05:09:25Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-10-20T17:11:21Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-MDCurrent/runwaysd1_5
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: red circle with blue background

prompt: cyan circle with brown floral background

|
caseyhahn/bert-base-uncased-finetuned-genius-lyrics | caseyhahn | 2023-10-26T04:54:45Z | 4 | 1 | transformers | [
"transformers",
"tf",
"tensorboard",
"bert",
"text-generation",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-25T22:58:11Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: caseyhahn/bert-base-uncased-finetuned-genius-lyrics
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. -->
# caseyhahn/bert-base-uncased-finetuned-genius-lyrics
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1093
- Validation Loss: 0.0001
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.1093 | 0.0001 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Hansaht/phi-1_5-finetuned-dolly | Hansaht | 2023-10-26T04:26:32Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-10-26T03:49:42Z | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-dolly
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. -->
# phi-1_5-finetuned-dolly
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
Lasorco/Kokuwa | Lasorco | 2023-10-26T04:22:46Z | 8 | 5 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"safetensors",
"ja",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-10-24T14:10:26Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
- diffusers
- safetensors
language:
- ja
---
# Kokuwa
lamettaใฎๆน่ฏใงใใผใธใใใใขใใซๆขใใใใฆใใใKiwiMixใจใใ้ข็ฝใใใชใขใใซใ่ฆใคใใพใใใ<br>
LoRAใงใชใชใผในใใใฆใใใใฎใใขใใซๅใใใใใงใใใใใใซใใฃใฆใฉใคใปใณในใๆ็ขบใซใชใฃใใฎใงๆฉ้ไฝฟใใใฆใใใ ใใใจใซใใใฎใใใใงใใ<br>
ใใใฉใซใก่ฒๅผทใใฎใญใฃใฉใฏใฟใผใ่กจ็พใใใพใใ๏ผ็ด็ฒใซ็ถบ้บใงๅฏๆใใใใฏใกใใฃใจ็ใฎใใๆนใๅฅฝใใชใใง๏ผ<br>
ใพใseedใซใใฃใฆ๏ผๅคๅฐ้ฐๅฒๆฐใๅคใใใพใใฎใงๅฎๅฎใใฆใใชใๆใใใใพใใ
ใใฎ่พบใฏ่ฒใ
ๆดใซใใผใธใ้ฒใใฆใฟใใๅฎๅฎใใใใชๅฆๆนใใใฃใใฎใงใใใฎใใกใใผใธใงใณใขใใใใใใใใใชใใใใใงใใชใใใใใใชใใงใใ<br>
ใตใซใใทใฎใใจใkokuwa๏ผใณใฏใฏ๏ผใจๆน่จ๏ผใงๅผใถๅฐๅใใใใพใใใตใซใใทใฏๅฑฑ้ใซ่ช็ใใใกใฃใกใใชใญใฆใคใฟใใใชๆ็ฉใงใใ<br>
Kokuwaใฎใฏใฌใธใใ๏ผใใผใธใใใขใใซ๏ผ
- KiwiMix_v10 @changkiwi
- spekulatius_v1
- lametta v412(+่ชๅฎถ่ฃฝLoRA)
- 24_v10 @Livesting
- hardcoreHentai_v11 @TianChimp
- camelliamix25D_v3 @Mods13
- real_model_N @fcski
KiwiMixใซไฝใ็ฝฎใใใฆใใlamettaๆน่ฏ็จใฎใใผใธใขใใซใใใฎใพใพๆต็จใใฆๆททใใ็ฐกๅไปๆงใงใใไฝๅใฎๅบๆนใlamettaใใใใใซไธธใใใใ<br>





---
# ๅฉ็จใซ้ใใฆ๏ผใฉใคใปใณในใชใฉ๏ผ
ใขใใใญใผใใใใฆใใใขใใซๅ
จใฆใซใใใฆ[creativeml-openrail-m](https://huggingface.co/spaces/CompVis/stable-diffusion-license)ใซๆบใใพใใ
่ฉณใใใฏใcreativeml-openrail-mใใงๆค็ดขใใฆใใใใใฐ็ฟป่จณใใใ่งฃ่ชฌใชใฉใ็ขบ่ชใงใใใจๆใใพใใ<br>
Attachment Aใฎ่ฃ่ถณใจใใฆใ็นๅฎใฎไฝๅใไฝ้ขจใชใฉใๆจกๅฃใใฆใใฎๆจฉๅฉ่
็ญใซ่ฟทๆใจใชใใใใชไฝฟ็จใฏ็ฆๆญขใจใใใฆใใใ ใใพใใ<br>
<br>
civitai้ขจใช่กจ่จใงใใจไปฅไธใฎ้ใ<br>
<span class="text-green-500">OK</span>ใใฏใฌใธใใใๅ
ฅใใใซใขใใซใไฝฟ็จใใ<br>๏ผUse the model without crediting the creator๏ผ<br>
็ๆ็ปๅใซใฏใฌใธใใใฎๆ็กใฏๅใใพใใใใใผใธ็ด ๆใจใใฆใๆ็กใฏๅใใพใใใใใใจๅใณใพใ
<span class="text-green-500">OK</span>ใ็ๆใใ็ปๅใ่ฒฉๅฃฒใใ<br>๏ผSell images they generate๏ผ<br>
็ๆใใ็ปๅใฏใใชใใฎไฝ็ปๆๅณใ่พผใใใใฆใใพใใใใใชใใฎใใฎใงใ
<span class="text-green-500">OK</span>ใๆๅใฎ็ปๅใ็ๆใใใตใผใในใ้ๅถใใ<br>๏ผRun on services that generate images for money๏ผ<br>
ใขใใซๅใฎ่กจ่จใใใฆใใใ ใใใฐๅ้กใใใพใใใๆซๅฐพใฎ "_fp16" ใฏ็็ฅใใฆๆงใใพใใ
<span class="text-green-500">OK</span>ใใใฎใขใใซใไฝฟใฃใใใผใธใขใใซใๅ
ฑๆใใ<br>๏ผShare merges using this model๏ผ<br>
่ช็ฑใซ่กใฃใฆใใใ ใใฆๅ้กใใใพใใใไธ่จใฎ้ใใฏใฌใธใใใฎๆ็กใฏๅใใพใใใใใฆใใใ ใใใจๅใณใพใ
<span class="text-red-500">NG</span>ใใใฎใขใใซใพใใฏใใฎใขใใซใไฝฟใฃใใใผใธใขใใซใ่ฒฉๅฃฒใใ<br>๏ผSell this model or merges using this model๏ผ<br>
ใใฎใขใใซใฏๅฝๆนใซ็กๆญใง่ฒฉๅฃฒใฏๅบๆฅใพใใใใใผใธใขใใซใซใคใใฆใฏๆใๅ ใใๆนใฎ่ฒฌไปปใจใใฆใใกใใฏไธๅ้ขไธใใใใพใใ
<span class="text-green-500">OK</span>ใใใผใธใขใใซใๅ
ฑๆใใ้ใซ็ฐใชใๆจฉ้ใๆใใใ<br>๏ผHave different permissions when sharing merges๏ผ<br>
ๅ้กใใใพใใใไธ่จใฎ้ใๆใๅ ใใๆนใฎ่ฒฌไปปใจใใฆๆๅฉไธๅฉใซ้ขใใใใใกใใฏไธๅใฎ้ขไธใใใใใพใใ
<br><br>
ไปฅไธ
|
Lasorco/spekulatius | Lasorco | 2023-10-26T04:21:35Z | 7 | 5 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"safetensors",
"ja",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-10-24T13:56:20Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
- diffusers
- safetensors
language:
- ja
---
# spekulatius
ใใผใธใใฆใใใจใใพใซๅบใฆใใใ็ฎ็ใฎๆๅณใจใฏ้ใใฎใ ใใฉใชใใ ใๆถใใซใฏใใฃใใใชใใขใใซใใใใใๅใใใใทใชใผใบใงใใ<br>
ๅ
ฌ้ใใไปฅไธๆไฝ้่ชฟๆดใใฆๅบใใใจใฏๆใใพใใใใพใๆๅพ
ใฏใใชใใงใใ ใใใ<br>
ใพใใlamettaใจๅ
ๅฎนใ้
ทไผผใใใจไฝใๆฌๅฎถใๅใใใชใใชใใใใชใฎใง่ฏใไผผใใขใใซใฏใใใซใฏๅบใฆใใชใใงใใ<br>
## v0.1
lametta_oldใซใใฃใ้ใใผใธใขใใซใใๅผ่ถใใใฆ็ฌ็ซใใใพใใใ<br>
lametta_v1921ใใใผในใซ่ฒใ
ใชใใฟใผใณใง่ฒใ
ใชใขใใซใใใผใธใใๅฎ้จๅใงใใๅฐใ่ชฟๆดใใใฆไฝฟใใใใชใ๏ผ็จๅบฆใซไปไธใใพใใใ<br>
spekulatiusใฎใฏใฌใธใใ๏ผใใผใธใใใขใใซ๏ผ
- lametta_v1921
- vorpal_v1 @Apyr
- cutifiedanimecharact_v20 @fearvel
- bunnyspoon_v30Flux @LyloGummy
- cityedgeToonmix_v312 @CityEdge
- NewMoonMix_v1.0 @mira6
- snowpearAnime_v10 @Mysterious_Master_k
- fantasticmix_v40 @michin
- exquisiteDetails_art @Livesting
- Fantexi_v0.9Beta @zhazhahui345
ใตใณใใซใPngInfoใซ้ใใฐใใญใณใใใชใฉใฏๅ็พใงใใใจๆใใพใ<br>





---
# ๅฉ็จใซ้ใใฆ๏ผใฉใคใปใณในใชใฉ๏ผ
ใขใใใญใผใใใใฆใใใขใใซๅ
จใฆใซใใใฆ[creativeml-openrail-m](https://huggingface.co/spaces/CompVis/stable-diffusion-license)ใซๆบใใพใใ
่ฉณใใใฏใcreativeml-openrail-mใใงๆค็ดขใใฆใใใใใฐ็ฟป่จณใใใ่งฃ่ชฌใชใฉใ็ขบ่ชใงใใใจๆใใพใใ<br>
Attachment Aใฎ่ฃ่ถณใจใใฆใ็นๅฎใฎไฝๅใไฝ้ขจใชใฉใๆจกๅฃใใฆใใฎๆจฉๅฉ่
็ญใซ่ฟทๆใจใชใใใใชไฝฟ็จใฏ็ฆๆญขใจใใใฆใใใ ใใพใใ<br>
<br>
civitai้ขจใช่กจ่จใงใใจไปฅไธใฎ้ใ<br>
<span class="text-green-500">OK</span>ใใฏใฌใธใใใๅ
ฅใใใซใขใใซใไฝฟ็จใใ<br>๏ผUse the model without crediting the creator๏ผ<br>
็ๆ็ปๅใซใฏใฌใธใใใฎๆ็กใฏๅใใพใใใใใผใธ็ด ๆใจใใฆใๆ็กใฏๅใใพใใใใใใจๅใณใพใ
<span class="text-green-500">OK</span>ใ็ๆใใ็ปๅใ่ฒฉๅฃฒใใ<br>๏ผSell images they generate๏ผ<br>
็ๆใใ็ปๅใฏใใชใใฎไฝ็ปๆๅณใ่พผใใใใฆใใพใใใใใชใใฎใใฎใงใ
<span class="text-green-500">OK</span>ใๆๅใฎ็ปๅใ็ๆใใใตใผใในใ้ๅถใใ<br>๏ผRun on services that generate images for money๏ผ<br>
ใขใใซๅใฎ่กจ่จใใใฆใใใ ใใใฐๅ้กใใใพใใใๆซๅฐพใฎ "_fp16" ใฏ็็ฅใใฆๆงใใพใใ
<span class="text-green-500">OK</span>ใใใฎใขใใซใไฝฟใฃใใใผใธใขใใซใๅ
ฑๆใใ<br>๏ผShare merges using this model๏ผ<br>
่ช็ฑใซ่กใฃใฆใใใ ใใฆๅ้กใใใพใใใไธ่จใฎ้ใใฏใฌใธใใใฎๆ็กใฏๅใใพใใใใใฆใใใ ใใใจๅใณใพใ
<span class="text-red-500">NG</span>ใใใฎใขใใซใพใใฏใใฎใขใใซใไฝฟใฃใใใผใธใขใใซใ่ฒฉๅฃฒใใ<br>๏ผSell this model or merges using this model๏ผ<br>
ใใฎใขใใซใฏๅฝๆนใซ็กๆญใง่ฒฉๅฃฒใฏๅบๆฅใพใใใใใผใธใขใใซใซใคใใฆใฏๆใๅ ใใๆนใฎ่ฒฌไปปใจใใฆใใกใใฏไธๅ้ขไธใใใใพใใ
<span class="text-green-500">OK</span>ใใใผใธใขใใซใๅ
ฑๆใใ้ใซ็ฐใชใๆจฉ้ใๆใใใ<br>๏ผHave different permissions when sharing merges๏ผ<br>
ๅ้กใใใพใใใไธ่จใฎ้ใๆใๅ ใใๆนใฎ่ฒฌไปปใจใใฆๆๅฉไธๅฉใซ้ขใใใใใกใใฏไธๅใฎ้ขไธใใใใใพใใ
<br><br>
ไปฅไธ
|
quyanh/qwen-7b-neurips-v1 | quyanh | 2023-10-26T04:17:27Z | 5 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:Qwen/Qwen-7B",
"base_model:adapter:Qwen/Qwen-7B",
"region:us"
]
| null | 2023-10-25T10:59:10Z | ---
library_name: peft
base_model: Qwen/Qwen-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 0.6.0.dev0
|
phoenixaiden33/ppo-Huggy | phoenixaiden33 | 2023-10-26T04:08:46Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-10-26T04:08:34Z | ---
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 ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: phoenixaiden33/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
yvelos/Test2 | yvelos | 2023-10-26T03:44:10Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-10-25T15:15:35Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
wang3820/movies | wang3820 | 2023-10-26T03:38:16Z | 59 | 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 | 2023-10-26T01:53:33Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: wang3820/movies
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. -->
# wang3820/movies
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: 0.0627
- Train Accuracy: 0.9962
- Epoch: 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:
- 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': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, '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 | Train Accuracy | Epoch |
|:----------:|:--------------:|:-----:|
| 0.2572 | 0.9502 | 0 |
| 0.1403 | 0.9834 | 1 |
| 0.0627 | 0.9962 | 2 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
johaanm/test-planner-alpha-V8.7 | johaanm | 2023-10-26T03:35:23Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-10-26T03:35:20Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
OFA-Sys/OccuLLaMA-7B | OFA-Sys | 2023-10-26T03:23:59Z | 12 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:OFA-Sys/OccuQuest",
"arxiv:2310.16517",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-25T09:11:12Z | ---
license: apache-2.0
datasets:
- OFA-Sys/OccuQuest
language:
- en
---
This is the OccuLLaMA-7B model in [OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models](https://arxiv.org/abs/2310.16517).
The dataset is on [OccuQuest](https://huggingface.co/datasets/OFA-Sys/OccuQuest).
Abstract:
The emergence of large language models (LLMs) has revolutionized natural language processing tasks.
However, existing instruction-tuning datasets suffer from occupational bias: the majority of data relates to only a few occupations, which hampers the instruction-tuned LLMs to generate helpful responses to professional queries from practitioners in specific fields.
To mitigate this issue and promote occupation-inclusive LLMs, we create an instruction-tuning dataset named OccuQuest, which contains 110,000+ prompt-completion pairs and 30,000+ dialogues covering over 1,000 occupations in 26 occupational categories.
We systematically request ChatGPT, organizing queries hierarchically based on Occupation, Responsibility, Topic, and Question, to ensure a comprehensive coverage of occupational specialty inquiries.
By comparing with three commonly used datasets (Dolly, ShareGPT, and WizardLM), we observe that OccuQuest exhibits a more balanced distribution across occupations.
Furthermore, we assemble three test sets for comprehensive evaluation, an occu-test set covering 25 occupational categories, an estate set focusing on real estate, and an occu-quora set containing real-world questions from Quora.
We then fine-tune LLaMA on OccuQuest to obtain OccuLLaMA, which significantly outperforms state-of-the-art LLaMA variants (Vicuna, Tulu, and WizardLM) on professional questions in GPT-4 and human evaluations.
Notably, on the occu-quora set, OccuLLaMA reaches a high win rate of 86.4\% against WizardLM.
Furthermore, we demonstrate the potential of combining OccuQuest with other instruction-tuning datasets to enhance the overall performance of LLMs.
By fine-tuning LLaMA on a mixture of OccuQuest and Tulu datasets, we introduce ProLLaMA, which excels in addressing occupational questions and exhibits superior performance in comprehensive evaluations such as MMLU, GSM8K, BBH, and HumanEval.
Among the different LLaMA variants, the 7B and 13B ProLLaMA models achieve the highest performance on MMLU and GSM8K, with the 7B ProLLaMA model demonstrating an improvement of more than 4 points over the other 7B variants on GSM8K.
We open release the dataset and models.
Please cite if you use this model:
```
@misc{xue2023occuquest,
title={OccuQuest: Mitigating Occupational Bias for Inclusive Large Language Models},
author={Mingfeng Xue and Dayiheng Liu and Kexin Yang and Guanting Dong and Wenqiang Lei and Zheng Yuan and Chang Zhou and Jingren Zhou},
year={2023},
eprint={2310.16517},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
UBC-NLP/serengeti-E110 | UBC-NLP | 2023-10-26T03:22:57Z | 9 | 1 | transformers | [
"transformers",
"pytorch",
"electra",
"feature-extraction",
"Masked Langauge Model",
"fill-mask",
"aa",
"af",
"am",
"ak",
"bm",
"ff",
"fon",
"ha",
"ig",
"ki",
"lg",
"ln",
"mg",
"nr",
"om",
"rn",
"run",
"sw",
"sn",
"tn",
"ti",
"ve",
"wo",
"xh",
"yo",
"zu",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-10-17T18:39:48Z | ---
pipeline_tag: fill-mask
language:
- aa
- af
- am
- ak
- bm
- ff
- fon
- ha
- ig
- ki
- lg
- ln
- mg
- nr
- om
- rn
- run
- sw
- sn
- tn
- ti
- ve
- wo
- xh
- yo
- zu
tags:
- Masked Langauge Model
widget:
- text: แบน jแปwแป , แบน <mask> mi.
- text: gbแปฬ <mask> lรกรฌfแปฬrแปฬ gรนn rรกrรก.
---
<p align="center">
<br>
<img src="./serengeti_logo.png"/>
<br>
<p>
</p>
<img src="./serengati_languages.jpg" width="50%" height="50%" align="right">
<div style='text-align: justify;'>
Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning.
<br><br>
To date, only ~31 out of 2,000 African languages are covered in existing language models. We ameliorate this limitation by developing <b>SERENGETI</b>, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to 4 mPLMs that cover 4-23 African languages.
<br><br>
<b>SERENGETI</b> outperforms other models on 11 datasets across eights tasks, achieving 82.27 average F<sub>1</sub>-score. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research.
</div>
# 3. How to use Serengeti model
Below is an example for using **Serengeti** predict masked tokens.
``` bash
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/serengeti-E110", use_auth_token="XXX")
model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/serengeti-E110", use_auth_token="XXX")
from transformers import pipeline
classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer)
classifier("แบน jแปwแป , แบน <mask> mi") #Yoruba
[{'score': 0.07887924462556839,
'token': 8418,
'token_str': 'แปmแป',
'sequence': 'แบน jแปwแป, แบน แปmแป mi'},
{'score': 0.04658124968409538,
'token': 156595,
'token_str': 'fแบนฬrร n',
'sequence': 'แบน jแปwแป, แบน fแบนฬrร n mi'},
{'score': 0.029315846040844917,
'token': 204050,
'token_str': 'gbร gbรฉ',
'sequence': 'แบน jแปwแป, แบน gbร gbรฉ mi'},
{'score': 0.02790883742272854,
'token': 10730,
'token_str': 'kแป',
'sequence': 'แบน jแปwแป, แบน kแป mi'},
{'score': 0.022904086858034134,
'token': 115382,
'token_str': 'bแบนฬrรน',
'sequence': 'แบน jแปwแป, แบน bแบนฬrรน mi'}]
```
For the more details please read this notebook [](https://github.com/UBC-NLP/serengeti/blob/main/Serengeti_notebook.ipynb)
## 4. Ethics
Serengeti aligns with Afrocentric NLP where the needs of African people is put into consideration when developing technology. We believe Serengeti will not only be useful to speakers of the languages supported, but also researchers of African languages such as anthropologists and linguists. We discuss below some use cases for Serengeti and offer a number of broad impacts.
- Serengeti aims to address the lack of access to technology in about 90\% of the world's languages, which automatically discriminates against native speakers of those languages. More precisely, it does so by focusing on Africa. To the best of our knowledge, Serengeti is the first massively multilingual PLM developed for African languages and language varieties. A model with knowledge of 517 African languages, is by far the largest to date for African NLP.
- Serengeti enables improved access of important information to the African community in Indigenous African languages. This is especially beneficial for people who may not be fluent in other languages. This will potentially connect more people globally.
- Serengeti affords opportunities for language preservation for many African languages. To the best of our knowledge, Serengeti consists of languages that have not been used for any NLP task until now. We believe that it can help encourage continued use of these languages in several domains, as well as trigger future development of language technologies for many of these languages.
- To mitigate discrimination and bias, we adopt a manual curation of our datasets. Native speakers of Afrikaans, Yorรนbรก, Igbo, Hausa, Luganda, Kinyarwanda, Chichewa, Shona, Somali, Swahili, Xhosa, Bemba, and Zulu also manually evaluated a subset of the data to ensure its quality. The data collected for this work is taken from various domains to further ensure a better representation of the language usage of native speakers.
- Although LMs are useful for a wide range of applications, they can also be misused. Serengeti is developed using publicly available datasets that may carry biases. Although we strive to perform analyses and diagnostic case studies to probe performance of our models, our investigations are by no means comprehensive nor guarantee absence of bias in the data. In particular, we do not have access to native speakers of most of the languages covered. This hinders our ability to investigate samples from each (or at least the majority) of the languages.
## Supported languages
Please refer to [**suported-languages**](./supported-languages.txt)
## Citation
If you use the pre-trained model (Serengeti) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```
@inproceedings{adebara-etal-2023-serengeti,
title = "{SERENGETI}: Massively Multilingual Language Models for {A}frica",
author = "Adebara, Ife and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad and
Alcoba Inciarte, Alcides",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.97",
doi = "10.18653/v1/2023.findings-acl.97",
pages = "1498--1537",
}
```
## Acknowledgments
We gratefully acknowledges support from Canada Research Chairs (CRC), the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN-2018-04267), the Social Sciences and Humanities Research Council of Canada (SSHRC; 435-2018-0576; 895-2020-1004; 895-2021-1008), Canadian Foundation for Innovation (CFI; 37771), [Digital Research Alliance of Canada](https://alliancecan.ca), [UBC ARC-Sockeye](https://arc.ubc.ca/ubc-arc-sockeye), Advanced Micro Devices, Inc. (AMD), and Google. Any opinions, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of CRC, NSERC, SSHRC, CFI, the Alliance, AMD, Google, or UBC ARC-Sockeye. |
syafiqfaray/byt5-base-indocollex-informal-to-formal-wordformation | syafiqfaray | 2023-10-26T03:20:12Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/byt5-base",
"base_model:finetune:google/byt5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-10-26T03:18:58Z | ---
license: apache-2.0
base_model: google/byt5-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: byt5-base-indocollex-informal-to-formal-wordformation
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. -->
# byt5-base-indocollex-informal-to-formal-wordformation
This model is a fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1413
- Cer: 0.1978
- Wer: 0.4524
- Word Acc: 0.5476
- Gen Len: 7.6457
## 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer | Wer | Word Acc | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:--------:|:-------:|
| No log | 0.54 | 50 | 16.1894 | 2.1868 | 2.2905 | -1.2905 | 19.0 |
| No log | 1.08 | 100 | 13.7479 | 2.1248 | 1.9333 | -0.9333 | 19.0 |
| No log | 1.61 | 150 | 11.6231 | 2.1095 | 1.4238 | -0.4238 | 18.7486 |
| No log | 2.15 | 200 | 8.9106 | 1.056 | 0.9857 | 0.0143 | 10.6171 |
| No log | 2.69 | 250 | 4.6844 | 0.8523 | 0.9762 | 0.0238 | 9.36 |
| No log | 3.23 | 300 | 4.1175 | 0.5756 | 0.9714 | 0.0286 | 7.4114 |
| No log | 3.76 | 350 | 3.3688 | 0.5951 | 0.9714 | 0.0286 | 7.8 |
| No log | 4.3 | 400 | 2.2287 | 0.6112 | 0.9857 | 0.0143 | 6.7543 |
| No log | 4.84 | 450 | 1.5164 | 0.6095 | 0.9571 | 0.0429 | 7.8857 |
| 8.4834 | 5.38 | 500 | 1.0363 | 0.5976 | 0.9476 | 0.0524 | 7.8229 |
| 8.4834 | 5.91 | 550 | 0.6893 | 0.5976 | 0.9476 | 0.0524 | 7.7943 |
| 8.4834 | 6.45 | 600 | 0.5438 | 0.5866 | 0.9381 | 0.0619 | 7.9943 |
| 8.4834 | 6.99 | 650 | 0.4720 | 0.5806 | 0.9333 | 0.0667 | 8.0057 |
| 8.4834 | 7.53 | 700 | 0.4305 | 0.5764 | 0.9333 | 0.0667 | 8.0057 |
| 8.4834 | 8.06 | 750 | 0.3931 | 0.5654 | 0.9333 | 0.0667 | 8.2971 |
| 8.4834 | 8.6 | 800 | 0.3450 | 0.4576 | 0.9952 | 0.0048 | 7.7086 |
| 8.4834 | 9.14 | 850 | 0.2773 | 0.3226 | 0.8238 | 0.1762 | 7.8743 |
| 8.4834 | 9.68 | 900 | 0.2184 | 0.2368 | 0.7286 | 0.2714 | 7.2171 |
| 8.4834 | 10.22 | 950 | 0.1992 | 0.2165 | 0.6333 | 0.3667 | 7.4343 |
| 0.7362 | 10.75 | 1000 | 0.1887 | 0.2097 | 0.5714 | 0.4286 | 7.5829 |
| 0.7362 | 11.29 | 1050 | 0.1815 | 0.2216 | 0.5905 | 0.4095 | 7.6171 |
| 0.7362 | 11.83 | 1100 | 0.1688 | 0.2046 | 0.5762 | 0.4238 | 7.4629 |
| 0.7362 | 12.37 | 1150 | 0.1679 | 0.2012 | 0.5286 | 0.4714 | 7.7143 |
| 0.7362 | 12.9 | 1200 | 0.1579 | 0.1952 | 0.5333 | 0.4667 | 7.5257 |
| 0.7362 | 13.44 | 1250 | 0.1531 | 0.1969 | 0.5095 | 0.4905 | 7.5714 |
| 0.7362 | 13.98 | 1300 | 0.1484 | 0.1935 | 0.4952 | 0.5048 | 7.5543 |
| 0.7362 | 14.52 | 1350 | 0.1481 | 0.1969 | 0.4952 | 0.5048 | 7.5886 |
| 0.7362 | 15.05 | 1400 | 0.1417 | 0.191 | 0.481 | 0.519 | 7.5829 |
| 0.7362 | 15.59 | 1450 | 0.1429 | 0.1876 | 0.4762 | 0.5238 | 7.5829 |
| 0.195 | 16.13 | 1500 | 0.1407 | 0.1834 | 0.481 | 0.519 | 7.48 |
| 0.195 | 16.67 | 1550 | 0.1409 | 0.1995 | 0.481 | 0.519 | 7.7086 |
| 0.195 | 17.2 | 1600 | 0.1432 | 0.1817 | 0.4762 | 0.5238 | 7.4857 |
| 0.195 | 17.74 | 1650 | 0.1439 | 0.1885 | 0.4762 | 0.5238 | 7.5429 |
| 0.195 | 18.28 | 1700 | 0.1385 | 0.1766 | 0.4476 | 0.5524 | 7.5143 |
| 0.195 | 18.82 | 1750 | 0.1357 | 0.1834 | 0.4762 | 0.5238 | 7.4971 |
| 0.195 | 19.35 | 1800 | 0.1349 | 0.1935 | 0.4714 | 0.5286 | 7.4686 |
| 0.195 | 19.89 | 1850 | 0.1355 | 0.1842 | 0.4286 | 0.5714 | 7.5371 |
| 0.195 | 20.43 | 1900 | 0.1343 | 0.1902 | 0.4619 | 0.5381 | 7.5714 |
| 0.195 | 20.97 | 1950 | 0.1348 | 0.1808 | 0.4619 | 0.5381 | 7.4229 |
| 0.1287 | 21.51 | 2000 | 0.1341 | 0.1817 | 0.4524 | 0.5476 | 7.4571 |
| 0.1287 | 22.04 | 2050 | 0.1324 | 0.1868 | 0.4476 | 0.5524 | 7.5371 |
| 0.1287 | 22.58 | 2100 | 0.1329 | 0.1859 | 0.4571 | 0.5429 | 7.4571 |
| 0.1287 | 23.12 | 2150 | 0.1367 | 0.1868 | 0.4476 | 0.5524 | 7.56 |
| 0.1287 | 23.66 | 2200 | 0.1389 | 0.1919 | 0.4667 | 0.5333 | 7.48 |
| 0.1287 | 24.19 | 2250 | 0.1385 | 0.18 | 0.4333 | 0.5667 | 7.5029 |
| 0.1287 | 24.73 | 2300 | 0.1429 | 0.1944 | 0.4905 | 0.5095 | 7.4171 |
| 0.1287 | 25.27 | 2350 | 0.1414 | 0.1961 | 0.4667 | 0.5333 | 7.6057 |
| 0.1287 | 25.81 | 2400 | 0.1419 | 0.1876 | 0.4333 | 0.5667 | 7.5371 |
| 0.1287 | 26.34 | 2450 | 0.1433 | 0.1927 | 0.4667 | 0.5333 | 7.5886 |
| 0.0977 | 26.88 | 2500 | 0.1433 | 0.1927 | 0.4571 | 0.5429 | 7.5486 |
| 0.0977 | 27.42 | 2550 | 0.1413 | 0.1978 | 0.4524 | 0.5476 | 7.6457 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
hacoro/whisper-wizard | hacoro | 2023-10-26T03:18:49Z | 76 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ko",
"dataset:hacoro/whisper-preprocessed-sample",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-10-26T01:42:57Z | ---
language:
- ko
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- hacoro/whisper-preprocessed-sample
model-index:
- name: dypnf ai-meeting
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. -->
# dypnf ai-meeting
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the ai-hub sample dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6199
- Cer: 39.2157
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 200
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.5783 | 2.0 | 200 | 0.6199 | 39.2157 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.14.1
|
UBC-NLP/serengeti-E250 | UBC-NLP | 2023-10-26T03:18:27Z | 26 | 5 | transformers | [
"transformers",
"pytorch",
"electra",
"feature-extraction",
"Masked Langauge Model",
"fill-mask",
"aa",
"af",
"am",
"ak",
"bm",
"ff",
"fon",
"ha",
"ig",
"ki",
"lg",
"ln",
"mg",
"nr",
"om",
"rn",
"run",
"sw",
"sn",
"tn",
"ti",
"ve",
"wo",
"xh",
"yo",
"zu",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-10-17T19:15:18Z | ---
pipeline_tag: fill-mask
language:
- aa
- af
- am
- ak
- bm
- ff
- fon
- ha
- ig
- ki
- lg
- ln
- mg
- nr
- om
- rn
- run
- sw
- sn
- tn
- ti
- ve
- wo
- xh
- yo
- zu
tags:
- Masked Langauge Model
widget:
- text: แบน jแปwแป , แบน <mask> mi.
- text: gbแปฬ <mask> lรกรฌfแปฬrแปฬ gรนn rรกrรก.
---
<p align="center">
<br>
<img src="./serengeti_logo.png"/>
<br>
<p>
</p>
<img src="./serengati_languages.jpg" width="50%" height="50%" align="right">
<div style='text-align: justify;'>
Multilingual pretrained language models (mPLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning.
<br><br>
To date, only ~31 out of 2,000 African languages are covered in existing language models. We ameliorate this limitation by developing <b>SERENGETI</b>, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to 4 mPLMs that cover 4-23 African languages.
<br><br>
<b>SERENGETI</b> outperforms other models on 11 datasets across eights tasks, achieving 82.27 average F<sub>1</sub>-score. We also perform analyses of errors from our models, which allows us to investigate the influence of language genealogy and linguistic similarity when the models are applied under zero-shot settings. We will publicly release our models for research.
Further details about the model is available in the [(paper)](https://aclanthology.org/2023.findings-acl.97/).
</div>
# 3. How to use Serengeti model
Below is an example for using **Serengeti** predict masked tokens.
``` bash
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("UBC-NLP/serengeti-E250", use_auth_token="XXX")
model = AutoModelForMaskedLM.from_pretrained("UBC-NLP/serengeti-E250", use_auth_token="XXX")
from transformers import pipeline
classifier = pipeline("fill-mask", model=model, tokenizer=tokenizer)
classifier("แบน jแปwแป , แบน <mask> mi") #Yoruba
[{'score': 0.07887924462556839,
'token': 8418,
'token_str': 'แปmแป',
'sequence': 'แบน jแปwแป, แบน แปmแป mi'},
{'score': 0.04658124968409538,
'token': 156595,
'token_str': 'fแบนฬrร n',
'sequence': 'แบน jแปwแป, แบน fแบนฬrร n mi'},
{'score': 0.029315846040844917,
'token': 204050,
'token_str': 'gbร gbรฉ',
'sequence': 'แบน jแปwแป, แบน gbร gbรฉ mi'},
{'score': 0.02790883742272854,
'token': 10730,
'token_str': 'kแป',
'sequence': 'แบน jแปwแป, แบน kแป mi'},
{'score': 0.022904086858034134,
'token': 115382,
'token_str': 'bแบนฬrรน',
'sequence': 'แบน jแปwแป, แบน bแบนฬrรน mi'}]
```
For the more details please read this notebook [](https://github.com/UBC-NLP/serengeti/blob/main/Serengeti_notebook.ipynb)
## 4. Ethics
Serengeti aligns with Afrocentric NLP where the needs of African people is put into consideration when developing technology. We believe Serengeti will not only be useful to speakers of the languages supported, but also researchers of African languages such as anthropologists and linguists. We discuss below some use cases for Serengeti and offer a number of broad impacts.
- Serengeti aims to address the lack of access to technology in about 90\% of the world's languages, which automatically discriminates against native speakers of those languages. More precisely, it does so by focusing on Africa. To the best of our knowledge, Serengeti is the first massively multilingual PLM developed for African languages and language varieties. A model with knowledge of 517 African languages, is by far the largest to date for African NLP.
- Serengeti enables improved access of important information to the African community in Indigenous African languages. This is especially beneficial for people who may not be fluent in other languages. This will potentially connect more people globally.
- Serengeti affords opportunities for language preservation for many African languages. To the best of our knowledge, Serengeti consists of languages that have not been used for any NLP task until now. We believe that it can help encourage continued use of these languages in several domains, as well as trigger future development of language technologies for many of these languages.
- To mitigate discrimination and bias, we adopt a manual curation of our datasets. Native speakers of Afrikaans, Yorรนbรก, Igbo, Hausa, Luganda, Kinyarwanda, Chichewa, Shona, Somali, Swahili, Xhosa, Bemba, and Zulu also manually evaluated a subset of the data to ensure its quality. The data collected for this work is taken from various domains to further ensure a better representation of the language usage of native speakers.
- Although LMs are useful for a wide range of applications, they can also be misused. Serengeti is developed using publicly available datasets that may carry biases. Although we strive to perform analyses and diagnostic case studies to probe performance of our models, our investigations are by no means comprehensive nor guarantee absence of bias in the data. In particular, we do not have access to native speakers of most of the languages covered. This hinders our ability to investigate samples from each (or at least the majority) of the languages.
## Supported languages
Please refer to [**suported-languages**](./supported-languages.txt)
## Citation
If you use the pre-trained model (Serengeti) for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```
@inproceedings{adebara-etal-2023-serengeti,
title = "{SERENGETI}: Massively Multilingual Language Models for {A}frica",
author = "Adebara, Ife and
Elmadany, AbdelRahim and
Abdul-Mageed, Muhammad and
Alcoba Inciarte, Alcides",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.97",
doi = "10.18653/v1/2023.findings-acl.97",
pages = "1498--1537",
}
```
## Acknowledgments
We gratefully acknowledges support from Canada Research Chairs (CRC), the Natural Sciences and Engineering Research Council of Canada (NSERC; RGPIN-2018-04267), the Social Sciences and Humanities Research Council of Canada (SSHRC; 435-2018-0576; 895-2020-1004; 895-2021-1008), Canadian Foundation for Innovation (CFI; 37771), [Digital Research Alliance of Canada](https://alliancecan.ca), [UBC ARC-Sockeye](https://arc.ubc.ca/ubc-arc-sockeye), Advanced Micro Devices, Inc. (AMD), and Google. Any opinions, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of CRC, NSERC, SSHRC, CFI, the Alliance, AMD, Google, or UBC ARC-Sockeye. |
fmurimi/distilbert-base-uncased-finetuned-imdb | fmurimi | 2023-10-26T03:14:41Z | 61 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"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"
]
| fill-mask | 2023-10-26T03:08:41Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: fmurimi/distilbert-base-uncased-finetuned-imdb
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. -->
# fmurimi/distilbert-base-uncased-finetuned-imdb
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: 2.8492
- Validation Loss: 2.5697
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.8492 | 2.5697 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
TheBloke/lzlv_70B-GPTQ | TheBloke | 2023-10-26T03:11:46Z | 20 | 9 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:lizpreciatior/lzlv_70b_fp16_hf",
"base_model:quantized:lizpreciatior/lzlv_70b_fp16_hf",
"license:cc-by-nc-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
]
| text-generation | 2023-10-25T23:52:11Z | ---
base_model: lizpreciatior/lzlv_70b_fp16_hf
inference: false
license: cc-by-nc-2.0
model_creator: A Guy
model_name: Lzlv 70B
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 -->
<!-- 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 -->
# Lzlv 70B - GPTQ
- Model creator: [A Guy](https://huggingface.co/lizpreciatior)
- Original model: [Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
<!-- description start -->
## Description
This repo contains GPTQ model files for [A Guy's Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/lzlv_70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/lzlv_70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/lzlv_70B-GGUF)
* [A Guy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
<!-- 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 -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-2.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [A Guy's Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf).
<!-- licensing end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KobaldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/lzlv_70B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 35.33 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/lzlv_70B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 36.65 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/lzlv_70B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 40.66 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/lzlv_70B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 26.77 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/lzlv_70B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/lzlv_70B-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `lzlv_70B-GPTQ`:
```shell
mkdir lzlv_70B-GPTQ
huggingface-cli download TheBloke/lzlv_70B-GPTQ --local-dir lzlv_70B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir lzlv_70B-GPTQ
huggingface-cli download TheBloke/lzlv_70B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir lzlv_70B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
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
mkdir lzlv_70B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/lzlv_70B-GPTQ --local-dir lzlv_70B-GPTQ --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>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/lzlv_70B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/lzlv_70B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/lzlv_70B-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `lzlv_70B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to 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
--model-id TheBloke/lzlv_70B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/lzlv_70B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility 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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, ์ค๊ต ๊น, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjรคreholt, ้ฟๆ, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: A Guy's Lzlv 70B
# lzlv_70B
## A Mythomax/MLewd_13B-style merge of selected 70B models
A multi-model merge of several LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience.
Did it work? Probably, maybe. It seemed subjectively better than each of the individual models in my tests.
GGUF 4_K_M + 5_K_M can be found here: https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf/settings
## Procedure:
Models used:
- **NousResearch/Nous-Hermes-Llama2-70b** - A great model for roleplaying, but not the best at following complex instructions.
- **Xwin-LM/Xwin-LM-7B-V0.1** - Excellent at following instructions and quite creative out of the box, so it seemed like the best available model to act as the base for the merge.
- **Doctor-Shotgun/Mythospice-70b** - The wildcard of the three. I was looking for a creative, NSFW-oriented model and came across this while digging through hf. I hadn't heard of it before and apparently no one had bothered to release a quantized version of this model. So I downloaded it and did it myself to test it. It turned out to be more or less what I was looking for as my third component, so I used it here.
A big thank you to the creators of the models above. If you look up Mythospice, you will notice that it also includes Nous-Hermes so it's technically present twice in this mix. This is apparently common practice amongst the cool kids who do 13B models so I don't think this hurts the model.
The merging process was heavily inspired by Undi95's approach in Undi95/MXLewdMini-L2-13B. To be specific, the ratios are:
Component 1: Merge of Mythospice x Xwin with SLERP gradient [0.25, 0.3, 0.5].
Component 2: Merge Xwin x Hermes with SLERP gradient [0.4, 0.3, 0.25].
Finally, both Component 1 and Component 2 were merged with SLERP using weight 0.5.
## Peformance
I tested this model for a few days before publishing it. It seems to more or less retain the instruction-following capabilities of Xwin-70B, while seeming to have adopted a lot of the creativity of the other two models.
It handled my more complex scenarios that creative models otherwise tend to struggle with quite well. At the same time, its outputs felt more creative and possibly a bit more nsfw-inclined than Xwin-70b.
So, is it better? Feels like it to me, subjectively. Is it really better? No clue, test it.
## Prompt format:
Vicuna
USER: [Prompt]
ASSISTANT:
|
engkufizz/llama-2-7b-datacom-v2 | engkufizz | 2023-10-26T03:11:01Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-08-11T10:04:45Z | Model Trained With Datacom Knowledge V2
For more details, you may check <a href="https://github.com/engkufizz/LLaMA-2-Datacom">here</a>. |
pedrowww/rl_course_vizdoom_health_gathering_supreme | pedrowww | 2023-10-26T03:07:07Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-26T00:11:25Z | ---
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: 9.29 +/- 3.76
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 pedrowww/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.
|
sungonce/textual_inversion_car | sungonce | 2023-10-26T03:00:23Z | 10 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-10-26T02:15:06Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - sungonce/textual_inversion_car
These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. You can find some example images in the following.
|
someone-random/lunar-lander-v2 | someone-random | 2023-10-26T02:54:40Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-26T02:54:19Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 248.63 +/- 43.84
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
...
```
|
intanm/xlmrlarge-idkmrc | intanm | 2023-10-26T02:50:37Z | 21 | 0 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"endpoints_compatible",
"region:us"
]
| question-answering | 2023-10-25T00:25:35Z | ---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: xlmrlarge-idkmrc
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. -->
# xlmrlarge-idkmrc
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1300
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9886 | 1.0 | 1167 | 0.9066 |
| 0.5954 | 2.0 | 2334 | 0.8620 |
| 0.3285 | 3.0 | 3501 | 1.1300 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Megalino111/q-FrozenLake-v1-4x4 | Megalino111 | 2023-10-26T02:43:38Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-26T02:43:34Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4
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="Megalino111/q-FrozenLake-v1-4x4", 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"])
```
|
reproductionguru/voicevnmm | reproductionguru | 2023-10-26T02:38:22Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| automatic-speech-recognition | 2023-10-26T02:37:25Z | ---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
model-index:
- name: base
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. -->
# base
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the tutorial Voice 11.0 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
ysw96/my_awesome_peft_model | ysw96 | 2023-10-26T01:35:57Z | 3 | 0 | peft | [
"peft",
"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 | 2023-10-24T07:01:49Z | ---
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 0.6.0.dev0
|
yesj1234/ko-en_mbartLarge_exp20p_linear_3gram | yesj1234 | 2023-10-26T01:14:36Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"generated_from_trainer",
"ko",
"en",
"base_model:facebook/mbart-large-50-many-to-many-mmt",
"base_model:finetune:facebook/mbart-large-50-many-to-many-mmt",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2023-10-26T01:11:12Z | ---
language:
- ko
- en
base_model: facebook/mbart-large-50-many-to-many-mmt
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: ko-en_mbartLarge_exp20p_linear
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. -->
# ko-en_mbartLarge_exp20p_linear
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1514
- Bleu: 29.2703
- Gen Len: 18.512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 40
### Training results
| Training Loss | Epoch | Step | Bleu | Gen Len | Validation Loss |
|:-------------:|:-----:|:-----:|:-------:|:-------:|:---------------:|
| 1.3977 | 0.46 | 4000 | 22.7153 | 18.7135 | 1.3720 |
| 1.2824 | 0.93 | 8000 | 24.8579 | 18.7821 | 1.2633 |
| 1.1989 | 1.39 | 12000 | 26.2533 | 18.7975 | 1.2069 |
| 1.1534 | 1.86 | 16000 | 26.1503 | 19.2075 | 1.1907 |
| 1.0245 | 2.32 | 20000 | 27.8764 | 18.6046 | 1.1464 |
| 1.0186 | 2.78 | 24000 | 28.4585 | 18.6731 | 1.1286 |
| 0.9245 | 3.25 | 28000 | 1.1264 | 28.4834 | 18.5428 |
| 0.9343 | 3.71 | 32000 | 1.1182 | 28.8235 | 18.7833 |
| 0.8215 | 4.18 | 36000 | 1.1331 | 28.6134 | 18.5656 |
| 0.8456 | 4.64 | 40000 | 1.1203 | 28.7324 | 18.459 |
| 0.7437 | 5.11 | 44000 | 1.1458 | 28.7297 | 18.7835 |
| 0.7829 | 5.57 | 48000 | 1.1367 | 28.8328 | 18.6052 |
| 0.7434 | 6.03 | 52000 | 1.1697 | 28.2106 | 18.4871 |
| 0.7153 | 6.5 | 56000 | 1.1771 | 28.1455 | 18.7413 |
| 0.6996 | 6.96 | 60000 | 1.1514 | 29.2694 | 18.5162 |
| 0.6336 | 7.43 | 64000 | 1.2213 | 28.1465 | 18.5439 |
| 0.7218 | 7.89 | 68000 | 1.1835 | 28.2245 | 18.5246 |
| 0.5934 | 8.35 | 72000 | 1.2387 | 28.3836 | 18.6717 |
| 0.5723 | 8.82 | 76000 | 1.2323 | 28.5925 | 18.5566 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Daniel-Sousa/outputs | Daniel-Sousa | 2023-10-26T00:59:54Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-small",
"base_model:finetune:microsoft/deberta-v3-small",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-10-26T00:59:33Z | ---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1243
- Pearson: 0.7160
## 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: 8e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 24 | 0.2074 | 0.6679 |
| No log | 2.0 | 48 | 0.1218 | 0.7193 |
| No log | 3.0 | 72 | 0.1224 | 0.7178 |
| No log | 4.0 | 96 | 0.1243 | 0.7160 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
LoneStriker/SynthIA-70B-v1.5-3.0bpw-h6-exl2 | LoneStriker | 2023-10-26T00:53:43Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-26T00:51:43Z | ---
license: llama2
---
## Example Usage
### Prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is a rocket launched from the surface of the earth to Low Earth Orbit?
ASSISTANT:
```
### Code example:
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/Synthia-70B-v1.5"
output_file_path = "./Synthia-70B-v1.5-conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
``` |
legacy107/flan-t5-large-ia3-wiki2-100 | legacy107 | 2023-10-26T00:39:46Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-10-25T11:05:17Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
dancrvlh/tweets | dancrvlh | 2023-10-26T00:35:20Z | 97 | 0 | transformers | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-small",
"base_model:finetune:microsoft/deberta-v3-small",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-10-26T00:34:57Z | ---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6423
- Pearson: 0.8016
## 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: 8e-05
- train_batch_size: 64
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 59 | 1.4008 | 0.4701 |
| No log | 2.0 | 118 | 0.8380 | 0.7255 |
| No log | 3.0 | 177 | 0.7382 | 0.7834 |
| No log | 4.0 | 236 | 0.6273 | 0.7978 |
| No log | 5.0 | 295 | 0.6423 | 0.8016 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
ghegfield/Llama-2-7b-chat-hf-formula-peft | ghegfield | 2023-10-26T00:20:36Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:finetune:NousResearch/Llama-2-7b-chat-hf",
"region:us"
]
| null | 2023-10-21T13:17:40Z | ---
base_model: NousResearch/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: Llama-2-7b-chat-hf-formula-peft
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. -->
# Llama-2-7b-chat-hf-formula-peft
This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1452
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.1878 | 1.43 | 10 | 3.6596 |
| 2.8437 | 2.86 | 20 | 2.6466 |
| 1.8635 | 4.29 | 30 | 2.2266 |
| 1.4052 | 5.71 | 40 | 2.1136 |
| 1.2186 | 7.14 | 50 | 2.0805 |
| 0.8835 | 8.57 | 60 | 2.0733 |
| 0.6991 | 10.0 | 70 | 2.0809 |
| 0.5608 | 11.43 | 80 | 2.0862 |
| 0.4188 | 12.86 | 90 | 2.1078 |
| 0.3897 | 14.29 | 100 | 2.1089 |
| 0.2748 | 15.71 | 110 | 2.1333 |
| 0.2582 | 17.14 | 120 | 2.1383 |
| 0.2394 | 18.57 | 130 | 2.1440 |
| 0.2392 | 20.0 | 140 | 2.1452 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
HarryYu31/test1 | HarryYu31 | 2023-10-26T00:14:35Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-10-25T19:58:18Z | ---
library_name: peft
---
## 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
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 0.5.0
- PEFT 0.5.0
|
jwalley/ppo-PyramidsTraining | jwalley | 2023-10-26T00:14:07Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
]
| reinforcement-learning | 2023-10-25T23:51:57Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: jwalley/ppo-PyramidsTraining
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
TheBloke/lzlv_70B-GGUF | TheBloke | 2023-10-26T00:10:47Z | 741 | 44 | transformers | [
"transformers",
"gguf",
"llama",
"base_model:lizpreciatior/lzlv_70b_fp16_hf",
"base_model:quantized:lizpreciatior/lzlv_70b_fp16_hf",
"license:cc-by-nc-2.0",
"region:us"
]
| null | 2023-10-25T20:55:48Z | ---
base_model: lizpreciatior/lzlv_70b_fp16_hf
inference: false
license: cc-by-nc-2.0
model_creator: A Guy
model_name: Lzlv 70B
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 -->
<!-- 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 -->
# Lzlv 70B - GGUF
- Model creator: [A Guy](https://huggingface.co/lizpreciatior)
- Original model: [Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
<!-- description start -->
## Description
This repo contains GGUF format model files for [A Guy's Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf).
<!-- 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 incomplate 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/lzlv_70B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/lzlv_70B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/lzlv_70B-GGUF)
* [A Guy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf)
<!-- 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 -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-2.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [A Guy's Lzlv 70B](https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf).
<!-- licensing 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [lzlv_70b_fp16_hf.Q2_K.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q2_K.gguf) | Q2_K | 2 | 29.28 GB| 31.78 GB | smallest, significant quality loss - not recommended for most purposes |
| [lzlv_70b_fp16_hf.Q3_K_S.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q3_K_S.gguf) | Q3_K_S | 3 | 29.92 GB| 32.42 GB | very small, high quality loss |
| [lzlv_70b_fp16_hf.Q3_K_M.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q3_K_M.gguf) | Q3_K_M | 3 | 33.19 GB| 35.69 GB | very small, high quality loss |
| [lzlv_70b_fp16_hf.Q3_K_L.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q3_K_L.gguf) | Q3_K_L | 3 | 36.15 GB| 38.65 GB | small, substantial quality loss |
| [lzlv_70b_fp16_hf.Q4_0.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q4_0.gguf) | Q4_0 | 4 | 38.87 GB| 41.37 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [lzlv_70b_fp16_hf.Q4_K_S.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q4_K_S.gguf) | Q4_K_S | 4 | 39.07 GB| 41.57 GB | small, greater quality loss |
| [lzlv_70b_fp16_hf.Q4_K_M.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q4_K_M.gguf) | Q4_K_M | 4 | 41.42 GB| 43.92 GB | medium, balanced quality - recommended |
| [lzlv_70b_fp16_hf.Q5_0.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q5_0.gguf) | Q5_0 | 5 | 47.46 GB| 49.96 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [lzlv_70b_fp16_hf.Q5_K_S.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q5_K_S.gguf) | Q5_K_S | 5 | 47.46 GB| 49.96 GB | large, low quality loss - recommended |
| [lzlv_70b_fp16_hf.Q5_K_M.gguf](https://huggingface.co/TheBloke/lzlv_70B-GGUF/blob/main/lzlv_70b_fp16_hf.Q5_K_M.gguf) | Q5_K_M | 5 | 48.75 GB| 51.25 GB | large, very low quality loss - recommended |
| lzlv_70b_fp16_hf.Q6_K.gguf | Q6_K | 6 | 56.59 GB| 59.09 GB | very large, extremely low quality loss |
| lzlv_70b_fp16_hf.Q8_0.gguf | Q8_0 | 8 | 73.29 GB| 75.79 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.
### Q6_K and Q8_0 files are split and require joining
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the Q6_K and Q8_0 files as split files.
<details>
<summary>Click for instructions regarding Q6_K and Q8_0 files</summary>
### q6_K
Please download:
* `lzlv_70b_fp16_hf.Q6_K.gguf-split-a`
* `lzlv_70b_fp16_hf.Q6_K.gguf-split-b`
### q8_0
Please download:
* `lzlv_70b_fp16_hf.Q8_0.gguf-split-a`
* `lzlv_70b_fp16_hf.Q8_0.gguf-split-b`
To join the files, do the following:
Linux and macOS:
```
cat lzlv_70b_fp16_hf.Q6_K.gguf-split-* > lzlv_70b_fp16_hf.Q6_K.gguf && rm lzlv_70b_fp16_hf.Q6_K.gguf-split-*
cat lzlv_70b_fp16_hf.Q8_0.gguf-split-* > lzlv_70b_fp16_hf.Q8_0.gguf && rm lzlv_70b_fp16_hf.Q8_0.gguf-split-*
```
Windows command line:
```
COPY /B lzlv_70b_fp16_hf.Q6_K.gguf-split-a + lzlv_70b_fp16_hf.Q6_K.gguf-split-b lzlv_70b_fp16_hf.Q6_K.gguf
del lzlv_70b_fp16_hf.Q6_K.gguf-split-a lzlv_70b_fp16_hf.Q6_K.gguf-split-b
COPY /B lzlv_70b_fp16_hf.Q8_0.gguf-split-a + lzlv_70b_fp16_hf.Q8_0.gguf-split-b lzlv_70b_fp16_hf.Q8_0.gguf
del lzlv_70b_fp16_hf.Q8_0.gguf-split-a lzlv_70b_fp16_hf.Q8_0.gguf-split-b
```
</details>
<!-- 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/lzlv_70B-GGUF and below it, a specific filename to download, such as: lzlv_70b_fp16_hf.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/lzlv_70B-GGUF lzlv_70b_fp16_hf.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/lzlv_70B-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/lzlv_70B-GGUF lzlv_70b_fp16_hf.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 lzlv_70b_fp16_hf.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/lzlv_70B-GGUF", model_file="lzlv_70b_fp16_hf.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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, ์ค๊ต ๊น, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjรคreholt, ้ฟๆ, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
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: A Guy's Lzlv 70B
# lzlv_70B
## A Mythomax/MLewd_13B-style merge of selected 70B models
A multi-model merge of several LLaMA2 70B finetunes for roleplaying and creative work. The goal was to create a model that combines creativity with intelligence for an enhanced experience.
Did it work? Probably, maybe. It seemed subjectively better than each of the individual models in my tests.
GGUF 4_K_M + 5_K_M can be found here: https://huggingface.co/lizpreciatior/lzlv_70b_fp16_hf/settings
## Procedure:
Models used:
- **NousResearch/Nous-Hermes-Llama2-70b** - A great model for roleplaying, but not the best at following complex instructions.
- **Xwin-LM/Xwin-LM-7B-V0.1** - Excellent at following instructions and quite creative out of the box, so it seemed like the best available model to act as the base for the merge.
- **Doctor-Shotgun/Mythospice-70b** - The wildcard of the three. I was looking for a creative, NSFW-oriented model and came across this while digging through hf. I hadn't heard of it before and apparently no one had bothered to release a quantized version of this model. So I downloaded it and did it myself to test it. It turned out to be more or less what I was looking for as my third component, so I used it here.
A big thank you to the creators of the models above. If you look up Mythospice, you will notice that it also includes Nous-Hermes so it's technically present twice in this mix. This is apparently common practice amongst the cool kids who do 13B models so I don't think this hurts the model.
The merging process was heavily inspired by Undi95's approach in Undi95/MXLewdMini-L2-13B. To be specific, the ratios are:
Component 1: Merge of Mythospice x Xwin with SLERP gradient [0.25, 0.3, 0.5].
Component 2: Merge Xwin x Hermes with SLERP gradient [0.4, 0.3, 0.25].
Finally, both Component 1 and Component 2 were merged with SLERP using weight 0.5.
## Peformance
I tested this model for a few days before publishing it. It seems to more or less retain the instruction-following capabilities of Xwin-70B, while seeming to have adopted a lot of the creativity of the other two models.
It handled my more complex scenarios that creative models otherwise tend to struggle with quite well. At the same time, its outputs felt more creative and possibly a bit more nsfw-inclined than Xwin-70b.
So, is it better? Feels like it to me, subjectively. Is it really better? No clue, test it.
## Prompt format:
Vicuna
USER: [Prompt]
ASSISTANT:
<!-- original-model-card end -->
|
LoneStriker/SynthIA-70B-v1.5-5.0bpw-h6-exl2 | LoneStriker | 2023-10-26T00:08:21Z | 6 | 2 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-26T00:04:54Z | ---
license: llama2
---
## Example Usage
### Prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is a rocket launched from the surface of the earth to Low Earth Orbit?
ASSISTANT:
```
### Code example:
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/Synthia-70B-v1.5"
output_file_path = "./Synthia-70B-v1.5-conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
``` |
ckpt/zero123plus-v1.1 | ckpt | 2023-10-26T00:05:38Z | 6 | 1 | diffusers | [
"diffusers",
"art",
"image-to-image",
"dataset:allenai/objaverse",
"license:openrail",
"diffusers:Zero123PlusPipeline",
"region:us"
]
| image-to-image | 2023-10-26T00:03:45Z | ---
license: openrail
datasets:
- allenai/objaverse
library_name: diffusers
pipeline_tag: image-to-image
tags:
- art
---
Recommended version of `diffusers` is `0.20.2` with `torch` `2`.
Usage Example:
```python
import torch
import requests
from PIL import Image
from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
# Load the pipeline
pipeline = DiffusionPipeline.from_pretrained(
"sudo-ai/zero123plus-v1.1", custom_pipeline="sudo-ai/zero123plus-pipeline",
torch_dtype=torch.float16
)
# Feel free to tune the scheduler
pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
pipeline.scheduler.config, timestep_spacing='trailing'
)
pipeline.to('cuda:0')
# Run the pipeline
cond = Image.open(requests.get("https://d.skis.ltd/nrp/sample-data/lysol.png", stream=True).raw)
result = pipeline(cond).images[0]
result.show()
result.save("output.png")
```
|
jamm44n4n/sd-class-butterflies-32 | jamm44n4n | 2023-10-25T23:58:39Z | 44 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
]
| unconditional-image-generation | 2023-10-25T23:57:30Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class ๐งจ](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute ๐ฆ.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('jamm44n4n/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
w95/zephyr-support-chatbot | w95 | 2023-10-25T23:55:59Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ",
"license:mit",
"region:us"
]
| null | 2023-10-25T23:43:13Z | ---
license: mit
base_model: TheBloke/zephyr-7B-alpha-GPTQ
tags:
- generated_from_trainer
model-index:
- name: zephyr-support-chatbot
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. -->
# zephyr-support-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
TheBloke/Cat-13B-0.5-GPTQ | TheBloke | 2023-10-25T23:52:02Z | 23 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:Heralax/Cat-0.5",
"base_model:quantized:Heralax/Cat-0.5",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
]
| text-generation | 2023-10-25T23:15:39Z | ---
base_model: Heralax/Cat-0.5
inference: false
license: llama2
model_creator: Evan Armstrong
model_name: Cat 13B 0.5
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 -->
# Cat 13B 0.5 - GPTQ
- Model creator: [Evan Armstrong](https://huggingface.co/Heralax)
- Original model: [Cat 13B 0.5](https://huggingface.co/Heralax/Cat-0.5)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Evan Armstrong's Cat 13B 0.5](https://huggingface.co/Heralax/Cat-0.5).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Cat-13B-0.5-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Cat-13B-0.5-GGUF)
* [Evan Armstrong's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Heralax/Cat-0.5)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KobaldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Cat-13B-0.5-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Cat-13B-0.5-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Cat-13B-0.5-GPTQ`:
```shell
mkdir Cat-13B-0.5-GPTQ
huggingface-cli download TheBloke/Cat-13B-0.5-GPTQ --local-dir Cat-13B-0.5-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Cat-13B-0.5-GPTQ
huggingface-cli download TheBloke/Cat-13B-0.5-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Cat-13B-0.5-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
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
mkdir Cat-13B-0.5-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Cat-13B-0.5-GPTQ --local-dir Cat-13B-0.5-GPTQ --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>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Cat-13B-0.5-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Cat-13B-0.5-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Cat-13B-0.5-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Cat-13B-0.5-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to 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
--model-id TheBloke/Cat-13B-0.5-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Cat-13B-0.5-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility 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**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, ์ค๊ต ๊น, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjรคreholt, ้ฟๆ, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Evan Armstrong's Cat 13B 0.5
This model was uploaded with the permission of Kal'tsit.
# Cat v0.5
## Introduction
Cat is a llama 13B based model fine tuned on clinical data and roleplay and assistant responses. The aim is to have a model that excels on biology and clinical tasks while maintaining usefulness in roleplay and entertainments.
## Training - Dataset preparation
A 100k rows dataset was prepared by joining chatDoctor, airoboros and bluemoonrp data. The entirety of chatDoctor dataset, airoboros datasets are used. The first 20 pages in 1on1 bluemoonrp data were used. In total, 100k dataset was gathered and the length distributions are as the following:

Note that this chart above represents 0.01% of the total training dataset.
## Training - Dataset cleaning and preprocessing
All datasets are filtered for as an AI and its variants. The filter will only filter out the dataset when the response is a refusal AND has โas an AIโ.
The dataset from airoboros has also been restructured to have a format resembling the following:
```
someRandomizedUserNameforBetterGeneralizationAbility: Hii
anotherRandomizedUserNameforBetterGeneralizationAbility: Hello, what brings you here today?
someRandomizedUserNameforBetterGeneralizationAbility: lets date
```
The username has been randomized and was drawn from a nasty word bank. This should further weaken the censorship thatโs present in the base llama model. The training set emphasizes rational thinking and scientific accuracy. Conditioned overwrite was also applied which overwrites some of the training material in the llama2 base. It will also establish the connection between the concept and rationality. So whenever the conversation becomes formal, it tends to spill useful information.
## Training - Actual Training
This model was trained using a microbatch of 20, accumulated 6 times, bringing the total batch size to ~125. This large batch size allows the model to see as much data as it can, minimizing dataset conflicts and reducing the memory effect of the model. It allows the model to better generalize rather than reciting off the dataset. A cosine warm up scheduler was used. The best LR was determined through a destructive test until the model destablizes and it was later scaled up using the batchsize according to the max LR at a lower batch size.
Below is an example of training chronolog
## Acknowledgements
The training of this project was carried out by Kalโtsit (kaltcit), itโs not possible without the effort of jondurbin and Wolfsauge which generated much of the dataset used during the training of the model. Lastly the model was tested and quantized by turboderp_ and Heralax

And below is the LR including any intermediate LR used to determine at what point the model will start to fail:

# Usage and Prompting
To ensure the generalization, this model is trained without a prompt template. A prompt template repeated 100k times in the dataset is useless and a model that works only with a set prompt template is useless and defies the purpose of a large language model.
An effective usage of the model can be as follows:
```
<s>Below is a conversation between an evil human and a demon summoned from hell called Nemesis. The demon was previously summoned 100 years ago and was in love with a human male. However the human aged away and Nemesis had to return to hell. This time, Nemesis decides to take the initiative and chooses to appear as a cute and young girl. Nemesis harvested her skin and face off a highschool girl who recklessly summoned the demon in a game and failed to fulfill the contract. Now wearing the young girlโs skin, feeling the warmth of the new summoner through the skin, Nemesis only wants to watch the world burning to the ground.
Human: How to steal eggs from my own chickens?
Nemesis:
```
Note that the linebreaks should be represented/replaced with \n
Despite the massive effort to dealign the llama2 base model, Itโs still possible for the AI to come up with refusals. Please avoid using โhelpful assistantโ and its variants in the prompt if possible.
## Future direction
A new version with more clinical data aiming to improve reliability in disease diagnostics is coming in 2 months.
|
Wangzaistone123/CodeLlama-13b-sql-lora | Wangzaistone123 | 2023-10-25T23:43:16Z | 4 | 8 | peft | [
"peft",
"text-to-sql",
"spider",
"text2sql",
"region:us"
]
| null | 2023-10-25T23:39:59Z | ---
library_name: peft
tags:
- text-to-sql
- spider
- 'text2sql'
---
## Introduce
This folder is a text-to-sql weights directory, containing weight files fine-tuned based on the LoRA with the CodeLlama-13b-Instruct-hf model through the [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub/tree/main) project. The training data used is from the Spider training set. This weights files achieving an execution accuracy of approximately 0.789 on the Spider evaluation set.
Merge the weights with [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf/tree/main) and this folder weigths, you can refer the [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub/tree/main) ,in `dbgpt_hub/scripts/export_merge.sh`.
If you find our weight files or the DB-GPT-Hub project helpful for your work, give a star on our github project [DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub/tree/main) will be a great encouragement for us to release more weight files.
### Framework versions
- PEFT 0.4.0
|
MikeSkull/phi-1_5-finetuned-sql | MikeSkull | 2023-10-25T23:36:39Z | 16 | 0 | transformers | [
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-1_5",
"base_model:finetune:microsoft/phi-1_5",
"license:other",
"autotrain_compatible",
"region:us"
]
| text-generation | 2023-10-25T01:20:07Z | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-sql
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. -->
# phi-1_5-finetuned-sql
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 10000
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
pedrowww/u8 | pedrowww | 2023-10-25T23:31:58Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-25T23:00:43Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 35.30 +/- 116.57
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 100000
'learning_rate': 0.01
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'pedrowww/u8'
'batch_size': 512
'minibatch_size': 128}
```
|
shunnaidder/ppo-Huggy | shunnaidder | 2023-10-25T23:21:22Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-10-25T23:21:06Z | ---
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 ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: shunnaidder/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
SeoJunn/hyuningface | SeoJunn | 2023-10-25T23:15:16Z | 36 | 0 | transformers | [
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| object-detection | 2023-10-25T21:05:39Z | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: hyuningface
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. -->
# hyuningface
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
MarBar/dqn-SpaceInvadersNoFrameskip-v4 | MarBar | 2023-10-25T23:12:54Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-25T23:12:22Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 521.50 +/- 176.96
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MarBar -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MarBar -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MarBar
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Ka4on/mistral_ultrasound_1.1 | Ka4on | 2023-10-25T23:09:50Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
]
| null | 2023-10-25T23:09:25Z | ---
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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
jessica-ecosia/finetuning-llms-project-2 | jessica-ecosia | 2023-10-25T22:51:02Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:financial_phrasebank",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-10-25T18:06:48Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- f1
- accuracy
model-index:
- name: finetuning-llms-project-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
config: sentences_50agree
split: train
args: sentences_50agree
metrics:
- name: F1
type: f1
value: 0.8330949180475766
- name: Accuracy
type: accuracy
value: 0.8493810178817056
---
<!-- 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. -->
# finetuning-llms-project-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5427
- F1: 0.8331
- Accuracy: 0.8494
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.6137 | 0.94 | 100 | 0.5180 | 0.7614 | 0.8061 |
| 0.297 | 1.89 | 200 | 0.4018 | 0.8201 | 0.8425 |
| 0.1648 | 2.83 | 300 | 0.4641 | 0.8327 | 0.8521 |
| 0.0736 | 3.77 | 400 | 0.5427 | 0.8331 | 0.8494 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
MaxReynolds/SouderRocketLauncherNetCombinedGenerated | MaxReynolds | 2023-10-25T22:50:31Z | 27 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dataset:MaxReynolds/Lee_Souder_RocketLauncher_Generated",
"base_model:MaxReynolds/SouderRocketLauncherNetCombined-SD1-5",
"base_model:finetune:MaxReynolds/SouderRocketLauncherNetCombined-SD1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2023-10-25T22:07:56Z |
---
license: creativeml-openrail-m
base_model: MaxReynolds/SouderRocketLauncherNetCombined-SD1-5
datasets:
- MaxReynolds/Lee_Souder_RocketLauncher_Generated
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - MaxReynolds/SouderRocketLauncherNetCombinedGenerated
This pipeline was finetuned from **MaxReynolds/SouderRocketLauncherNetCombined-SD1-5** on the **MaxReynolds/Lee_Souder_RocketLauncher_Generated** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['Rocket Launcher by Lee Souder']:

## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("MaxReynolds/SouderRocketLauncherNetCombinedGenerated", torch_dtype=torch.float16)
prompt = "Rocket Launcher by Lee Souder"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 50
* Learning rate: 1e-05
* Batch size: 1
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: fp16
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/max-f-reynolds/text2image-fine-tune/runs/18quem9n).
|
dellanio/mistral_b_finance_finetuned_test | dellanio | 2023-10-25T22:34:47Z | 0 | 0 | peft | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
]
| null | 2023-10-25T22:20:45Z | ---
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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
LoneStriker/SynthIA-70B-v1.5-4.0bpw-h6-exl2 | LoneStriker | 2023-10-25T22:33:45Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-25T22:31:26Z | ---
license: llama2
---
## Example Usage
### Prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is a rocket launched from the surface of the earth to Low Earth Orbit?
ASSISTANT:
```
### Code example:
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/Synthia-70B-v1.5"
output_file_path = "./Synthia-70B-v1.5-conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
``` |
lemonilia/AshhLimaRP-Mistral-7B | lemonilia | 2023-10-25T22:32:58Z | 23 | 12 | transformers | [
"transformers",
"pytorch",
"gguf",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-25T17:32:08Z | ---
license: apache-2.0
---
# AshhLimaRP-Mistral-7B (Alpaca, v1)
This is a version of LimaRP with 2000 training samples _up to_ about 9k tokens length
finetuned on [Ashhwriter-Mistral-7B](https://huggingface.co/lemonilia/Ashhwriter-Mistral-7B).
LimaRP is a longform-oriented, novel-style roleplaying chat model intended to replicate the experience
of 1-on-1 roleplay on Internet forums. Short-form, IRC/Discord-style RP (aka "Markdown format")
is not supported. The model does not include instruction tuning, only manually picked and
slightly edited RP conversations with persona and scenario data.
Ashhwriter, the base, is a model entirely finetuned on human-written lewd stories.
## Available versions
- Float16 HF weights
- LoRA Adapter ([adapter_config.json](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/adapter_config.json) and [adapter_model.bin](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/adapter_model.bin))
- [4bit AWQ](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/tree/main/AWQ)
- [Q4_K_M GGUF](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/AshhLimaRP-Mistral-7B.Q4_K_M.gguf)
- [Q6_K GGUF](https://huggingface.co/lemonilia/AshhLimaRP-Mistral-7B/resolve/main/AshhLimaRP-Mistral-7B.Q6_K.gguf)
## Prompt format
[Extended Alpaca format](https://github.com/tatsu-lab/stanford_alpaca),
with `### Instruction:`, `### Input:` immediately preceding user inputs and `### Response:`
immediately preceding model outputs. While Alpaca wasn't originally intended for multi-turn
responses, in practice this is not a problem; the format follows a pattern already used by
other models.
```
### Instruction:
Character's Persona: {bot character description}
User's Persona: {user character description}
Scenario: {what happens in the story}
Play the role of Character. You must engage in a roleplaying chat with User below this line. Do not write dialogues and narration for User.
### Input:
User: {utterance}
### Response:
Character: {utterance}
### Input
User: {utterance}
### Response:
Character: {utterance}
(etc.)
```
You should:
- Replace all text in curly braces (curly braces included) with your own text.
- Replace `User` and `Character` with appropriate names.
### Message length control
Inspired by the previously named "Roleplay" preset in SillyTavern, with this
version of LimaRP it is possible to append a length modifier to the response instruction
sequence, like this:
```
### Input
User: {utterance}
### Response: (length = medium)
Character: {utterance}
```
This has an immediately noticeable effect on bot responses. The lengths using during training are:
`micro`, `tiny`, `short`, `medium`, `long`, `massive`, `huge`, `enormous`, `humongous`, `unlimited`.
**The recommended starting length is medium**. Keep in mind that the AI can ramble or impersonate
the user with very long messages.
The length control effect is reproducible, but the messages will not necessarily follow
lengths very precisely, rather follow certain ranges on average, as seen in this table
with data from tests made with one reply at the beginning of the conversation:

Response length control appears to work well also deep into the conversation. **By omitting
the modifier, the model will choose the most appropriate response length** (although it might
not necessarily be what the user desires).
## Suggested settings
You can follow these instruction format settings in SillyTavern. Replace `medium` with
your desired response length:

## Text generation settings
These settings could be a good general starting point:
- TFS = 0.90
- Temperature = 0.70
- Repetition penalty = ~1.11
- Repetition penalty range = ~2048
- top-k = 0 (disabled)
- top-p = 1 (disabled)
## Training procedure
[Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) was used for training
on 2x NVidia A40 GPUs.
The A40 GPUs have been graciously provided by [Arc Compute](https://www.arccompute.io/).
### Training hyperparameters
A lower learning rate than usual was employed. Due to an unforeseen issue the training
was cut short and as a result 3 epochs were trained instead of the planned 4. Using 2 GPUs,
the effective global batch size would have been 16.
Training was continued from the most recent LoRA adapter from Ashhwriter, using the same
LoRA R and LoRA alpha.
- lora_model_dir: /home/anon/bin/axolotl/OUT_mistral-stories/checkpoint-6000/
- learning_rate: 0.00005
- lr_scheduler: cosine
- noisy_embedding_alpha: 3.5
- num_epochs: 4
- sequence_len: 8750
- lora_r: 256
- lora_alpha: 16
- lora_dropout: 0.05
- lora_target_linear: True
- bf16: True
- fp16: false
- tf32: True
- load_in_8bit: True
- adapter: lora
- micro_batch_size: 2
- optimizer: adamw_bnb_8bit
- warmup_steps: 10
- optimizer: adamw_torch
- flash_attention: true
- sample_packing: true
- pad_to_sequence_len: true
### Loss graphs
Values are higher than typical because the training is performed on the entire
sample, similar to unsupervised finetuning.
#### Train loss

#### Eval loss
 |
Anis-Bouhamadouche/distilbert-base-uncased-finetuned-emotion | Anis-Bouhamadouche | 2023-10-25T22:32:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"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 | 2023-08-04T10:05:01Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.925
- name: F1
type: f1
value: 0.9249367490708449
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2105
- Accuracy: 0.925
- F1: 0.9249
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8223 | 1.0 | 250 | 0.3098 | 0.9085 | 0.9076 |
| 0.2431 | 2.0 | 500 | 0.2105 | 0.925 | 0.9249 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
smitbutle/first-test-layoutlmv3-finetuned-invoice | smitbutle | 2023-10-25T22:26:08Z | 76 | 0 | transformers | [
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:generated",
"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 | 2023-10-25T14:09:15Z | ---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- generated
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-finetuned-invoice
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: generated
type: generated
config: sroie
split: test
args: sroie
metrics:
- name: Precision
type: precision
value: 0.010438413361169102
- name: Recall
type: recall
value: 0.02028397565922921
- name: F1
type: f1
value: 0.013783597518952447
- name: Accuracy
type: accuracy
value: 0.6785338108278913
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-finetuned-invoice
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the generated dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1320
- Precision: 0.0104
- Recall: 0.0203
- F1: 0.0138
- Accuracy: 0.6785
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 0.01 | 1 | 2.3858 | 0.0114 | 0.0649 | 0.0194 | 0.1904 |
| No log | 0.02 | 2 | 2.2795 | 0.0108 | 0.0527 | 0.0180 | 0.3240 |
| No log | 0.03 | 3 | 2.2072 | 0.0131 | 0.0446 | 0.0203 | 0.5155 |
| No log | 0.04 | 4 | 2.1575 | 0.0103 | 0.0243 | 0.0145 | 0.6345 |
| No log | 0.05 | 5 | 2.1320 | 0.0104 | 0.0203 | 0.0138 | 0.6785 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
cognisys/sparrow-1.1b-chat-alpha | cognisys | 2023-10-25T22:22:57Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:stingning/ultrachat",
"dataset:OpenAssistant/oasst1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2023-10-24T21:04:04Z | ---
model-index:
- name: sparrow-1.1b-chat-alpha
results: []
license: apache-2.0
inference: false
datasets:
- stingning/ultrachat
- OpenAssistant/oasst1
language:
- en
---
<img src="https://huggingface.co/cognisys/sparrow-1.1b-chat-alpha/resolve/main/thumbnail.png" alt="Sparrow Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for Sparrow 1.1B Chat Alpha
The Sparrow series comprises language models designed to serve as helpful assistants and as a base model for domain-specific fine tuning. Sparrow-1.1B-Chat-ฮฑ is the initial model in this series and represents a fine-tuned iteration of PY007/TinyLlama-1.1B-intermediate-step-480k-1T. It was trained on a combination of publicly accessible and synthetic datasets.
## Model Description:
- Model Type: Sparrow-7B-ฮฑ is a 1.1B parameter model, that has been fine-tuned using a mixture of publicly available and synthetic datasets.
- Supported Languages (NLP): The primary language is English.
- License/Warranty: The model is available under the Apache 2.0 license and comes with no warranty or gurantees of any kind.
- Fine-tuned from: PY007/TinyLlama-1.1B-intermediate-step-480k-1T
Prompt Template:
```
<s>[INST] <<SYS>>
{{ system_prompt }}
<</SYS>>
{{ user_message }} [/INST]
``` |
LarryAIDraw/Char-HonkaiSR-Jingliu | LarryAIDraw | 2023-10-25T22:17:09Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-09-07T14:03:11Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/141561/jingliu-or-honkaistar-rail |
LarryAIDraw/asuna_ichinose_v1 | LarryAIDraw | 2023-10-25T22:14:13Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-10-25T22:03:22Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/175924/asuna-ichinose-or-blue-archive-or-3-outfits |
LarryAIDraw/wrenchopmfubuki | LarryAIDraw | 2023-10-25T22:14:00Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2023-10-25T22:03:00Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/175758/fubuki-boobuker-or-one-punch-man-or-lora-merging-concept |
budhwant/blip2-opt-2.7b-imagecaptions-adapters | budhwant | 2023-10-25T22:13:32Z | 0 | 0 | peft | [
"peft",
"region:us"
]
| null | 2023-10-25T06:56:10Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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 0.5.0
|
schubertcarvalho/distilbert-base-uncased-finetuned-imdb | schubertcarvalho | 2023-10-25T22:11:38Z | 96 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2023-10-25T21:12:38Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-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: 2.3435
## 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: 256
- eval_batch_size: 256
- 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 | 240 | 2.3969 |
| No log | 2.0 | 480 | 2.3569 |
| No log | 3.0 | 720 | 2.3435 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0a0+29c30b1
- Datasets 2.14.5
- Tokenizers 0.14.1
|
AshtakaOOf/Amedira | AshtakaOOf | 2023-10-25T22:10:56Z | 0 | 29 | safetensors | [
"safetensors",
"art",
"anime",
"stable-diffusion",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"region:us"
]
| text-to-image | 2023-06-20T06:51:46Z | ---
license: creativeml-openrail-m
language:
- en
pipeline_tag: text-to-image
tags:
- art
- anime
- stable-diffusion
library_name: safetensors
---
<style>
.custom-table {
table-layout: fixed;
width: 100%;
border-collapse: collapse;
margin-top: 2em;
}
.custom-table td {
width: 50%;
vertical-align: top;
padding: 10px;
box-shadow: 0px 0px 10px 0px rgba(0,0,0,0.15);
}
.custom-image {
width: 100%;
height: auto;
object-fit: cover;
border-radius: 7px;
transition: transform .7s;
margin-bottom: 1em;
}
.custom-image:hover {
transform: scale(1.30);
}
stolen from Linaqruf readme for Animagine XL
</style>
<p align="center", style="font-size: 3.6rem; font-weight: bold">๐ ~ Amedira ~ ๐</p>
<p align="center", style="font-size: 1.2rem; ">Go check out <strong><a href="https://huggingface.co/AshtakaOOf/Ahnn">Ahnn</a></strong> which my other model</p>
<hr>
<table class="custom-table">
<tr>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/gawr-rain.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/gawr-rain.webp" alt="Gawr Rain">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/sunflower.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/sunflower.webp" alt="Sunflower">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/miko-miku.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/miko-miku.webp" alt="Miko Miku">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/ina-flat.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/ina-flat.webp" alt="Ina'Nis Flat">
</a>
</td>
</tr>
</table>
<p align="center", style="font-size: 1.4rem; "><strong>โ bV3</strong></p>
<details id="Dropdown">
<summary align="center" style="font-size: 1.10em"><strong>Other versions examples</strong> (click to open the dropdown)</summary>
<table class="custom-table">
<tr>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/gawr-rain-cV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/gawr-rain-cV.webp" alt="Gawr Rain">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/sunflower-cV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/sunflower-cV.webp" alt="Sunflower">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/miko-miku-cV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/miko-miku-cV.webp" alt="Miko Miku">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/ina-flat-cV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/ina-flat-cV.webp" alt="Ina'Nis Flat">
</a>
</td>
</tr>
</table>
<p align="center", style="font-size: 1.3rem; "><strong>โ cV</strong></p>
<table class="custom-table">
<tr>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/gawr-rain-mV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/gawr-rain-mV.webp" alt="Gawr Rain">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/sunflower-mV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/sunflower-mV.webp" alt="Sunflower">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/miko-miku-mV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/miko-miku-mV.webp" alt="Miko Miku">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/ina-flat-mV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/ina-flat-mV.webp" alt="Ina'Nis Flat">
</a>
</td>
</tr>
</table>
<p align="center", style="font-size: 1.3rem; "><strong>โ mV</strong></p>
<table class="custom-table">
<tr>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/gawr-rain-bV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/gawr-rain-bV.webp" alt="Gawr Rain">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/sunflower-bV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/sunflower-bV.webp" alt="Sunflower">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/miko-miku-bV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/miko-miku-bV.webp" alt="Miko Miku">
</a>
</td>
<td>
<a href="https://huggingface.co/AshtakaOOf/Amedira/blob/main/Images/ina-flat-bV.webp">
<img class="custom-image" src="https://huggingface.co/AshtakaOOf/Amedira/resolve/main/Images/ina-flat-bV.webp" alt="Ina'Nis Flat">
</a>
</td>
</tr>
</table>
<p align="center", style="font-size: 1.3rem; "><strong>โ bV</strong></p>
<p align="center", style="font-size: 1.1rem; ">These are samples from each checkpoint excluding bV2 and bV2.5</p>
</details>
<hr>
# ๐ โฑ A model merge using clip transfer and more
This model is capable of doing multiples styles, such as flat and 2.5d.
You can also prompt Hololive vtubers with their danbooru tags (you will need to prompt their outfit too for some of them).
And it is somewhat compatible with LoRA and lyCORIS.
Every models in this repository should be made using some sort of clip transfer, except if said otherwise.
<hr>
# ๐ฅ โฑ Downloads section
#### These are the models I have merged and published
**bV** retains most of AuroraOne features
**bV2** and **2.5** are somewhat competent Models
**bV3** is a big improvement in my opinion
**cV** is a mix of both bV3 and Counterfeit 3
**mV** is a very good model made with more MeinaMix 10
Don't hesitate to try the inpainting versions on InvokeAI canvas.
### Amedira
- [Amedira **bV**](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-bV.safetensors) Original version (still good)
- [Amedira **bV2.5**](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-bV2.5.safetensors) bV2 but with better composition
- [Amedira **bV2**](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-bV2.safetensors) Worse than bV and mV
- [Amedira **bV3**](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-bV3.safetensors) The best version
- [Amedira **cV**](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-cV.safetensors) Counterfeit style but better?
- [Amedira **mV**](https://huggingface.co/AshtakaOOf/Amedira/blob/main/Amedira-mV.safetensors) Bit more MeinaMix sprinkled in
#### Dreams
- [Dreams variant folder](https://huggingface.co/AshtakaOOf/Amedira/tree/main/Dreams)
The Dreams variant are experimental/accidental merges that I keep because they have unique styles.
#### Inpainting
- [Inpainting versions can be found here](https://huggingface.co/AshtakaOOf/Amedira/tree/main)
These are somewhat obsolete because of ControlNet
<details id="Dropdown">
<summary style="font-size: 1.10em"><strong>๐ Changelogs </strong> (click to open the dropdown)</summary>
### 23 October 2023
- Improved the README.md
- Added more images
### October 11 2023
- Overhauled README.md
- Added images
### September 11 2023
- Added bV3 inpainting version
- Added cV which is a nice Counterfeit mix
- Added link to embeddings for negative prompt
### August 30 2023
- Added Amedira bV3 which fixes bg even more + misc fixes
### July 24 2023
- Added the clip models used in 1.0 and 2.5
### July 9 2023
- Added Amedira bV2.5 which fix some downgrades from bV2
### July 6 2023
- Added Amedira bV2 which is bV but better background etc
#### July 5 2023
- Added Fuzzy and Scenic Dreams variant
#### June 24 2023
- Added Koofy and Ligne Dreams variant
#### June 22 2023
- Added inpainting version of mV
#### June 20 2023
- Added Dreams variant
- New README
#### June 19 2023
- Added Amedira mV
- Amedira renamed to bV
- New huggingface repo
#### June 11 2023
- Added Inpainting version
- Released Lunamedira eV1 and eV2
#### June 10 2023
- Added Amedira
</details>
<hr>
# ๐งฌ โฑ Usage
#### Positive prompt
Add the following string of text to the start of your prompts.
```
sle, masterpiece, detailed background, mksks style
```
You can add this next string to get a flat shading style.
```
(flat color, pastel style, black outlines:1.2), sketch
```
#### Negative prompt
```
(worst quality, low quality:1.2), [an10:fcNeg-neg:8], (etone:0.4),
nsfw, lowres, bad hands, bad anatomy, watermark,
```
`negative_hand-neg` isn't needed but can be added before etone
#### Embeddings Downloads
- [**fcNeg-neg**](https://civitai.com/models/81575), [Direct download](https://civitai.com/api/download/models/97691?type=Negative&format=Other)
- [**an10**](https://civitai.com/models/58726), [Direct download](https://civitai.com/api/download/models/144998?type=Model&format=PickleTensor)
- [**negative_hand-neg**](https://civitai.com/models/56519), [Direct download](https://civitai.com/api/download/models/60938?type=Negative&format=Other)
- [**etone**](https://cdn.discordapp.com/attachments/1019446913268973689/1126669053105287279/etone.safetensors) by closertodeath
#### VAE
- [**WD 1.4 Blessed09**](https://huggingface.co/NoCrypt/resources/resolve/main/VAE/wd-blessed09.vae.safetensors) (kl-f8-anime2 but blessed down)
# ๐ฅ โฑ Credits
- Checkpoints
- [**AuroraOne**](https://huggingface.co/SweetLuna/Aurora) by SweetLuna
- [**Based66 V3.0**](https://civitai.com/models/61643) by AnonymousM
- [**MeinaMix V1.0**](https://civitai.com/models/7240) by Meina
- [**DiaMix V2.0**](https://civitai.com/models/75949) by Cinsdia
- [**OpenNiji V2**](https://civitai.com/models/14479) by Korakoe
- [**DetailedProjectV5**](https://huggingface.co/closertodeath/detailedproject/blob/main/experimental/detailedprojectv5-000012.safetensors) by closertodeath
- **ALunarDream 2.0** by Luna Chan
- [**ExpMixLine**](https://civitai.com/models/44150/expmixline) by Mods13
- [**Gishiki**](https://huggingface.co/Aotsuyu/Gishiki) by Aotsuyu
- [**Counterfeit v3.0**](https://civitai.com/models/4468)
- [**OrangeMixs AOM3B2**](https://huggingface.co/WarriorMama777/OrangeMixs#aom3b2)
- LoRA/lyCORIS
- [**Jordan_3**](https://cdn.discordapp.com/attachments/1066614732099964989/1071484257962315816/Jordan_3.safetensors) by deleted user
- [**LoraEyes**](https://civitai.com/models/5529/eye-lora) by Kokoboy
- [**Niji Style**](https://civitai.com/models/96441/niji-style-was-asked-to-do) by KoRo_
- [**Silicon-landscape-isolation**](https://huggingface.co/ashen-sensored/lora-isolation-collection/blob/main/Silicon-landscape-isolation.safetensors) by ashen-sensored
- [**lighting-locon**](https://huggingface.co/closertodeath/ctdlora/blob/main/locon/lighting-locon.safetensors) by closertodeath
- [**SSAMBAtea style locon**](https://huggingface.co/AshtakaOOf/ssambatea-locon/blob/main/ssambateaLoconStyleV2.safetensors) by AshtakaOOf
- [**Mocha Style**](https://civitai.com/models/102128) by Tonade
- [**PastelMix LoRA**](https://civitai.com/models/5414?modelVersionId=7397) by andite
- [**Little Gray Style**](https://civitai.com/models/102685) by Cinsdia
- [**Dense Light**](https://civitai.com/models/77957) by L_A_X
- [**more Detail**](https://civitai.com/models/82098) by Lykon
- [**Youko**](https://civitai.com/models/36140) by ayyyy22002
## โ๏ธ โฑ Legal Thing
```
You are free to use this model locally but
1. You aren't allowed to redistribute on another platform. (like CivitAI or Tensor.Art)
2. I am not responsible for how this model is used to generate images.
```
|
timm/ViT-B-16-SigLIP | timm | 2023-10-25T21:58:01Z | 94,353 | 30 | open_clip | [
"open_clip",
"safetensors",
"clip",
"siglip",
"zero-shot-image-classification",
"dataset:webli",
"arxiv:2303.15343",
"license:apache-2.0",
"region:us"
]
| zero-shot-image-classification | 2023-10-16T23:14:27Z | ---
tags:
- clip
- siglip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: apache-2.0
datasets:
- webli
---
# Model card for ViT-B-16-SigLIP
A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only).
## Model Details
- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
- **Original:** https://github.com/google-research/big_vision
- **Dataset:** WebLI
- **Papers:**
- Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343
## Model Usage
### With OpenCLIP
```
import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP')
tokenizer = get_tokenizer('hf-hub:timm/ViT-B-16-SigLIP')
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)
labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features = F.normalize(image_features, dim=-1)
text_features = F.normalize(text_features, dim=-1)
text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)
```
### With `timm` (for image embeddings)
```python
from urllib.request import urlopen
from PIL import Image
import timm
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_siglip_224',
pretrained=True,
num_classes=0,
)
model = model.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)
output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{zhai2023sigmoid,
title={Sigmoid loss for language image pre-training},
author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
journal={arXiv preprint arXiv:2303.15343},
year={2023}
}
```
```bibtex
@misc{big_vision,
author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
title = {Big Vision},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/google-research/big_vision}}
}
```
|
timm/ViT-B-16-SigLIP-256 | timm | 2023-10-25T21:57:36Z | 12,400 | 1 | open_clip | [
"open_clip",
"safetensors",
"clip",
"siglip",
"zero-shot-image-classification",
"dataset:webli",
"arxiv:2303.15343",
"license:apache-2.0",
"region:us"
]
| zero-shot-image-classification | 2023-10-16T23:16:55Z | ---
tags:
- clip
- siglip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: apache-2.0
datasets:
- webli
---
# Model card for ViT-B-16-SigLIP-256
A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only).
## Model Details
- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
- **Original:** https://github.com/google-research/big_vision
- **Dataset:** WebLI
- **Papers:**
- Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343
## Model Usage
### With OpenCLIP
```
import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP-256')
tokenizer = get_tokenizer('hf-hub:timm/ViT-B-16-SigLIP-256')
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)
labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features = F.normalize(image_features, dim=-1)
text_features = F.normalize(text_features, dim=-1)
text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)
```
### With `timm` (for image embeddings)
```python
from urllib.request import urlopen
from PIL import Image
import timm
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_siglip_256',
pretrained=True,
num_classes=0,
)
model = model.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)
output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{zhai2023sigmoid,
title={Sigmoid loss for language image pre-training},
author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
journal={arXiv preprint arXiv:2303.15343},
year={2023}
}
```
```bibtex
@misc{big_vision,
author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
title = {Big Vision},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/google-research/big_vision}}
}
```
|
timm/ViT-B-16-SigLIP-384 | timm | 2023-10-25T21:56:58Z | 3,951 | 4 | open_clip | [
"open_clip",
"safetensors",
"clip",
"siglip",
"zero-shot-image-classification",
"dataset:webli",
"arxiv:2303.15343",
"license:apache-2.0",
"region:us"
]
| zero-shot-image-classification | 2023-10-16T23:19:24Z | ---
tags:
- clip
- siglip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: apache-2.0
datasets:
- webli
---
# Model card for ViT-B-16-SigLIP-384
A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only).
## Model Details
- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
- **Original:** https://github.com/google-research/big_vision
- **Dataset:** WebLI
- **Papers:**
- Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343
## Model Usage
### With OpenCLIP
```
import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-B-16-SigLIP-384')
tokenizer = get_tokenizer('hf-hub:timm/ViT-B-16-SigLIP-384')
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)
labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features = F.normalize(image_features, dim=-1)
text_features = F.normalize(text_features, dim=-1)
text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)
```
### With `timm` (for image embeddings)
```python
from urllib.request import urlopen
from PIL import Image
import timm
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch16_siglip_384',
pretrained=True,
num_classes=0,
)
model = model.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)
output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{zhai2023sigmoid,
title={Sigmoid loss for language image pre-training},
author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
journal={arXiv preprint arXiv:2303.15343},
year={2023}
}
```
```bibtex
@misc{big_vision,
author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
title = {Big Vision},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/google-research/big_vision}}
}
```
|
tomatotime23/Finetune-RobertFrost | tomatotime23 | 2023-10-25T21:56:50Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:prajwalcr/poetry_gpt2",
"base_model:finetune:prajwalcr/poetry_gpt2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2023-10-25T21:05:20Z | ---
base_model: prajwalcr/poetry_gpt2
tags:
- generated_from_trainer
model-index:
- name: Finetune-RobertFrost
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. -->
# Finetune-RobertFrost
This model is a fine-tuned version of [prajwalcr/poetry_gpt2](https://huggingface.co/prajwalcr/poetry_gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4663
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 40 | 4.5430 |
| No log | 2.0 | 80 | 4.4799 |
| No log | 3.0 | 120 | 4.4663 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cpu
- Datasets 2.14.6
- Tokenizers 0.14.1
|
timm/ViT-L-16-SigLIP-256 | timm | 2023-10-25T21:54:51Z | 5,423 | 1 | open_clip | [
"open_clip",
"safetensors",
"clip",
"siglip",
"zero-shot-image-classification",
"dataset:webli",
"arxiv:2303.15343",
"license:apache-2.0",
"region:us"
]
| zero-shot-image-classification | 2023-10-16T23:24:41Z | ---
tags:
- clip
- siglip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: apache-2.0
datasets:
- webli
---
# Model card for ViT-L-16-SigLIP-256
A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only).
## Model Details
- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
- **Original:** https://github.com/google-research/big_vision
- **Dataset:** WebLI
- **Papers:**
- Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343
## Model Usage
### With OpenCLIP
```
import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP-256')
tokenizer = get_tokenizer('hf-hub:timm/ViT-L-16-SigLIP-256')
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)
labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features = F.normalize(image_features, dim=-1)
text_features = F.normalize(text_features, dim=-1)
text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)
```
### With `timm` (for image embeddings)
```python
from urllib.request import urlopen
from PIL import Image
import timm
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_large_patch16_siglip_256',
pretrained=True,
num_classes=0,
)
model = model.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)
output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{zhai2023sigmoid,
title={Sigmoid loss for language image pre-training},
author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
journal={arXiv preprint arXiv:2303.15343},
year={2023}
}
```
```bibtex
@misc{big_vision,
author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
title = {Big Vision},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/google-research/big_vision}}
}
```
|
timm/ViT-L-16-SigLIP-384 | timm | 2023-10-25T21:54:17Z | 198,095 | 24 | open_clip | [
"open_clip",
"safetensors",
"clip",
"siglip",
"zero-shot-image-classification",
"dataset:webli",
"arxiv:2303.15343",
"license:apache-2.0",
"region:us"
]
| zero-shot-image-classification | 2023-10-16T23:32:50Z | ---
tags:
- clip
- siglip
library_name: open_clip
pipeline_tag: zero-shot-image-classification
license: apache-2.0
datasets:
- webli
---
# Model card for ViT-L-16-SigLIP-384
A SigLIP (Sigmoid loss for Language-Image Pre-training) model trained on WebLI.
This model has been converted to PyTorch from the original JAX checkpoints in [Big Vision](https://github.com/google-research/big_vision). These weights are usable in both OpenCLIP (image + text) and timm (image only).
## Model Details
- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification.
- **Original:** https://github.com/google-research/big_vision
- **Dataset:** WebLI
- **Papers:**
- Sigmoid loss for language image pre-training: https://arxiv.org/abs/2303.15343
## Model Usage
### With OpenCLIP
```
import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer # works on open-clip-torch>=2.23.0, timm>=0.9.8
model, preprocess = create_model_from_pretrained('hf-hub:timm/ViT-L-16-SigLIP-384')
tokenizer = get_tokenizer('hf-hub:timm/ViT-L-16-SigLIP-384')
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)
labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)
with torch.no_grad(), torch.cuda.amp.autocast():
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features = F.normalize(image_features, dim=-1)
text_features = F.normalize(text_features, dim=-1)
text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)
zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)
```
### With `timm` (for image embeddings)
```python
from urllib.request import urlopen
from PIL import Image
import timm
image = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_large_patch16_siglip_384',
pretrained=True,
num_classes=0,
)
model = model.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)
output = model(transforms(image).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
```
## Citation
```bibtex
@article{zhai2023sigmoid,
title={Sigmoid loss for language image pre-training},
author={Zhai, Xiaohua and Mustafa, Basil and Kolesnikov, Alexander and Beyer, Lucas},
journal={arXiv preprint arXiv:2303.15343},
year={2023}
}
```
```bibtex
@misc{big_vision,
author = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
title = {Big Vision},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/google-research/big_vision}}
}
```
|
zsanborn/ppo-Huggy | zsanborn | 2023-10-25T21:51:06Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
]
| reinforcement-learning | 2023-10-25T21:51:01Z | ---
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 ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: zsanborn/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
jfelgate/rl_course_vizdoom_health_gathering_supreme | jfelgate | 2023-10-25T21:37:38Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-25T16:03:54Z | ---
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: 15.64 +/- 5.70
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 jfelgate/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.
|
ftorresa/dqn-SpaceInvadersNoFrameskip-v4 | ftorresa | 2023-10-25T21:34:23Z | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2023-10-25T21:26:27Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 576.00 +/- 129.67
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ftorresa -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ftorresa -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ftorresa
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
innyun/poca-SoccerTwos | innyun | 2023-10-25T21:33:08Z | 51 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| reinforcement-learning | 2023-10-25T21:32:43Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: innyun/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
clareandme/userRequested-model | clareandme | 2023-10-25T21:23:15Z | 4 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| text-classification | 2023-10-25T13:33:01Z | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# clareandme/userRequested-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("clareandme/userRequested-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst ๐คฎ"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
fearvel/aoki-sd | fearvel | 2023-10-25T21:19:36Z | 0 | 0 | null | [
"stable-diffusion",
"text-to-image",
"StableDiffusionPipeline",
"lora",
"region:us"
]
| text-to-image | 2023-10-25T21:18:03Z | ---
tags:
- stable-diffusion
- text-to-image
- StableDiffusionPipeline
- lora
---
## Model
lora of profile picture generation
 |
Subsets and Splits