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text-generation | mlx |
# mlx-community/Qwen1.5-110B-Chat-4bit
This model was converted to MLX format from [`Qwen/Qwen1.5-110B-Chat`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-110B-Chat) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen1.5-110B-Chat-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en"], "license": "other", "tags": ["chat", "mlx"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE", "pipeline_tag": "text-generation"} | mlx-community/Qwen1.5-110B-Chat-4bit | null | [
"mlx",
"safetensors",
"qwen2",
"chat",
"text-generation",
"conversational",
"en",
"license:other",
"region:us"
]
| null | 2024-04-26T13:22:28+00:00 |
text-generation | transformers | {"license": "apache-2.0"} | Defetya/qwen-4B-glue | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:22:40+00:00 |
|
text-generation | mlx |
# mlx-community/Qwen1.5-110B-Chat-8bit
This model was converted to MLX format from [`Qwen/Qwen1.5-110B-Chat`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/Qwen/Qwen1.5-110B-Chat) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Qwen1.5-110B-Chat-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en"], "license": "other", "tags": ["chat", "mlx"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE", "pipeline_tag": "text-generation"} | mlx-community/Qwen1.5-110B-Chat-8bit | null | [
"mlx",
"safetensors",
"qwen2",
"chat",
"text-generation",
"conversational",
"en",
"license:other",
"region:us"
]
| null | 2024-04-26T13:22:53+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-instruct-v0.2-bnb-4bit1024
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6953
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8431 | 0.02 | 25 | 1.4131 |
| 0.8021 | 0.04 | 50 | 0.7911 |
| 0.7972 | 0.05 | 75 | 0.7886 |
| 0.7886 | 0.07 | 100 | 0.7780 |
| 0.7762 | 0.09 | 125 | 0.7546 |
| 0.7338 | 0.11 | 150 | 0.7332 |
| 0.707 | 0.12 | 175 | 0.7399 |
| 0.7252 | 0.14 | 200 | 0.7303 |
| 0.7513 | 0.16 | 225 | 0.7384 |
| 0.7275 | 0.18 | 250 | 0.7380 |
| 0.7283 | 0.19 | 275 | 0.7285 |
| 0.7132 | 0.21 | 300 | 0.7452 |
| 0.7273 | 0.23 | 325 | 0.7370 |
| 0.7353 | 0.25 | 350 | 0.7388 |
| 0.7457 | 0.27 | 375 | 0.7292 |
| 0.7404 | 0.28 | 400 | 0.7315 |
| 0.7312 | 0.3 | 425 | 0.7341 |
| 0.7285 | 0.32 | 450 | 0.7277 |
| 0.7331 | 0.34 | 475 | 0.7318 |
| 0.7179 | 0.35 | 500 | 0.7401 |
| 0.7432 | 0.37 | 525 | 0.7399 |
| 0.7305 | 0.39 | 550 | 0.7463 |
| 0.723 | 0.41 | 575 | 0.7448 |
| 0.7303 | 0.42 | 600 | 0.7339 |
| 0.7213 | 0.44 | 625 | 0.7320 |
| 0.7236 | 0.46 | 650 | 0.7378 |
| 0.7263 | 0.48 | 675 | 0.7451 |
| 0.7462 | 0.5 | 700 | 0.7238 |
| 0.7287 | 0.51 | 725 | 0.7274 |
| 0.7364 | 0.53 | 750 | 0.7369 |
| 0.7276 | 0.55 | 775 | 0.7282 |
| 0.7268 | 0.57 | 800 | 0.7431 |
| 0.7382 | 0.58 | 825 | 0.7376 |
| 0.7185 | 0.6 | 850 | 0.7402 |
| 0.7153 | 0.62 | 875 | 0.7362 |
| 0.7314 | 0.64 | 900 | 0.7395 |
| 0.7465 | 0.65 | 925 | 0.7378 |
| 0.7228 | 0.67 | 950 | 0.7333 |
| 0.7336 | 0.69 | 975 | 0.7337 |
| 0.72 | 0.71 | 1000 | 0.7313 |
| 0.7258 | 0.73 | 1025 | 0.7379 |
| 0.7312 | 0.74 | 1050 | 0.7342 |
| 0.7268 | 0.76 | 1075 | 0.7350 |
| 0.7137 | 0.78 | 1100 | 0.7401 |
| 0.7277 | 0.8 | 1125 | 0.7277 |
| 0.7314 | 0.81 | 1150 | 0.7388 |
| 0.7106 | 0.83 | 1175 | 0.7371 |
| 0.7226 | 0.85 | 1200 | 0.7326 |
| 0.7262 | 0.87 | 1225 | 0.7328 |
| 0.7356 | 0.88 | 1250 | 0.7408 |
| 0.7245 | 0.9 | 1275 | 0.7365 |
| 0.7221 | 0.92 | 1300 | 0.7404 |
| 0.7194 | 0.94 | 1325 | 0.7418 |
| 0.7209 | 0.96 | 1350 | 0.7380 |
| 0.7205 | 0.97 | 1375 | 0.7279 |
| 0.6788 | 0.99 | 1400 | 0.6953 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "unsloth", "unsloth", "generated_from_trainer"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "model-index": [{"name": "mistral-7b-instruct-v0.2-bnb-4bit1024", "results": []}]} | 12yuens2/hotpotqa-unsloth-mistral-7b-4bit-1024 | null | [
"peft",
"safetensors",
"trl",
"sft",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T13:26:20+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_eli5_clm-model
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0890
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0259 | 1.0 | 957 | 0.0873 |
| 0.0102 | 2.0 | 1914 | 0.0855 |
| 0.0026 | 3.0 | 2871 | 0.0890 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]} | mikaya-vu/my_awesome_eli5_clm-model | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:26:41+00:00 |
null | null | {} | pamanseau/sn25-3 | null | [
"region:us"
]
| null | 2024-04-26T13:27:13+00:00 |
|
null | diffusers | # Marigold Normals (LCM) Model Card
This model belongs to the family of diffusion-based Marigold models for solving various computer vision tasks.
The Marigold Normals model focuses on the surface normals task.
It takes an input image and computes surface normals in each pixel.
The LCM stands for Latent Consistency Models, which is a technique for making the diffusion model fast.
The Marigold Normals model is trained from Stable Diffusion with synthetic data, and the LCM model is further fine-tuned from it.
Thanks to the rich visual knowledge stored in Stable Diffusion, Marigold models possess deep scene understanding and excel at solving computer vision tasks.
Read more about Marigold in our paper titled "Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation".
[](https://marigoldmonodepth.github.io)
[](https://github.com/prs-eth/Marigold)
[](https://arxiv.org/abs/2312.02145)
[](https://huggingface.co/spaces/toshas/marigold)
Developed by:
[Bingxin Ke](http://www.kebingxin.com/),
[Anton Obukhov](https://www.obukhov.ai/),
[Shengyu Huang](https://shengyuh.github.io/),
[Nando Metzger](https://nandometzger.github.io/),
[Rodrigo Caye Daudt](https://rcdaudt.github.io/),
[Konrad Schindler](https://scholar.google.com/citations?user=FZuNgqIAAAAJ&hl=en)
## 🎓 Citation
```bibtex
@InProceedings{ke2023repurposing,
title={Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation},
author={Bingxin Ke and Anton Obukhov and Shengyu Huang and Nando Metzger and Rodrigo Caye Daudt and Konrad Schindler},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}
```
## 🎫 License
This work is licensed under the Apache License, Version 2.0 (as defined in the [LICENSE](LICENSE.txt)).
By downloading and using the code and model you agree to the terms in the [LICENSE](LICENSE.txt).
[](https://www.apache.org/licenses/LICENSE-2.0)
| {"language": ["en"], "license": "apache-2.0", "tags": ["monocular normals estimation", "single image normals estimation", "normals", "in-the-wild", "zero-shot", "LCM"], "pipeline_tag": "normals-estimation"} | prs-eth/marigold-normals-lcm-v0-1 | null | [
"diffusers",
"safetensors",
"monocular normals estimation",
"single image normals estimation",
"normals",
"in-the-wild",
"zero-shot",
"LCM",
"normals-estimation",
"en",
"arxiv:2312.02145",
"license:apache-2.0",
"diffusers:MarigoldPipeline",
"region:us"
]
| null | 2024-04-26T13:27:15+00:00 |
text2text-generation | transformers | {} | NoaCA14/SQUAD_TEST-small-multitask-qg-ae | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:27:24+00:00 |
|
null | null | {} | pamanseau/sn25-4 | null | [
"region:us"
]
| null | 2024-04-26T13:27:26+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# code-llama-7b-text-to-sql
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "codellama/CodeLlama-7b-hf", "model-index": [{"name": "code-llama-7b-text-to-sql", "results": []}]} | oukwuaba/code-llama-7b-text-to-sql | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
]
| null | 2024-04-26T13:28:44+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers"} | MD1998/chating_beginners_v1 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:30:33+00:00 |
null | null | Just a simple modal using Yolov8 for Image Classification task on the dataset IP102 with 20 classes extracted based on image amount. | {"license": "apache-2.0"} | Khieminem/ip102-yolov8-imgcls | null | [
"onnx",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T13:30:40+00:00 |
text-generation | transformers | # finetune chinese Meta Llama3 Instruct 8b with Llama-Factory
```
“top.model_name": "LLaMA3-8B-Chat",
"top.finetuning_type": "lora",
"top.adapter_path": [],
"top.quantization_bit": "none",
"top.template": "llama3",
"top.rope_scaling": "none",
top.booster": "none",
"train.training_stage": "Supervised Fine-Tuning",
"train.dataset_dir": "data",
"train.dataset": [
"alpaca_zh",
"alpaca_gpt4_zh",
"guanaco",
"oaast_sft_zh",
"wikipedia_zh"
]，
top.model_name": "LLaMA3-8B-Chat",
"top.finetuning_type": "lora",
"top.adapter_path": [],
"top.quantization_bit": "none",
"top.template": "llama3",
"top.rope_scaling": "none",
"top.booster": "none",
"train.training_stage": "Supervised Fine-Tuning",
"train.dataset_dir": "data",
"train.dataset": [
"alpaca_zh",
"alpaca_gpt4_zh",
"guanaco",
"nsfc_zh",
"oaast_sft_zh",
"wikipedia_zh"
],
``` | {"license": "apache-2.0"} | pooka74/LLaMA3-8B-Chat-Chinese | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:32:01+00:00 |
null | null | {} | vladserkoff/mask2former-swin-small-ade-semantic | null | [
"region:us"
]
| null | 2024-04-26T13:32:26+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-31m_mz-131f_IMDB
This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-131f_IMDB", "results": []}]} | AlignmentResearch/robust_llm_pythia-31m_mz-131f_IMDB | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-31m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:33:36+00:00 |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-dmae-va-U5-100-3i
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5087
- Accuracy: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.9 | 7 | 0.5069 | 0.8333 |
| 0.3296 | 1.94 | 15 | 0.5087 | 0.8667 |
| 0.2919 | 2.97 | 23 | 0.5190 | 0.8667 |
| 0.2572 | 4.0 | 31 | 0.6483 | 0.7667 |
| 0.2572 | 4.9 | 38 | 0.5785 | 0.8167 |
| 0.2229 | 5.94 | 46 | 0.5932 | 0.8333 |
| 0.1799 | 6.97 | 54 | 0.5272 | 0.85 |
| 0.1563 | 8.0 | 62 | 0.6124 | 0.85 |
| 0.1563 | 8.9 | 69 | 0.6798 | 0.8167 |
| 0.125 | 9.94 | 77 | 0.7356 | 0.7833 |
| 0.1343 | 10.97 | 85 | 0.5086 | 0.85 |
| 0.0906 | 12.0 | 93 | 0.7601 | 0.7667 |
| 0.103 | 12.9 | 100 | 0.8084 | 0.8 |
| 0.103 | 13.94 | 108 | 0.5612 | 0.85 |
| 0.1002 | 14.97 | 116 | 0.6454 | 0.8333 |
| 0.1107 | 16.0 | 124 | 0.7783 | 0.8 |
| 0.1036 | 16.9 | 131 | 0.7857 | 0.7833 |
| 0.1036 | 17.94 | 139 | 0.6504 | 0.8167 |
| 0.1248 | 18.97 | 147 | 0.6510 | 0.8167 |
| 0.1074 | 20.0 | 155 | 0.7813 | 0.7833 |
| 0.1038 | 20.9 | 162 | 0.6553 | 0.8 |
| 0.1052 | 21.94 | 170 | 0.6449 | 0.8333 |
| 0.1052 | 22.97 | 178 | 0.7444 | 0.8 |
| 0.0782 | 24.0 | 186 | 1.0751 | 0.6833 |
| 0.0952 | 24.9 | 193 | 0.6453 | 0.8333 |
| 0.0803 | 25.94 | 201 | 0.7794 | 0.8 |
| 0.0803 | 26.97 | 209 | 0.6160 | 0.8333 |
| 0.0947 | 28.0 | 217 | 0.6362 | 0.85 |
| 0.0702 | 28.9 | 224 | 0.7610 | 0.8167 |
| 0.0737 | 29.94 | 232 | 0.7924 | 0.8167 |
| 0.0644 | 30.97 | 240 | 0.9755 | 0.8 |
| 0.0644 | 32.0 | 248 | 0.8580 | 0.8333 |
| 0.0695 | 32.9 | 255 | 1.1410 | 0.7167 |
| 0.09 | 33.94 | 263 | 0.8442 | 0.8 |
| 0.0619 | 34.97 | 271 | 1.1689 | 0.7167 |
| 0.0619 | 36.0 | 279 | 0.7599 | 0.8333 |
| 0.0607 | 36.9 | 286 | 0.8498 | 0.8167 |
| 0.0509 | 37.94 | 294 | 0.8331 | 0.85 |
| 0.0666 | 38.97 | 302 | 0.8166 | 0.8167 |
| 0.0615 | 40.0 | 310 | 0.9394 | 0.7667 |
| 0.0615 | 40.9 | 317 | 0.8837 | 0.8 |
| 0.0503 | 41.94 | 325 | 0.8208 | 0.8333 |
| 0.0431 | 42.97 | 333 | 1.1271 | 0.75 |
| 0.0548 | 44.0 | 341 | 0.9044 | 0.7833 |
| 0.0548 | 44.9 | 348 | 0.9017 | 0.8 |
| 0.0414 | 45.94 | 356 | 1.1390 | 0.75 |
| 0.0609 | 46.97 | 364 | 0.8937 | 0.8 |
| 0.0556 | 48.0 | 372 | 0.8459 | 0.8 |
| 0.0556 | 48.9 | 379 | 1.0285 | 0.7667 |
| 0.0417 | 49.94 | 387 | 0.7379 | 0.85 |
| 0.0409 | 50.97 | 395 | 0.7817 | 0.8333 |
| 0.0206 | 52.0 | 403 | 0.7860 | 0.8167 |
| 0.0414 | 52.9 | 410 | 0.8414 | 0.8167 |
| 0.0414 | 53.94 | 418 | 0.8657 | 0.8 |
| 0.0329 | 54.97 | 426 | 0.8824 | 0.8 |
| 0.0394 | 56.0 | 434 | 0.7990 | 0.8333 |
| 0.0373 | 56.9 | 441 | 0.8101 | 0.8167 |
| 0.0373 | 57.94 | 449 | 0.8535 | 0.8 |
| 0.0418 | 58.97 | 457 | 0.9149 | 0.8167 |
| 0.0365 | 60.0 | 465 | 0.9278 | 0.8 |
| 0.0367 | 60.9 | 472 | 0.9064 | 0.8 |
| 0.0355 | 61.94 | 480 | 0.9610 | 0.7833 |
| 0.0355 | 62.97 | 488 | 0.9174 | 0.8167 |
| 0.0492 | 64.0 | 496 | 0.9877 | 0.7667 |
| 0.0326 | 64.9 | 503 | 1.0192 | 0.7833 |
| 0.0233 | 65.94 | 511 | 0.9588 | 0.8 |
| 0.0233 | 66.97 | 519 | 0.9829 | 0.7833 |
| 0.0251 | 68.0 | 527 | 1.0540 | 0.7667 |
| 0.0283 | 68.9 | 534 | 1.0556 | 0.7667 |
| 0.0307 | 69.94 | 542 | 1.0036 | 0.7833 |
| 0.0319 | 70.97 | 550 | 0.9294 | 0.8 |
| 0.0319 | 72.0 | 558 | 1.0077 | 0.8 |
| 0.0246 | 72.9 | 565 | 1.0298 | 0.7833 |
| 0.0205 | 73.94 | 573 | 1.0041 | 0.7833 |
| 0.0345 | 74.97 | 581 | 0.9182 | 0.7833 |
| 0.0345 | 76.0 | 589 | 0.9054 | 0.8333 |
| 0.0181 | 76.9 | 596 | 0.9338 | 0.8333 |
| 0.0287 | 77.94 | 604 | 0.9678 | 0.7833 |
| 0.0268 | 78.97 | 612 | 0.9841 | 0.7833 |
| 0.0293 | 80.0 | 620 | 1.0380 | 0.7667 |
| 0.0293 | 80.9 | 627 | 1.0837 | 0.7833 |
| 0.0222 | 81.94 | 635 | 1.0132 | 0.7667 |
| 0.033 | 82.97 | 643 | 0.9785 | 0.8 |
| 0.0227 | 84.0 | 651 | 0.9848 | 0.8 |
| 0.0227 | 84.9 | 658 | 0.9780 | 0.8 |
| 0.0295 | 85.94 | 666 | 0.9613 | 0.8167 |
| 0.0291 | 86.97 | 674 | 0.9753 | 0.8167 |
| 0.031 | 88.0 | 682 | 0.9831 | 0.8 |
| 0.031 | 88.9 | 689 | 0.9820 | 0.8 |
| 0.0233 | 89.94 | 697 | 0.9793 | 0.8 |
| 0.0195 | 90.32 | 700 | 0.9788 | 0.8 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-patch16-224-dmae-va-U5-100-3i", "results": []}]} | Augusto777/vit-base-patch16-224-dmae-va-U5-100-3i | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:34:06+00:00 |
null | null | {} | yiII1/gpt2-cnwiki-full_data-T4x2-V2 | null | [
"region:us"
]
| null | 2024-04-26T13:35:34+00:00 |
|
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 31889 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 3188,
"evaluator": "utils.ToponymResolutionEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | dguzh/geo-all-MiniLM-L6-v2 | null | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:35:46+00:00 |
null | null | {} | ihsass/uzma-new-mix-camelt | null | [
"region:us"
]
| null | 2024-04-26T13:36:10+00:00 |
|
null | diffusers | {} | RonenWeiz/encdec_model_73000_5_epochs_cosine | null | [
"diffusers",
"safetensors",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
]
| null | 2024-04-26T13:36:33+00:00 |
|
null | null | {} | ammar00200/Recommendation_Model | null | [
"tflite",
"region:us"
]
| null | 2024-04-26T13:36:58+00:00 |
|
null | null | {} | OlegHehe/my_awesome_model | null | [
"region:us"
]
| null | 2024-04-26T13:39:22+00:00 |
|
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b1-finetuned-cityscapes-1024-1024-straighter-only-test
This model is a fine-tuned version of [nvidia/segformer-b1-finetuned-cityscapes-1024-1024](https://huggingface.co/nvidia/segformer-b1-finetuned-cityscapes-1024-1024) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319
- Mean Iou: 0.9378
- Mean Accuracy: 0.9615
- Overall Accuracy: 0.9895
- Accuracy Default: 1e-06
- Accuracy Pipe: 0.8987
- Accuracy Floor: 0.9897
- Accuracy Background: 0.9959
- Iou Default: 1e-06
- Iou Pipe: 0.8434
- Iou Floor: 0.9813
- Iou Background: 0.9889
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Default | Accuracy Pipe | Accuracy Floor | Accuracy Background | Iou Default | Iou Pipe | Iou Floor | Iou Background |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:----------------:|:-------------:|:--------------:|:-------------------:|:-----------:|:--------:|:---------:|:--------------:|
| 0.3904 | 1.0 | 36 | 0.1465 | 0.8037 | 0.8484 | 0.9645 | 1e-06 | 0.5855 | 0.9696 | 0.9900 | 1e-06 | 0.5120 | 0.9355 | 0.9635 |
| 0.1244 | 2.0 | 72 | 0.0891 | 0.8640 | 0.9024 | 0.9766 | 1e-06 | 0.7371 | 0.9764 | 0.9938 | 1e-06 | 0.6565 | 0.9592 | 0.9762 |
| 0.0818 | 3.0 | 108 | 0.0669 | 0.8868 | 0.9178 | 0.9804 | 1e-06 | 0.7826 | 0.9745 | 0.9965 | 1e-06 | 0.7154 | 0.9657 | 0.9793 |
| 0.061 | 4.0 | 144 | 0.0525 | 0.9072 | 0.9407 | 0.9839 | 1e-06 | 0.8472 | 0.9801 | 0.9949 | 1e-06 | 0.7675 | 0.9711 | 0.9830 |
| 0.051 | 5.0 | 180 | 0.0470 | 0.9118 | 0.9444 | 0.9849 | 1e-06 | 0.8585 | 0.9790 | 0.9958 | 1e-06 | 0.7789 | 0.9722 | 0.9845 |
| 0.0461 | 6.0 | 216 | 0.0424 | 0.9191 | 0.9510 | 0.9861 | 1e-06 | 0.8736 | 0.9851 | 0.9944 | 1e-06 | 0.7959 | 0.9762 | 0.9851 |
| 0.0388 | 7.0 | 252 | 0.0401 | 0.9184 | 0.9443 | 0.9862 | 1e-06 | 0.8508 | 0.9862 | 0.9960 | 1e-06 | 0.7932 | 0.9769 | 0.9853 |
| 0.0348 | 8.0 | 288 | 0.0372 | 0.9244 | 0.9565 | 0.9870 | 1e-06 | 0.8894 | 0.9859 | 0.9943 | 1e-06 | 0.8104 | 0.9763 | 0.9865 |
| 0.0324 | 9.0 | 324 | 0.0362 | 0.9237 | 0.9486 | 0.9870 | 1e-06 | 0.8656 | 0.9833 | 0.9969 | 1e-06 | 0.8076 | 0.9773 | 0.9861 |
| 0.031 | 10.0 | 360 | 0.0349 | 0.9239 | 0.9520 | 0.9872 | 1e-06 | 0.8737 | 0.9870 | 0.9954 | 1e-06 | 0.8067 | 0.9788 | 0.9863 |
| 0.0287 | 11.0 | 396 | 0.0333 | 0.9285 | 0.9531 | 0.9877 | 1e-06 | 0.8720 | 0.9930 | 0.9944 | 1e-06 | 0.8209 | 0.9778 | 0.9868 |
| 0.0268 | 12.0 | 432 | 0.0332 | 0.9283 | 0.9522 | 0.9879 | 1e-06 | 0.8737 | 0.9865 | 0.9966 | 1e-06 | 0.8191 | 0.9787 | 0.9872 |
| 0.025 | 13.0 | 468 | 0.0311 | 0.9317 | 0.9622 | 0.9883 | 1e-06 | 0.9042 | 0.9877 | 0.9945 | 1e-06 | 0.8281 | 0.9794 | 0.9877 |
| 0.0247 | 14.0 | 504 | 0.0310 | 0.9308 | 0.9535 | 0.9884 | 1e-06 | 0.8742 | 0.9904 | 0.9959 | 1e-06 | 0.8247 | 0.9801 | 0.9876 |
| 0.0236 | 15.0 | 540 | 0.0307 | 0.9322 | 0.9538 | 0.9886 | 1e-06 | 0.8755 | 0.9897 | 0.9963 | 1e-06 | 0.8292 | 0.9793 | 0.9880 |
| 0.0223 | 16.0 | 576 | 0.0301 | 0.9346 | 0.9633 | 0.9888 | 1e-06 | 0.9083 | 0.9861 | 0.9955 | 1e-06 | 0.8360 | 0.9791 | 0.9886 |
| 0.0208 | 17.0 | 612 | 0.0308 | 0.9326 | 0.9578 | 0.9887 | 1e-06 | 0.8876 | 0.9907 | 0.9953 | 1e-06 | 0.8300 | 0.9797 | 0.9882 |
| 0.0198 | 18.0 | 648 | 0.0295 | 0.9339 | 0.9589 | 0.9888 | 1e-06 | 0.8897 | 0.9921 | 0.9949 | 1e-06 | 0.8335 | 0.9799 | 0.9882 |
| 0.0194 | 19.0 | 684 | 0.0311 | 0.9315 | 0.9524 | 0.9886 | 1e-06 | 0.8712 | 0.9894 | 0.9967 | 1e-06 | 0.8265 | 0.9802 | 0.9878 |
| 0.0188 | 20.0 | 720 | 0.0299 | 0.9332 | 0.9558 | 0.9888 | 1e-06 | 0.8807 | 0.9906 | 0.9959 | 1e-06 | 0.8318 | 0.9796 | 0.9882 |
| 0.0187 | 21.0 | 756 | 0.0298 | 0.9344 | 0.9567 | 0.9890 | 1e-06 | 0.8833 | 0.9905 | 0.9961 | 1e-06 | 0.8339 | 0.9810 | 0.9883 |
| 0.0179 | 22.0 | 792 | 0.0304 | 0.9334 | 0.9566 | 0.9889 | 1e-06 | 0.8834 | 0.9904 | 0.9959 | 1e-06 | 0.8317 | 0.9804 | 0.9882 |
| 0.0174 | 23.0 | 828 | 0.0301 | 0.9350 | 0.9603 | 0.9890 | 1e-06 | 0.8960 | 0.9895 | 0.9955 | 1e-06 | 0.8364 | 0.9803 | 0.9884 |
| 0.017 | 24.0 | 864 | 0.0294 | 0.9352 | 0.9589 | 0.9890 | 1e-06 | 0.8925 | 0.9877 | 0.9963 | 1e-06 | 0.8371 | 0.9802 | 0.9883 |
| 0.0172 | 25.0 | 900 | 0.0322 | 0.9334 | 0.9555 | 0.9888 | 1e-06 | 0.8796 | 0.9908 | 0.9960 | 1e-06 | 0.8320 | 0.9799 | 0.9882 |
| 0.0165 | 26.0 | 936 | 0.0312 | 0.9331 | 0.9556 | 0.9888 | 1e-06 | 0.8813 | 0.9891 | 0.9964 | 1e-06 | 0.8318 | 0.9792 | 0.9884 |
| 0.0162 | 27.0 | 972 | 0.0296 | 0.9350 | 0.9589 | 0.9891 | 1e-06 | 0.8911 | 0.9899 | 0.9959 | 1e-06 | 0.8360 | 0.9806 | 0.9885 |
| 0.0155 | 28.0 | 1008 | 0.0314 | 0.9359 | 0.9578 | 0.9892 | 1e-06 | 0.8880 | 0.9890 | 0.9965 | 1e-06 | 0.8384 | 0.9808 | 0.9884 |
| 0.0154 | 29.0 | 1044 | 0.0291 | 0.9379 | 0.9637 | 0.9894 | 1e-06 | 0.9061 | 0.9898 | 0.9952 | 1e-06 | 0.8438 | 0.9812 | 0.9887 |
| 0.0151 | 30.0 | 1080 | 0.0289 | 0.9372 | 0.9620 | 0.9893 | 1e-06 | 0.8994 | 0.9912 | 0.9952 | 1e-06 | 0.8419 | 0.9810 | 0.9887 |
| 0.0152 | 31.0 | 1116 | 0.0310 | 0.9365 | 0.9573 | 0.9893 | 1e-06 | 0.8865 | 0.9884 | 0.9969 | 1e-06 | 0.8397 | 0.9815 | 0.9884 |
| 0.0143 | 32.0 | 1152 | 0.0307 | 0.9376 | 0.9614 | 0.9894 | 1e-06 | 0.8983 | 0.9904 | 0.9956 | 1e-06 | 0.8433 | 0.9809 | 0.9887 |
| 0.0138 | 33.0 | 1188 | 0.0295 | 0.9385 | 0.9623 | 0.9896 | 1e-06 | 0.9004 | 0.9910 | 0.9955 | 1e-06 | 0.8451 | 0.9814 | 0.9889 |
| 0.0149 | 34.0 | 1224 | 0.0308 | 0.9380 | 0.9617 | 0.9894 | 1e-06 | 0.9007 | 0.9883 | 0.9961 | 1e-06 | 0.8444 | 0.9809 | 0.9886 |
| 0.0138 | 35.0 | 1260 | 0.0304 | 0.9376 | 0.9616 | 0.9894 | 1e-06 | 0.8993 | 0.9899 | 0.9958 | 1e-06 | 0.8431 | 0.9809 | 0.9888 |
| 0.0138 | 36.0 | 1296 | 0.0299 | 0.9379 | 0.9598 | 0.9895 | 1e-06 | 0.8932 | 0.9901 | 0.9962 | 1e-06 | 0.8433 | 0.9816 | 0.9887 |
| 0.0139 | 37.0 | 1332 | 0.0298 | 0.9378 | 0.9615 | 0.9895 | 1e-06 | 0.8983 | 0.9903 | 0.9958 | 1e-06 | 0.8435 | 0.9812 | 0.9889 |
| 0.0133 | 38.0 | 1368 | 0.0293 | 0.9393 | 0.9624 | 0.9897 | 1e-06 | 0.9008 | 0.9906 | 0.9958 | 1e-06 | 0.8467 | 0.9823 | 0.9889 |
| 0.0131 | 39.0 | 1404 | 0.0318 | 0.9368 | 0.9592 | 0.9893 | 1e-06 | 0.8922 | 0.9893 | 0.9963 | 1e-06 | 0.8406 | 0.9814 | 0.9884 |
| 0.0129 | 40.0 | 1440 | 0.0303 | 0.9382 | 0.9627 | 0.9895 | 1e-06 | 0.9034 | 0.9890 | 0.9958 | 1e-06 | 0.8447 | 0.9813 | 0.9887 |
| 0.0126 | 41.0 | 1476 | 0.0304 | 0.9392 | 0.9631 | 0.9896 | 1e-06 | 0.9037 | 0.9901 | 0.9956 | 1e-06 | 0.8471 | 0.9818 | 0.9887 |
| 0.0126 | 42.0 | 1512 | 0.0311 | 0.9378 | 0.9595 | 0.9895 | 1e-06 | 0.8929 | 0.9892 | 0.9965 | 1e-06 | 0.8432 | 0.9817 | 0.9887 |
| 0.0125 | 43.0 | 1548 | 0.0314 | 0.9383 | 0.9611 | 0.9895 | 1e-06 | 0.8974 | 0.9899 | 0.9960 | 1e-06 | 0.8453 | 0.9809 | 0.9888 |
| 0.0129 | 44.0 | 1584 | 0.0319 | 0.9374 | 0.9585 | 0.9895 | 1e-06 | 0.8886 | 0.9904 | 0.9964 | 1e-06 | 0.8420 | 0.9816 | 0.9887 |
| 0.0127 | 45.0 | 1620 | 0.0313 | 0.9380 | 0.9594 | 0.9895 | 1e-06 | 0.8920 | 0.9900 | 0.9964 | 1e-06 | 0.8436 | 0.9816 | 0.9887 |
| 0.0127 | 46.0 | 1656 | 0.0321 | 0.9379 | 0.9626 | 0.9895 | 1e-06 | 0.9029 | 0.9893 | 0.9957 | 1e-06 | 0.8444 | 0.9805 | 0.9890 |
| 0.0121 | 47.0 | 1692 | 0.0321 | 0.9377 | 0.9599 | 0.9895 | 1e-06 | 0.8930 | 0.9907 | 0.9960 | 1e-06 | 0.8430 | 0.9813 | 0.9888 |
| 0.0115 | 48.0 | 1728 | 0.0305 | 0.9390 | 0.9633 | 0.9897 | 1e-06 | 0.9043 | 0.9900 | 0.9957 | 1e-06 | 0.8463 | 0.9817 | 0.9890 |
| 0.0118 | 49.0 | 1764 | 0.0319 | 0.9378 | 0.9615 | 0.9895 | 1e-06 | 0.8987 | 0.9897 | 0.9959 | 1e-06 | 0.8434 | 0.9813 | 0.9889 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
| {"license": "other", "tags": ["generated_from_trainer"], "base_model": "nvidia/segformer-b1-finetuned-cityscapes-1024-1024", "model-index": [{"name": "segformer-b1-finetuned-cityscapes-1024-1024-straighter-only-test", "results": []}]} | selvaa/segformer-b1-finetuned-cityscapes-1024-1024-straighter-only-test | null | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"generated_from_trainer",
"base_model:nvidia/segformer-b1-finetuned-cityscapes-1024-1024",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:40:44+00:00 |
null | null | {} | kpriyanshu256/vlr-ff-embed-60m | null | [
"region:us"
]
| null | 2024-04-26T13:40:51+00:00 |
|
null | null | {} | cjkepinsky/SequenceClassification | null | [
"region:us"
]
| null | 2024-04-26T13:40:56+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "262.29 +/- 23.03", "name": "mean_reward", "verified": false}]}]}]} | mosterdslop/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-26T13:41:28+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3-8b-sft-qlora-re
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 100
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "llama3-8b-sft-qlora-re", "results": []}]} | xahilmalik/llama3-8b-sft-qlora-re | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
]
| null | 2024-04-26T13:41:30+00:00 |
null | null | {"license": "openrail"} | Danikdsa/Yoona | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-26T13:41:44+00:00 |
|
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 31889 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.ContrastiveLoss.ContrastiveLoss` with parameters:
```
{'distance_metric': 'SiameseDistanceMetric.COSINE_DISTANCE', 'margin': 0.5, 'size_average': True}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 3188,
"evaluator": "utils.ToponymResolutionEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | dguzh/geo-all-distilroberta-v1 | null | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:42:11+00:00 |
null | null | {} | shreyshah/phibit-128k-instruct-GGUF | null | [
"region:us"
]
| null | 2024-04-26T13:42:19+00:00 |
|
token-classification | transformers | {"license": "mit"} | PDBEurope/Bioformer8L-ProteinStructure-NER-v0.1_quantized | null | [
"transformers",
"onnx",
"bert",
"token-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:42:20+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# dsfdsf2/distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.8581
- Validation Loss: 3.6729
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.8581 | 3.6729 | 0 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.16.1
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilgpt2", "model-index": [{"name": "dsfdsf2/distilgpt2-finetuned-wikitext2", "results": []}]} | dsfdsf2/distilgpt2-finetuned-wikitext2 | null | [
"transformers",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:45:07+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/chargoddard/llama3-42b-v0
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/llama3-42b-v0-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ1_S.gguf) | i1-IQ1_S | 9.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ1_M.gguf) | i1-IQ1_M | 10.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 13.2 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ2_S.gguf) | i1-IQ2_S | 13.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ2_M.gguf) | i1-IQ2_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q2_K.gguf) | i1-Q2_K | 16.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 17.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 18.2 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 19.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ3_S.gguf) | i1-IQ3_S | 19.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ3_M.gguf) | i1-IQ3_M | 19.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 21.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 22.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q4_0.gguf) | i1-Q4_0 | 24.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 24.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 26.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 29.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 30.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3-42b-v0-i1-GGUF/resolve/main/llama3-42b-v0.i1-Q6_K.gguf) | i1-Q6_K | 35.5 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["axolotl", "mergekit", "llama"], "datasets": ["JeanKaddour/minipile"], "base_model": "chargoddard/llama3-42b-v0", "quantized_by": "mradermacher"} | mradermacher/llama3-42b-v0-i1-GGUF | null | [
"transformers",
"gguf",
"axolotl",
"mergekit",
"llama",
"en",
"dataset:JeanKaddour/minipile",
"base_model:chargoddard/llama3-42b-v0",
"license:llama3",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:46:07+00:00 |
null | transformers |
# itayl/Hebrew-Mistral-7B-Q5_K_M-GGUF
This model was converted to GGUF format from [`yam-peleg/Hebrew-Mistral-7B`](https://huggingface.co/yam-peleg/Hebrew-Mistral-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/yam-peleg/Hebrew-Mistral-7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo itayl/Hebrew-Mistral-7B-Q5_K_M-GGUF --model hebrew-mistral-7b.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo itayl/Hebrew-Mistral-7B-Q5_K_M-GGUF --model hebrew-mistral-7b.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m hebrew-mistral-7b.Q5_K_M.gguf -n 128
```
| {"language": ["en", "he"], "license": "apache-2.0", "library_name": "transformers", "tags": ["llama-cpp", "gguf-my-repo"]} | itayl/Hebrew-Mistral-7B-Q5_K_M-GGUF | null | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"he",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:46:49+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Likich/llama3-finetune-qualcoding | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:47:16+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** richie-ghost
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | richie-ghost/llama-3b-unsloth-quantized_merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:48:53+00:00 |
null | null | {} | fanino/repo_name | null | [
"region:us"
]
| null | 2024-04-26T13:48:56+00:00 |
|
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# job_postings_mlm_model_450k
This model is a fine-tuned version of [giyoung-kwon-0902/job_postings_mlm_model_400k](https://huggingface.co/giyoung-kwon-0902/job_postings_mlm_model_400k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1113
## 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: 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.153 | 1.0 | 17544 | 0.1361 |
| 0.1215 | 2.0 | 35088 | 0.1113 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "giyoung-kwon-0902/job_postings_mlm_model_400k", "model-index": [{"name": "job_postings_mlm_model_450k", "results": []}]} | giyoung-kwon-0902/job_postings_mlm_model_450k | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:giyoung-kwon-0902/job_postings_mlm_model_400k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:49:01+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | pesc101/Mistral-7B-Instruct-v0.2-lbl-2x | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:49:53+00:00 |
text-generation | transformers | {} | mayankkeshari/openassistant-guanaco-1k-finetuned | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:52:12+00:00 |
|
null | null | {} | wcvz/esm2_t130_150M-lora-classifier_2024-04-26_09-52-47 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T13:52:47+00:00 |
|
null | null | {"license": "apache-2.0"} | zevilife/test | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T13:52:51+00:00 |
|
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Zangs3011/llama3_8B_norobots
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/llama3_8B_norobots-GGUF/resolve/main/llama3_8B_norobots.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": [], "base_model": "Zangs3011/llama3_8B_norobots", "quantized_by": "mradermacher"} | mradermacher/llama3_8B_norobots-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:Zangs3011/llama3_8B_norobots",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:54:10+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/aipib/sakana-dareties2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/sakana-dareties2-GGUF/resolve/main/sakana-dareties2.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["merge", "mergekit", "lazymergekit", "stabilityai/japanese-stablelm-base-gamma-7b", "augmxnt/shisa-gamma-7b-v1"], "base_model": "aipib/sakana-dareties2", "quantized_by": "mradermacher"} | mradermacher/sakana-dareties2-GGUF | null | [
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"stabilityai/japanese-stablelm-base-gamma-7b",
"augmxnt/shisa-gamma-7b-v1",
"en",
"base_model:aipib/sakana-dareties2",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:54:41+00:00 |
null | null | {} | saimdev/custom_urdu_tts_model_refined_7 | null | [
"region:us"
]
| null | 2024-04-26T13:54:43+00:00 |
|
null | null | Just an imatrix quant of https://huggingface.co/jeiku/Fett-uccine_Mini_3B_GGUF to use on non-flagship smartphones. | {} | BlueNipples/Fett-uccine_Mini_3B-q2k-imat_GGUF | null | [
"gguf",
"region:us"
]
| null | 2024-04-26T13:54:52+00:00 |
null | null | {} | bakkensus/mistral-zeroshot-silver-gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-26T13:55:59+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat
This model is a fine-tuned version of [sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat](https://huggingface.co/sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1555
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 256
- total_eval_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1709 | 1.0 | 545 | 1.1553 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "alignment-handbook", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat", "model-index": [{"name": "sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat", "results": []}]} | sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat-200k | null | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:sanchit-gandhi/distil-zephyr-1.5b-ssft-ultrachat",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T13:56:34+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Rimyy/TentativeGemma1epEv | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:57:43+00:00 |
null | null | {"language": ["en"], "license": "apache-2.0", "tags": ["llava-next"]} | MatchboxAI/llava-next-inference | null | [
"llava-next",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:57:59+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** sravaniayyagari
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b"} | sravaniayyagari/llama3_finetuned_1 | null | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:58:01+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | FounderNest/mistral-7b-instruct-classifier-fit-assessment-finetuned-v3.4 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:58:05+00:00 |
null | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# RNAMamba-14M-Contrastive
This model is a fine-tuned version of [afg1/RNAMamba-14M](https://huggingface.co/afg1/RNAMamba-14M) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "afg1/RNAMamba-14M", "model-index": [{"name": "RNAMamba-14M-Contrastive", "results": []}]} | afg1/RNAMamba-14M-Contrastive | null | [
"transformers",
"safetensors",
"mamba",
"generated_from_trainer",
"base_model:afg1/RNAMamba-14M",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:58:27+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** richie-ghost
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | richie-ghost/llama-3b-unsloth-quantized_lora | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
]
| null | 2024-04-26T13:58:58+00:00 |
token-classification | transformers | {"license": "mit"} | PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1_onnx | null | [
"transformers",
"onnx",
"bert",
"token-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T13:59:01+00:00 |
|
null | null | {"license": "llama3"} | prakhar-b/llama3-test1 | null | [
"license:llama3",
"region:us"
]
| null | 2024-04-26T13:59:20+00:00 |
|
null | null | {} | Xrunner/hive-za | null | [
"region:us"
]
| null | 2024-04-26T13:59:57+00:00 |
|
null | null | {} | HenryCai1129/adapter-llama-adaptertoxic2nontoxic-100-50-0.009 | null | [
"region:us"
]
| null | 2024-04-26T14:00:26+00:00 |
|
text-generation | transformers | {} | simonycl/self-seq-Llama-2-7b-hf-new-without-ab | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:00:26+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-hf-platypus_vxxiii-chat-added_lamini_v2
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 6
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.1
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-7b-hf-platypus_vxxiii-chat-added_lamini_v2", "results": []}]} | NassimB/mistral-7b-hf-platypus_vxxiii-chat-added_lamini_v2 | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T14:01:24+00:00 |
text-generation | transformers | <!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with awq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo cognitivecomputations/dolphin-2.9-llama3-8b installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install autoawq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from awq import AutoAWQForCausalLM
model = AutoAWQForCausalLM.from_quantized("PrunaAI/cognitivecomputations-dolphin-2.9-llama3-8b-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("cognitivecomputations/dolphin-2.9-llama3-8b")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model cognitivecomputations/dolphin-2.9-llama3-8b before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). | {"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg", "base_model": "cognitivecomputations/dolphin-2.9-llama3-8b"} | PrunaAI/cognitivecomputations-dolphin-2.9-llama3-8b-AWQ-4bit-smashed | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-26T14:04:34+00:00 |
token-classification | transformers |
# SOTA Entity Recognition English Foundation Model by NuMind 🔥
This model provides the best embedding for the Entity Recognition task in English. It is an improved version of the model from our [**paper**](https://arxiv.org/abs/2402.15343).
**Checkout other models by NuMind:**
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## About
[Roberta-base](https://huggingface.co/roberta-base) fine-tuned on the expanded version of [NuNER data](https://huggingface.co/datasets/numind/NuNER) using contrastive learning from [**NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data**](https://arxiv.org/abs/2402.15343).
**Metrics:**
Read more about evaluation protocol & datasets in our [NuNER data](https://huggingface.co/datasets/numind/NuNER) using contrastive learning from [**paper**](https://arxiv.org/abs/2402.15343).
Here is the aggregated performance of the models over several datasets:
k=X means that as training data, we took only X examples for each class, trained the model, and evaluated it on the full test set.
| Model | k=1 | k=4 | k=16 | k=64 |
|----------|----------|----------|----------|----------|
| RoBERTa-base | 24.5 | 44.7 | 58.1 | 65.4
| RoBERTa-base + NER-BERT pre-training | 32.3 | 50.9 | 61.9 | 67.6 |
| NuNER v0.1 | 34.3 | 54.6 | 64.0 | 68.7 |
| NuNER v1.0 | 39.4 | 59.6 | 67.8 | 71.5 |
| **NuNER v2.0** | **43.6** | **61.0** | **68.2** | **72.0** |
NuNER v1.0 has similar performance to 7B LLMs (70 times bigger than NuNER v1.0) created specifically for the NER task. Thus NuNER v2.0 should be even better than the 7b LLM.
| Model | k=8~16| k=64~128 |
|----------|----------|----------|
| UniversalNER (7B) | 57.89 ± 4.34 | 71.02 ± 1.53 |
| NuNER v1.0 (100M) | 58.75 ± 0.93 | 70.30 ± 0.35 |
## Usage
Embeddings can be used out of the box or fine-tuned on specific datasets.
Get embeddings:
```python
import torch
import transformers
model = transformers.AutoModel.from_pretrained(
'numind/NuNER-v2.0'
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
'numind/NuNER-v2.0'
)
text = [
"NuMind is an AI company based in Paris and USA.",
"See other models from us on https://huggingface.co/numind"
]
encoded_input = tokenizer(
text,
return_tensors='pt',
padding=True,
truncation=True
)
output = model(**encoded_input)
emb = output.last_hidden_state
```
## Citation
```
@misc{bogdanov2024nuner,
title={NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated Data},
author={Sergei Bogdanov and Alexandre Constantin and Timothée Bernard and Benoit Crabbé and Etienne Bernard},
year={2024},
eprint={2402.15343},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | {"language": ["en"], "license": "mit", "tags": ["token-classification", "entity-recognition", "foundation-model", "feature-extraction", "RoBERTa", "generic"], "datasets": ["numind/NuNER"], "pipeline_tag": "token-classification", "inference": false} | numind/NuNER-v2.0 | null | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"token-classification",
"entity-recognition",
"foundation-model",
"RoBERTa",
"generic",
"en",
"dataset:numind/NuNER",
"arxiv:2402.15343",
"license:mit",
"region:us"
]
| null | 2024-04-26T14:06:13+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GPT2WaP
This model is a [gpt2](https://huggingface.co/gpt2) model trained from scratch on the War and peace book.
It achieves the following results on the evaluation set:
- Loss: 9.0987
- Perplexity: 8943.6289
## 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: 64
- eval_batch_size: 64
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Perplexity |
|:-------------:|:-------:|:----:|:---------------:|:----------:|
| 10.157 | 0.6897 | 10 | 9.2336 | 10235.7480 |
| 9.2581 | 1.3793 | 20 | 8.9452 | 7671.1870 |
| 8.8166 | 2.0690 | 30 | 9.4917 | 13248.7207 |
| 8.5094 | 2.7586 | 40 | 9.5417 | 13928.9434 |
| 8.0914 | 3.4483 | 50 | 9.5507 | 14054.4785 |
| 7.663 | 4.1379 | 60 | 9.4760 | 13043.2441 |
| 7.3275 | 4.8276 | 70 | 9.3510 | 11510.8203 |
| 6.9788 | 5.5172 | 80 | 9.0822 | 8797.7188 |
| 6.6639 | 6.2069 | 90 | 8.9803 | 7945.4014 |
| 6.3749 | 6.8966 | 100 | 8.6494 | 5706.8130 |
| 6.0702 | 7.5862 | 110 | 8.5696 | 5268.9268 |
| 5.9107 | 8.2759 | 120 | 8.3612 | 4277.6265 |
| 5.6724 | 8.9655 | 130 | 8.4294 | 4579.6484 |
| 5.5949 | 9.6552 | 140 | 8.4934 | 4882.4316 |
| 5.4904 | 10.3448 | 150 | 8.4683 | 4761.3862 |
| 5.3792 | 11.0345 | 160 | 8.4647 | 4744.5381 |
| 5.3091 | 11.7241 | 170 | 8.5767 | 5306.3535 |
| 5.233 | 12.4138 | 180 | 8.5257 | 5042.5068 |
| 5.2252 | 13.1034 | 190 | 8.5328 | 5078.8433 |
| 5.1445 | 13.7931 | 200 | 8.5871 | 5361.9390 |
| 5.0824 | 14.4828 | 210 | 8.5784 | 5315.4043 |
| 5.0272 | 15.1724 | 220 | 8.6434 | 5672.6934 |
| 4.979 | 15.8621 | 230 | 8.6836 | 5905.4277 |
| 4.924 | 16.5517 | 240 | 8.7112 | 6070.2261 |
| 4.9394 | 17.2414 | 250 | 8.7233 | 6144.3931 |
| 4.8663 | 17.9310 | 260 | 8.7411 | 6254.5234 |
| 4.8599 | 18.6207 | 270 | 8.7824 | 6518.7896 |
| 4.8572 | 19.3103 | 280 | 8.8338 | 6862.5586 |
| 4.8064 | 20.0 | 290 | 8.7774 | 6485.7441 |
| 4.746 | 20.6897 | 300 | 8.8458 | 6944.8892 |
| 4.7569 | 21.3793 | 310 | 8.8436 | 6930.1416 |
| 4.6954 | 22.0690 | 320 | 8.8618 | 7057.1084 |
| 4.7277 | 22.7586 | 330 | 8.8706 | 7119.4478 |
| 4.6432 | 23.4483 | 340 | 8.9084 | 7393.6138 |
| 4.6032 | 24.1379 | 350 | 8.9111 | 7413.5176 |
| 4.6198 | 24.8276 | 360 | 8.9526 | 7728.0210 |
| 4.5874 | 25.5172 | 370 | 8.9740 | 7895.1641 |
| 4.5455 | 26.2069 | 380 | 8.9365 | 7604.7129 |
| 4.5313 | 26.8966 | 390 | 8.9738 | 7893.2969 |
| 4.5297 | 27.5862 | 400 | 8.9659 | 7831.8110 |
| 4.5279 | 28.2759 | 410 | 8.9914 | 8034.0391 |
| 4.4974 | 28.9655 | 420 | 9.0293 | 8344.2529 |
| 4.4554 | 29.6552 | 430 | 9.0191 | 8259.1533 |
| 4.4651 | 30.3448 | 440 | 9.0236 | 8296.4531 |
| 4.4647 | 31.0345 | 450 | 9.0349 | 8391.1279 |
| 4.4668 | 31.7241 | 460 | 9.0530 | 8543.8340 |
| 4.4264 | 32.4138 | 470 | 9.0722 | 8709.4141 |
| 4.4008 | 33.1034 | 480 | 9.0876 | 8844.6104 |
| 4.3982 | 33.7931 | 490 | 9.0711 | 8700.4893 |
| 4.3846 | 34.4828 | 500 | 9.0894 | 8860.7441 |
| 4.3971 | 35.1724 | 510 | 9.0879 | 8847.6973 |
| 4.379 | 35.8621 | 520 | 9.0949 | 8909.6025 |
| 4.3696 | 36.5517 | 530 | 9.1097 | 9042.2295 |
| 4.3447 | 37.2414 | 540 | 9.1007 | 8961.6953 |
| 4.3796 | 37.9310 | 550 | 9.0869 | 8839.0781 |
| 4.364 | 38.6207 | 560 | 9.0987 | 8943.6289 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "GPT2WaP", "results": []}]} | Kasdeja23/GPT2WaP | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:06:14+00:00 |
null | null | {} | sm09-dev/Beyondv4-neg | null | [
"region:us"
]
| null | 2024-04-26T14:06:25+00:00 |
|
null | null | {"license": "unknown"} | LucasHolo/123 | null | [
"license:unknown",
"region:us"
]
| null | 2024-04-26T14:06:39+00:00 |
|
token-classification | transformers | {"language": ["en"], "license": "mit", "tags": ["biology", "protein structure", "token classification"], "widget": [{"text": "N-terminal acetylation (Nt-acetylation), carried out by N-terminal acetyltransferases (NATs), is a conserved and primary modification of nascent peptide chains. Naa60 (also named NatF) is a recently identified NAT found only in multicellular eukaryotes. This protein was shown to locate on the Golgi apparatus and mainly catalyze the Nt-acetylation of transmembrane proteins, and it also harbors lysine N\u03b5-acetyltransferase (KAT) activity to catalyze the acetylation of lysine \u03b5-amine. Here, we report the crystal structures of human Naa60 (hNaa60) in complex with Acetyl-Coenzyme A (Ac-CoA) or Coenzyme A (CoA). The hNaa60 protein contains an amphipathic helix following its GNAT domain that may contribute to Golgi localization of hNaa60, and the \u03b27-\u03b28 hairpin adopted different conformations in the hNaa60(1-242) and hNaa60(1-199) crystal structures. Remarkably, we found that the side-chain of Phe 34 can influence the position of the coenzyme, indicating a new regulatory mechanism involving enzyme, co-factor and substrates interactions. Moreover, structural comparison and biochemical studies indicated that Tyr 97 and His 138 are key residues for catalytic reaction and that a non-conserved \u03b23-\u03b24 long loop participates in the regulation of hNaa60 activity."}]} | PDBEurope/BiomedNLP-PubMedBERT-ProteinStructure-NER-v2.1_quantized | null | [
"transformers",
"onnx",
"bert",
"token-classification",
"biology",
"protein structure",
"token classification",
"en",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:06:57+00:00 |
|
image-classification | transformers | {"license": "apache-2.0", "datasets": ["imagenet-1k"]} | mpiorczynski/relu-vit-base-patch16-224 | null | [
"transformers",
"safetensors",
"vit",
"image-classification",
"dataset:imagenet-1k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:06:59+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper large urdu - huzaifa
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common 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: 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: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"language": ["ur"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper large urdu - huzaifa", "results": []}]} | huzaifa1117/whisper-large-urdu-3 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ur",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:07:49+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-hf-platypus_vxxiii-chat-added_lamini_v3
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 6
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.1
- Pytorch 2.2.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "mistral-7b-hf-platypus_vxxiii-chat-added_lamini_v3", "results": []}]} | NassimB/mistral-7b-hf-platypus_vxxiii-chat-added_lamini_v3 | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T14:08:32+00:00 |
text-generation | transformers |
# MixtureOfPhi3
<p align="center">
<img src="https://substackcdn.com/image/fetch/f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F11201acc-4089-416d-921b-cbd71fbf8ddb_1024x1024.jpeg" width="300" class="center"/>
</p>
**MixtureOfPhi3** is a Mixure of Experts (MoE) made with the following models using mergekit:
* [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
* [Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
This has been created using [LazyMergekit-Phi3](https://colab.research.google.com/drive/1Upb8JOAS3-K-iemblew34p9h1H6wtCeU?usp=sharing)
This run is only for development purposes, since merging 2 identical models does not bring any performance benefits, but once specialized finetunes of Phi3 models will be available, it will be a starting point for creating MoE from them.
## ©️ Credits
* [mlabonne's phixtral](https://huggingface.co/mlabonne/phixtral-4x2_8) where I adapted the inference code to Phi3's architecture.
* [mergekit](https://github.com/cg123/mergekit) code which I tweaked to merge Phi3s
These have been merged using `cheap_embed` where each model is assigned a vector representation of words - such as experts for scientific work, reasoning, math etc.
Try your own in the link above !
## 🧩 Configuration
```yaml
base_model: microsoft/Phi-3-mini-128k-instruct
gate_mode: cheap_embed
dtype: float16
experts:
- source_model: microsoft/Phi-3-mini-128k-instruct
positive_prompts: ["research, logic, math, science"]
- source_model: microsoft/Phi-3-mini-128k-instruct
positive_prompts: ["creative, art"]
```
## 💻 Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model = "paulilioaica/MixtureOfPhi3"
tokenizer = AutoTokenizer.from_pretrained(model)
model = AutoModelForCausalLM.from_pretrained(
model,
trust_remote_code=True,
)
prompt="How many continents are there?"
input = f"<|system|>\nYou are a helpful AI assistant.<|end|>\n<|user|>{prompt}\n<|assistant|>"
tokenized_input = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(tokenized_input, max_new_tokens=128, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(tokenizer.decode(outputs[0]))
``` | {"license": "apache-2.0", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "phi3_mergekit", "microsoft/Phi-3-mini-128k-instruct"], "base_model": ["microsoft/Phi-3-mini-128k-instruct", "microsoft/Phi-3-mini-128k-instruct"]} | paulilioaica/MixtureOfPhi3 | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"phi3_mergekit",
"microsoft/Phi-3-mini-128k-instruct",
"conversational",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:08:38+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# esm2_t130_150M-lora-classifier_2024-04-26_10-08-51
This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4537
- Accuracy: 0.8984
## 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: 0.0008701568055793088
- train_batch_size: 28
- eval_batch_size: 28
- seed: 8893
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6764 | 1.0 | 55 | 0.6794 | 0.5820 |
| 0.5521 | 2.0 | 110 | 0.6192 | 0.6777 |
| 0.5409 | 3.0 | 165 | 0.5147 | 0.7383 |
| 0.5518 | 4.0 | 220 | 0.3518 | 0.8672 |
| 0.1386 | 5.0 | 275 | 0.3596 | 0.8574 |
| 0.303 | 6.0 | 330 | 0.4030 | 0.8359 |
| 0.1962 | 7.0 | 385 | 0.3143 | 0.8848 |
| 0.1501 | 8.0 | 440 | 0.3232 | 0.8652 |
| 0.2994 | 9.0 | 495 | 0.3014 | 0.8770 |
| 0.0914 | 10.0 | 550 | 0.2980 | 0.8887 |
| 0.2108 | 11.0 | 605 | 0.2854 | 0.8770 |
| 0.2896 | 12.0 | 660 | 0.3684 | 0.8691 |
| 0.0818 | 13.0 | 715 | 0.3349 | 0.8828 |
| 0.3152 | 14.0 | 770 | 0.3530 | 0.8848 |
| 0.0554 | 15.0 | 825 | 0.3371 | 0.8887 |
| 0.1928 | 16.0 | 880 | 0.3347 | 0.875 |
| 0.2658 | 17.0 | 935 | 0.3765 | 0.8867 |
| 0.4242 | 18.0 | 990 | 0.4166 | 0.8945 |
| 0.0964 | 19.0 | 1045 | 0.3400 | 0.8945 |
| 0.0375 | 20.0 | 1100 | 0.3581 | 0.9004 |
| 0.1781 | 21.0 | 1155 | 0.3816 | 0.8848 |
| 0.1563 | 22.0 | 1210 | 0.3940 | 0.8867 |
| 0.017 | 23.0 | 1265 | 0.4098 | 0.8926 |
| 0.1866 | 24.0 | 1320 | 0.4710 | 0.8770 |
| 0.0632 | 25.0 | 1375 | 0.4541 | 0.8828 |
| 0.1501 | 26.0 | 1430 | 0.4645 | 0.8828 |
| 0.109 | 27.0 | 1485 | 0.4434 | 0.8926 |
| 0.0353 | 28.0 | 1540 | 0.4264 | 0.8984 |
| 0.4502 | 29.0 | 1595 | 0.4479 | 0.8984 |
| 0.0341 | 30.0 | 1650 | 0.4537 | 0.8984 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/esm2_t30_150M_UR50D", "model-index": [{"name": "esm2_t130_150M-lora-classifier_2024-04-26_10-08-51", "results": []}]} | wcvz/esm2_t130_150M-lora-classifier_2024-04-26_10-08-51 | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/esm2_t30_150M_UR50D",
"license:mit",
"region:us"
]
| null | 2024-04-26T14:08:51+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "PixelCopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "30.00 +/- 28.77", "name": "mean_reward", "verified": false}]}]}]} | i-pj/PixelCopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| null | 2024-04-26T14:09:19+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llava-1.5-7b-hf-ft-mix-vsft
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) 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: 1.4e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "llava-hf/llava-1.5-7b-hf", "model-index": [{"name": "llava-1.5-7b-hf-ft-mix-vsft", "results": []}]} | Praveen0309/llava-1.5-7b-hf-ft-mix-vsft | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:llava-hf/llava-1.5-7b-hf",
"region:us"
]
| null | 2024-04-26T14:09:47+00:00 |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.03381425514817238
f1_macro: 0.9910410929202866
f1_micro: 0.9908675799086758
f1_weighted: 0.9908473335613555
precision_macro: 0.9909727371947719
precision_micro: 0.9908675799086758
precision_weighted: 0.9908883151237302
recall_macro: 0.9911698494022667
recall_micro: 0.9908675799086758
recall_weighted: 0.9908675799086758
accuracy: 0.9908675799086758
| {"tags": ["autotrain", "text-classification"], "datasets": ["autotrain-pmf0g-rj8fa/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | borggAI/alpha-prompt-classification | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"autotrain",
"dataset:autotrain-pmf0g-rj8fa/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:09:49+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | FounderNest/Mistral-7B-Instruct-v0.2-AWQ-classifier-fit-assessment-finetuned-v3.4 | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:09:55+00:00 |
null | null | {} | sm09-dev/Asian-Less2-Neg | null | [
"region:us"
]
| null | 2024-04-26T14:10:11+00:00 |
|
text-generation | transformers | {} | jobvector/jv_entity_llm | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:10:25+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-3
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-3", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-130_IMDB_n-its-10-seed-3 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:11:19+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - fatimaaa1/model2
<Gallery />
## Model description
These are fatimaaa1/model2 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: fatimaaa1/model2/vae.
## Trigger words
You should use a bussiness card to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](fatimaaa1/model2/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a bussiness card", "widget": []} | fatimaaa1/model2 | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| null | 2024-04-26T14:11:19+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Ayon128/code-mixed_Banglish_English_0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:11:31+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# privacy-200k-masking
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0949
- eval_overall_precision: 0.9099
- eval_overall_recall: 0.9306
- eval_overall_f1: 0.9201
- eval_overall_accuracy: 0.9692
- eval_ACCOUNTNAME_f1: 0.9863
- eval_ACCOUNTNUMBER_f1: 0.9551
- eval_AGE_f1: 0.9454
- eval_AMOUNT_f1: 0.9481
- eval_BIC_f1: 0.9140
- eval_BITCOINADDRESS_f1: 0.9227
- eval_BUILDINGNUMBER_f1: 0.9056
- eval_CITY_f1: 0.9351
- eval_COMPANYNAME_f1: 0.9621
- eval_COUNTY_f1: 0.9756
- eval_CREDITCARDCVV_f1: 0.9201
- eval_CREDITCARDISSUER_f1: 0.9767
- eval_CREDITCARDNUMBER_f1: 0.8506
- eval_CURRENCY_f1: 0.7277
- eval_CURRENCYCODE_f1: 0.8398
- eval_CURRENCYNAME_f1: 0.1576
- eval_CURRENCYSYMBOL_f1: 0.9216
- eval_DATE_f1: 0.7988
- eval_DOB_f1: 0.6103
- eval_EMAIL_f1: 0.9862
- eval_ETHEREUMADDRESS_f1: 0.9624
- eval_EYECOLOR_f1: 0.9779
- eval_FIRSTNAME_f1: 0.9636
- eval_GENDER_f1: 0.9852
- eval_HEIGHT_f1: 0.9771
- eval_IBAN_f1: 0.9513
- eval_IP_f1: 0.0
- eval_IPV4_f1: 0.8240
- eval_IPV6_f1: 0.7389
- eval_JOBAREA_f1: 0.9713
- eval_JOBTITLE_f1: 0.9819
- eval_JOBTYPE_f1: 0.9743
- eval_LASTNAME_f1: 0.9439
- eval_LITECOINADDRESS_f1: 0.8069
- eval_MAC_f1: 0.9668
- eval_MASKEDNUMBER_f1: 0.8084
- eval_MIDDLENAME_f1: 0.9401
- eval_NEARBYGPSCOORDINATE_f1: 0.9963
- eval_ORDINALDIRECTION_f1: 0.9904
- eval_PASSWORD_f1: 0.9690
- eval_PHONEIMEI_f1: 0.9842
- eval_PHONENUMBER_f1: 0.9690
- eval_PIN_f1: 0.8584
- eval_PREFIX_f1: 0.9594
- eval_SECONDARYADDRESS_f1: 0.9880
- eval_SEX_f1: 0.9952
- eval_SSN_f1: 0.9813
- eval_STATE_f1: 0.9664
- eval_STREET_f1: 0.9607
- eval_TIME_f1: 0.9560
- eval_URL_f1: 0.9866
- eval_USERAGENT_f1: 0.9901
- eval_USERNAME_f1: 0.9743
- eval_VEHICLEVIN_f1: 0.9699
- eval_VEHICLEVRM_f1: 0.9725
- eval_ZIPCODE_f1: 0.9018
- eval_runtime: 3609.2787
- eval_samples_per_second: 17.394
- eval_steps_per_second: 8.697
- epoch: 1.0
- step: 73241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 2
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-multilingual-cased", "model-index": [{"name": "privacy-200k-masking", "results": []}]} | taro-pudding/privacy-200k-masking | null | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:11:34+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | savanladani/nividous-7b-sft-lora | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:12:42+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Ayon128/code-mixed_Banglish_English_1 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:13:14+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speech_ocean_hubert_mdd
This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2027
- Wer: 0.0517
- Cer: 0.0499
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 42.7069 | 0.9873 | 39 | 36.7247 | 0.9992 | 0.9977 |
| 16.2787 | 2.0 | 79 | 7.8315 | 1.0 | 1.0 |
| 6.7896 | 2.9873 | 118 | 4.5645 | 1.0 | 1.0 |
| 4.0104 | 4.0 | 158 | 3.8654 | 1.0 | 1.0 |
| 3.8037 | 4.9873 | 197 | 3.8060 | 1.0 | 1.0 |
| 3.7898 | 6.0 | 237 | 3.7695 | 1.0 | 1.0 |
| 3.7777 | 6.9873 | 276 | 3.7717 | 1.0 | 1.0 |
| 3.7442 | 8.0 | 316 | 3.7320 | 1.0 | 1.0 |
| 3.7286 | 8.9873 | 355 | 3.6978 | 1.0 | 1.0 |
| 3.6272 | 10.0 | 395 | 3.5089 | 1.0 | 1.0 |
| 3.0921 | 10.9873 | 434 | 2.6068 | 0.9992 | 0.9997 |
| 2.2556 | 12.0 | 474 | 1.6832 | 0.5880 | 0.6815 |
| 1.7791 | 12.9873 | 513 | 1.2117 | 0.3861 | 0.4433 |
| 1.2731 | 14.0 | 553 | 0.7338 | 0.1793 | 0.1505 |
| 0.9596 | 14.9873 | 592 | 0.4892 | 0.1220 | 0.1005 |
| 0.7152 | 16.0 | 632 | 0.3525 | 0.0892 | 0.0752 |
| 0.521 | 16.9873 | 671 | 0.2843 | 0.0704 | 0.0623 |
| 0.4791 | 18.0 | 711 | 0.2351 | 0.0607 | 0.0568 |
| 0.3992 | 18.9873 | 750 | 0.2120 | 0.0547 | 0.0523 |
| 0.4245 | 19.7468 | 780 | 0.2027 | 0.0517 | 0.0499 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "facebook/hubert-large-ll60k", "model-index": [{"name": "speech_ocean_hubert_mdd", "results": []}]} | nrshoudi/speech_ocean_hubert_mdd | null | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/hubert-large-ll60k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:13:20+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Ayon128/code-mixed_Banglish_English_2 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:14:20+00:00 |
null | null | {} | sm09-dev/realisticvision-negative-embedding | null | [
"region:us"
]
| null | 2024-04-26T14:14:31+00:00 |
|
null | null | {} | Kayoru03314/yoimiya | null | [
"region:us"
]
| null | 2024-04-26T14:16:37+00:00 |
|
visual-question-answering | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Entreprenerdly/blip2-opt-2.7b-fp16-sharded | null | [
"transformers",
"safetensors",
"blip-2",
"visual-question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"8-bit",
"region:us"
]
| null | 2024-04-26T14:17:08+00:00 |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-dmae-va-U5-100-iN
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6381
- Accuracy: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.9 | 7 | 1.3812 | 0.45 |
| 1.3848 | 1.94 | 15 | 1.3606 | 0.5 |
| 1.3686 | 2.97 | 23 | 1.3075 | 0.5333 |
| 1.2965 | 4.0 | 31 | 1.2370 | 0.4667 |
| 1.2965 | 4.9 | 38 | 1.1168 | 0.5333 |
| 1.1753 | 5.94 | 46 | 1.0310 | 0.5667 |
| 1.0294 | 6.97 | 54 | 0.9316 | 0.6 |
| 0.902 | 8.0 | 62 | 0.8728 | 0.6833 |
| 0.902 | 8.9 | 69 | 0.8129 | 0.7667 |
| 0.7812 | 9.94 | 77 | 0.7006 | 0.8 |
| 0.6419 | 10.97 | 85 | 0.6381 | 0.8667 |
| 0.5109 | 12.0 | 93 | 0.6327 | 0.8167 |
| 0.3838 | 12.9 | 100 | 0.5442 | 0.8667 |
| 0.3838 | 13.94 | 108 | 0.6755 | 0.75 |
| 0.285 | 14.97 | 116 | 0.7756 | 0.7167 |
| 0.2672 | 16.0 | 124 | 0.8107 | 0.7167 |
| 0.2466 | 16.9 | 131 | 0.5219 | 0.8333 |
| 0.2466 | 17.94 | 139 | 0.7041 | 0.7833 |
| 0.2312 | 18.97 | 147 | 0.7879 | 0.75 |
| 0.1933 | 20.0 | 155 | 0.7090 | 0.8 |
| 0.1692 | 20.9 | 162 | 0.5395 | 0.8333 |
| 0.1578 | 21.94 | 170 | 0.6419 | 0.8167 |
| 0.1578 | 22.97 | 178 | 0.5736 | 0.8333 |
| 0.1321 | 24.0 | 186 | 0.7471 | 0.75 |
| 0.1114 | 24.9 | 193 | 0.6447 | 0.7667 |
| 0.1385 | 25.94 | 201 | 0.6158 | 0.8167 |
| 0.1385 | 26.97 | 209 | 0.6467 | 0.8 |
| 0.1136 | 28.0 | 217 | 0.6180 | 0.85 |
| 0.0997 | 28.9 | 224 | 0.8578 | 0.75 |
| 0.1064 | 29.94 | 232 | 0.6778 | 0.8167 |
| 0.0775 | 30.97 | 240 | 0.8124 | 0.8 |
| 0.0775 | 32.0 | 248 | 0.7783 | 0.8 |
| 0.0921 | 32.9 | 255 | 0.8320 | 0.7333 |
| 0.0919 | 33.94 | 263 | 0.8310 | 0.7833 |
| 0.0888 | 34.97 | 271 | 0.6576 | 0.85 |
| 0.0888 | 36.0 | 279 | 0.7044 | 0.8333 |
| 0.0693 | 36.9 | 286 | 0.7608 | 0.8167 |
| 0.061 | 37.94 | 294 | 0.7802 | 0.8 |
| 0.0699 | 38.97 | 302 | 0.7762 | 0.8167 |
| 0.0652 | 40.0 | 310 | 0.7579 | 0.8 |
| 0.0652 | 40.9 | 317 | 0.9985 | 0.75 |
| 0.0562 | 41.94 | 325 | 0.8027 | 0.8167 |
| 0.0534 | 42.97 | 333 | 0.9705 | 0.7833 |
| 0.0519 | 44.0 | 341 | 0.7301 | 0.8333 |
| 0.0519 | 44.9 | 348 | 0.8433 | 0.8 |
| 0.0529 | 45.94 | 356 | 0.8534 | 0.8 |
| 0.0772 | 46.97 | 364 | 0.8562 | 0.8 |
| 0.0644 | 48.0 | 372 | 0.8419 | 0.8 |
| 0.0644 | 48.9 | 379 | 1.1251 | 0.7667 |
| 0.0467 | 49.94 | 387 | 0.7537 | 0.8333 |
| 0.0576 | 50.97 | 395 | 0.7517 | 0.8333 |
| 0.0344 | 52.0 | 403 | 0.8343 | 0.8 |
| 0.0663 | 52.9 | 410 | 0.7636 | 0.8 |
| 0.0663 | 53.94 | 418 | 0.8253 | 0.8167 |
| 0.0353 | 54.97 | 426 | 0.9348 | 0.8 |
| 0.0524 | 56.0 | 434 | 0.8217 | 0.8167 |
| 0.0479 | 56.9 | 441 | 0.7586 | 0.8167 |
| 0.0479 | 57.94 | 449 | 0.8147 | 0.8 |
| 0.0595 | 58.97 | 457 | 1.0000 | 0.7833 |
| 0.0475 | 60.0 | 465 | 0.9291 | 0.7833 |
| 0.049 | 60.9 | 472 | 0.9588 | 0.7833 |
| 0.0398 | 61.94 | 480 | 0.9501 | 0.8 |
| 0.0398 | 62.97 | 488 | 0.9499 | 0.8 |
| 0.0496 | 64.0 | 496 | 0.9279 | 0.8 |
| 0.0354 | 64.9 | 503 | 0.9677 | 0.75 |
| 0.0325 | 65.94 | 511 | 0.8371 | 0.8333 |
| 0.0325 | 66.97 | 519 | 0.9683 | 0.8 |
| 0.0335 | 68.0 | 527 | 1.0455 | 0.7833 |
| 0.0375 | 68.9 | 534 | 0.9027 | 0.8167 |
| 0.0424 | 69.94 | 542 | 0.8043 | 0.85 |
| 0.0383 | 70.97 | 550 | 0.9035 | 0.7833 |
| 0.0383 | 72.0 | 558 | 0.9360 | 0.7833 |
| 0.0295 | 72.9 | 565 | 0.9841 | 0.7833 |
| 0.0307 | 73.94 | 573 | 0.9300 | 0.8 |
| 0.0376 | 74.97 | 581 | 0.9630 | 0.7833 |
| 0.0376 | 76.0 | 589 | 0.9777 | 0.7833 |
| 0.0259 | 76.9 | 596 | 0.9323 | 0.8 |
| 0.0345 | 77.94 | 604 | 0.9075 | 0.8 |
| 0.0346 | 78.97 | 612 | 0.8951 | 0.8 |
| 0.0319 | 80.0 | 620 | 0.9676 | 0.8 |
| 0.0319 | 80.9 | 627 | 0.9884 | 0.8 |
| 0.0226 | 81.94 | 635 | 0.9851 | 0.7833 |
| 0.033 | 82.97 | 643 | 0.9710 | 0.7833 |
| 0.0262 | 84.0 | 651 | 0.9851 | 0.7833 |
| 0.0262 | 84.9 | 658 | 0.9868 | 0.7833 |
| 0.0345 | 85.94 | 666 | 0.9702 | 0.7833 |
| 0.0299 | 86.97 | 674 | 0.9889 | 0.7833 |
| 0.0347 | 88.0 | 682 | 1.0003 | 0.7833 |
| 0.0347 | 88.9 | 689 | 0.9913 | 0.7833 |
| 0.0288 | 89.94 | 697 | 0.9859 | 0.7833 |
| 0.0198 | 90.32 | 700 | 0.9858 | 0.7833 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224", "model-index": [{"name": "vit-base-patch16-224-dmae-va-U5-100-iN", "results": []}]} | Augusto777/vit-base-patch16-224-dmae-va-U5-100-iN | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:18:03+00:00 |
fill-mask | transformers |
<!-- 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. -->
# dsfdsf2/distilroberta-base-finetuned-wikitext2
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.1556
- Validation Loss: 1.8940
- 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 |
|:----------:|:---------------:|:-----:|
| 2.1556 | 1.8940 | 0 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.16.1
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilroberta-base", "model-index": [{"name": "dsfdsf2/distilroberta-base-finetuned-wikitext2", "results": []}]} | dsfdsf2/distilroberta-base-finetuned-wikitext2 | null | [
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"base_model:distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T14:18:20+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Shared by [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Ayon128/code-mixed_Banglish_English_4 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:18:24+00:00 |
text-generation | transformers | {} | fxmeng/PiSSA-Llama-3-8B-Instruct-r128 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:18:27+00:00 |
|
null | null | {} | andrealexroom/TaskRoomv0.0.0.1.5.5 | null | [
"safetensors",
"region:us"
]
| null | 2024-04-26T14:18:42+00:00 |
|
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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## 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. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Ayon128/code-mixed_Banglish_English_3 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T14:19:13+00:00 |
null | null | {} | sm09-dev/style-paintmagic | null | [
"region:us"
]
| null | 2024-04-26T14:19:13+00:00 |
|
null | null | {} | Xrunner/hive-zc | null | [
"region:us"
]
| null | 2024-04-26T14:20:02+00:00 |
|
null | null |
# sbawa/TinyLlama-1.1B-Chat-v1.0-intel-dpo-Q4_K_M-GGUF
This model was converted to GGUF format from [`davanstrien/TinyLlama-1.1B-Chat-v1.0-intel-dpo`](https://huggingface.co/davanstrien/TinyLlama-1.1B-Chat-v1.0-intel-dpo) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/davanstrien/TinyLlama-1.1B-Chat-v1.0-intel-dpo) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo sbawa/TinyLlama-1.1B-Chat-v1.0-intel-dpo-Q4_K_M-GGUF --model tinyllama-1.1b-chat-v1.0-intel-dpo.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo sbawa/TinyLlama-1.1B-Chat-v1.0-intel-dpo-Q4_K_M-GGUF --model tinyllama-1.1b-chat-v1.0-intel-dpo.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m tinyllama-1.1b-chat-v1.0-intel-dpo.Q4_K_M.gguf -n 128
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["dpo", "llama-cpp", "gguf-my-repo"], "datasets": ["argilla/distilabel-intel-orca-dpo-pairs"], "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | sbawa/TinyLlama-1.1B-Chat-v1.0-intel-dpo-Q4_K_M-GGUF | null | [
"gguf",
"dpo",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T14:20:02+00:00 |
text-generation | transformers |
# Qwen1.5-110B-Chat-GPTQ-Int4
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 9 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B, 72B, and 110B dense models, and an MoE model of 14B with 2.7B activated;
* Significant performance improvement in human preference for chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
<br>
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B and 110B) and the mixture of SWA and full attention.
## Training details
We pretrained the models with a large amount of data, and we post-trained the models with both supervised finetuning and direct preference optimization.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen1.5-110B-Chat-GPTQ-Int4",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-110B-Chat-GPTQ-Int4")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Tips
* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in `generation_config.json`.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
| {"language": ["en"], "license": "other", "tags": ["chat"], "license_name": "tongyi-qianwen", "license_link": "https://huggingface.co/Qwen/Qwen1.5-110B-Chat-GPTQ-Int4/blob/main/LICENSE", "pipeline_tag": "text-generation"} | Qwen/Qwen1.5-110B-Chat-GPTQ-Int4 | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chat",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-26T14:21:02+00:00 |
null | null | {} | RonenWeiz/encdec_model_73000_resume_from_checkpoint_66500 | null | [
"region:us"
]
| null | 2024-04-26T14:21:37+00:00 |
|
null | null | {} | saimdev/custom_urdu_tts_model_refined_8 | null | [
"region:us"
]
| null | 2024-04-26T14:21:42+00:00 |
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