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| likes
int64 0
11k
| library_name
stringclasses 245
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listlengths 1
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|---|---|---|---|---|---|---|---|---|---|
indj/unsloth_4bit | indj | 2024-06-28T13:01:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T13:00:48Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** indj
- **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)
|
Miguel46/Swap_face | Miguel46 | 2024-06-28T13:08:17Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"image-to-video",
"av",
"dataset:HuggingFaceFW/fineweb-edu",
"license:apache-2.0",
"region:us"
]
| image-to-video | 2024-06-28T13:04:10Z | ---
license: apache-2.0
datasets:
- HuggingFaceFW/fineweb-edu
language:
- av
metrics:
- character
library_name: adapter-transformers
pipeline_tag: image-to-video
---
---import cv2
import dlib
import numpy as np
import torch
from transformers import pipeline
# Carregar o detector de rostos
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
# Função para detectar e alinhar rostos
def detect_and_align_face(image_path):
img = cv2.imread(image_path)
gray = cv2
license: apache-2.0
--- |
lerle144/naruto-lora | lerle144 | 2024-06-28T13:06:44Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:06:44Z | Entry not found |
starnet/19-star21-06-28 | starnet | 2024-06-28T13:13:29Z | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
]
| null | 2024-06-28T13:06:50Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
habulaj/375657341353 | habulaj | 2024-06-28T13:07:20Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:07:10Z | Entry not found |
matrix88999/213123 | matrix88999 | 2024-06-28T13:08:00Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:07:32Z | from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("Ateeqq/Text-Rewriter-Paraphraser", token='your_token')
model = AutoModelForSeq2SeqLM.from_pretrained("Ateeqq/Text-Rewriter-Paraphraser", token='your_token').to(device)
def generate_title(text):
input_ids = tokenizer(f'paraphraser: {text}', return_tensors="pt", padding="longest", truncation=True, max_length=64).input_ids.to(device)
outputs = model.generate(
input_ids,
num_beams=4,
num_beam_groups=4,
num_return_sequences=4,
repetition_penalty=10.0,
diversity_penalty=3.0,
no_repeat_ngram_size=2,
temperature=0.8,
max_length=64
)
return tokenizer.batch_decode(outputs, skip_special_tokens=True)
text = 'By leveraging prior model training through transfer learning, fine-tuning can reduce the amount of expensive computing power and labeled data needed to obtain large models tailored to niche use cases and business needs.'
generate_title(text)
|
jerry283/blip-image-captioning-base-UASNLP2 | jerry283 | 2024-06-28T13:08:37Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:08:37Z | Entry not found |
peemsurat/Peem_Pitikron | peemsurat | 2024-06-28T13:11:56Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T13:08:57Z | ---
license: openrail
---
|
twright8/setfit-oversample-lobbying | twright8 | 2024-07-02T16:29:01Z | 0 | 0 | setfit | [
"setfit",
"safetensors",
"bert",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"model-index",
"region:us"
]
| text-classification | 2024-06-28T13:09:48Z | ---
library_name: setfit
metrics:
- f1
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: To make introductions between Camelot's Chairman and the Cabinet Secretary.
We discussed the operation of the UK National Lottery and how to maximise returns
to National Lottery Good Causes as well as our plans to celebrate the 25th birthday
of The National Lottery.
- text: Discussion on crime
- text: To discuss Northern Powerhouse Rail and HS2
- text: To discuss food security
- text: Electricity market
inference: false
model-index:
- name: SetFit
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: f1
value: 0.92
name: F1
- type: accuracy
value: 0.9658119658119658
name: Accuracy
---
# SetFit
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. A [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
<!-- - **Sentence Transformer:** [Unknown](https://huggingface.co/unknown) -->
- **Classification head:** a [SetFitHead](huggingface.co/docs/setfit/reference/main#setfit.SetFitHead) instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | F1 | Accuracy |
|:--------|:-----|:---------|
| **all** | 0.92 | 0.9658 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("twright8/setfit-oversample-lobbying")
# Run inference
preds = model("Electricity market")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 2 | 26.1406 | 153 |
### Training Hyperparameters
- batch_size: (16, 2)
- num_epochs: (4, 9)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (1.0797496673911536e-05, 3.457046714445997e-05)
- head_learning_rate: 0.0004470582121407239
- loss: CoSENTLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: True
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: True
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-------:|:-------:|:-------------:|:---------------:|
| 0.0040 | 1 | 19.1843 | - |
| 0.2024 | 50 | 11.3434 | - |
| 0.4049 | 100 | 9.3116 | - |
| 0.6073 | 150 | 2.7233 | - |
| 0.8097 | 200 | 1.5662 | - |
| **1.0** | **247** | **-** | **14.3603** |
| 1.0121 | 250 | 0.0159 | - |
| 1.2146 | 300 | 0.0135 | - |
| 1.4170 | 350 | 0.0003 | - |
| 1.6194 | 400 | 0.0002 | - |
| 1.8219 | 450 | 0.0007 | - |
| 2.0 | 494 | - | 16.8205 |
| 2.0243 | 500 | 0.0023 | - |
| 2.2267 | 550 | 0.0004 | - |
| 2.4291 | 600 | 0.0001 | - |
| 2.6316 | 650 | 0.0 | - |
| 2.8340 | 700 | 0.0003 | - |
| 3.0 | 741 | - | 15.2312 |
| 3.0364 | 750 | 0.0 | - |
| 3.2389 | 800 | 3.1257 | - |
| 3.4413 | 850 | 0.0001 | - |
| 3.6437 | 900 | 0.0002 | - |
| 3.8462 | 950 | 0.0139 | - |
| 4.0 | 988 | - | 14.4995 |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 3.0.1
- Transformers: 4.39.0
- PyTorch: 2.3.1+cu118
- Datasets: 2.20.0
- Tokenizers: 0.15.2
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
YeBhoneLin10/ronaldo | YeBhoneLin10 | 2024-06-28T13:10:55Z | 0 | 0 | diffusers | [
"diffusers",
"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"
]
| text-to-image | 2024-06-28T13:10:54Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- dora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
instance_prompt: a photo of bagan
widget: []
---
<!-- 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 - YeBhoneLin10/ronaldo
<Gallery />
## Model description
These are YeBhoneLin10/ronaldo 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: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of bagan to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](YeBhoneLin10/ronaldo/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] |
jiang0203/save | jiang0203 | 2024-06-28T13:11:34Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:11:34Z | Entry not found |
Rgbeast5678/Nikoro | Rgbeast5678 | 2024-06-28T13:47:33Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-3-medium",
"license:apache-2.0",
"region:us"
]
| text-to-image | 2024-06-28T13:17:46Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/1000030866.jpg
base_model: stabilityai/stable-diffusion-3-medium
instance_prompt: null
license: apache-2.0
---
# Nikoro
<Gallery />
## Download model
[Download](/Rgbeast5678/Nikoro/tree/main) them in the Files & versions tab.
|
cbhhhcb/SDXL | cbhhhcb | 2024-06-28T13:51:49Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:19:06Z | Entry not found |
missankasha53/diekoenige | missankasha53 | 2024-06-28T13:52:16Z | 0 | 0 | null | [
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T13:19:09Z | Entry not found |
casque/enne_v07 | casque | 2024-06-28T13:19:54Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2024-06-28T13:19:10Z | ---
license: creativeml-openrail-m
---
|
Swarts/Ozlemikro | Swarts | 2024-06-28T13:23:44Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:20:25Z | Entry not found |
cindylo/data | cindylo | 2024-06-28T13:22:20Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T13:22:20Z | ---
license: apache-2.0
---
|
kalinkov/Detectron2_FasterRCNN_R50_FPN_InsectDetection | kalinkov | 2024-06-28T13:30:11Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T13:23:38Z | ---
license: apache-2.0
---
|
soheill/llama-3-8b-chat-customer-service | soheill | 2024-07-01T11:47:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T13:25:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
habulaj/320420286857 | habulaj | 2024-06-28T13:27:38Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:27:34Z | Entry not found |
healtori/04-heal-06-28-02 | healtori | 2024-06-28T13:31:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-28T13:28:30Z | Entry not found |
Grayx/john_paul_van_damme_49 | Grayx | 2024-06-28T13:44:02Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:32:28Z | Entry not found |
mariovendas/treinounifahe321 | mariovendas | 2024-06-28T13:36:53Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-28T13:36:53Z | ---
license: mit
---
|
Lokomotiv/whisper-large-ru-test-isworking | Lokomotiv | 2024-06-28T13:38:01Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:38:01Z | Entry not found |
starnet/11-star-06-28-02 | starnet | 2024-06-28T13:42:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-28T13:39:23Z | Entry not found |
GraydientPlatformAPI/loras28 | GraydientPlatformAPI | 2024-06-28T13:44:59Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:41:57Z | Entry not found |
DokiQueen/Boxed | DokiQueen | 2024-06-28T14:10:41Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:42:23Z | Entry not found |
healtori/11-heal-06-28-02 | healtori | 2024-06-28T13:47:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-28T13:44:50Z | Entry not found |
fifala/14-fifa-06-28-02 | fifala | 2024-06-28T13:50:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-28T13:47:50Z | Entry not found |
kgBolt/quantized-llama-3-sqlcoder-8b | kgBolt | 2024-06-28T13:51:08Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:51:07Z | Entry not found |
kojongmo/XGBoost_S1_model.pkl | kojongmo | 2024-06-28T13:51:19Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:51:12Z |
# Random Forest Model
This is a Random Forest model trained on user data to predict multiple targets.
|
huhuhuhus/google-gemma-2b-1719582686 | huhuhuhus | 2024-06-28T13:51:27Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:51:27Z | Entry not found |
kojongmo/RandomForest_S2_model.pkl | kojongmo | 2024-06-28T13:51:38Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:51:35Z |
# Random Forest Model
This is a Random Forest model trained on user data to predict multiple targets.
|
kojongmo/RandomForest_S3_model.pkl | kojongmo | 2024-06-28T13:51:52Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:51:48Z |
# Random Forest Model
This is a Random Forest model trained on user data to predict multiple targets.
|
kojongmo/RandomForest_S4_model.pkl | kojongmo | 2024-06-28T13:52:07Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:52:03Z |
# Random Forest Model
This is a Random Forest model trained on user data to predict multiple targets.
|
PrunaAI/victunes-TherapyLlama-8B-v1-bnb-4bit-smashed | PrunaAI | 2024-06-28T13:54:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:victunes/TherapyLlama-8B-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2024-06-28T13:52:18Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- 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 llm-int8.
- ***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 HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install transformers accelerate bitsandbytes>0.37.0
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyLlama-8B-v1-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
kojongmo/LightGBM_Q1_model.pkl | kojongmo | 2024-06-28T13:52:23Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:52:21Z |
# Random Forest Model
This is a Random Forest model trained on user data to predict multiple targets.
|
kojongmo/LightGBM_Q2_model.pkl | kojongmo | 2024-06-28T13:52:38Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:52:34Z |
# Random Forest Model
This is a Random Forest model trained on user data to predict multiple targets.
|
kojongmo/RandomForest_Q3_model.pkl | kojongmo | 2024-06-28T13:52:46Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:52:43Z |
# Random Forest Model
This is a Random Forest model trained on user data to predict multiple targets.
|
Ewopally/my_awesome_wnut_model | Ewopally | 2024-06-28T13:53:01Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:53:01Z | Entry not found |
shuyuej/MedLLaMA3-70B-base-INT4-G1024-GPTQ | shuyuej | 2024-06-28T15:49:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-06-28T13:53:38Z | ---
license: apache-2.0
---
|
jointriple/brand_classification_2_20240628_model_1 | jointriple | 2024-06-28T14:04:08Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:eu"
]
| null | 2024-06-28T13:55:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
fifala/16-fifa-06-28-02 | fifala | 2024-06-28T13:58:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-28T13:55:51Z | Entry not found |
sileod/deberta-base-long-tasksource | sileod | 2024-07-02T14:53:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"zero-shot-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| zero-shot-classification | 2024-06-28T13:56:40Z | ---
library_name: transformers
pipeline_tag: zero-shot-classification
---
# Model Card for Model ID
Continuous fine-tuning of deberta-tasksource, fine-tuned on newer tasksource and with context length of size 1024.
Upcoming: longer training + 1280 tokens context length. |
kryosaur/test-ai | kryosaur | 2024-06-28T13:57:12Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T13:57:12Z | Entry not found |
jgaertner/bert-finetuned-ner4invoice7 | jgaertner | 2024-06-28T13:58:32Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T13:58:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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] |
PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-1bit-smashed | PrunaAI | 2024-06-28T14:02:52Z | 0 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:victunes/TherapyLlama-8B-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-06-28T14:01:19Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-1bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-1bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
Grayx/john_paul_van_damme_50 | Grayx | 2024-06-28T14:01:58Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:01:45Z | Entry not found |
PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-2bit-smashed | PrunaAI | 2024-06-28T14:06:16Z | 0 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:victunes/TherapyLlama-8B-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-06-28T14:04:19Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-2bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-2bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
healtori/17-heal-06-28-02 | healtori | 2024-06-28T14:07:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-28T14:05:07Z | Entry not found |
jgaertner/bert-finetuned-ner4invoice8 | jgaertner | 2024-06-28T14:06:09Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:06:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
oliverchau/futon | oliverchau | 2024-06-28T14:08:10Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:07:52Z | Entry not found |
PrunaAI/victunes-TherapyLlama-8B-v1-QUANTO-int2bit-smashed | PrunaAI | 2024-07-01T08:00:20Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:victunes/TherapyLlama-8B-v1",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:10:31Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- 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 quanto.
- ***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 HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyLlama-8B-v1-QUANTO-int2bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
Pranja/gemma-2b-unsloth | Pranja | 2024-06-28T14:11:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:11:10Z | ---
base_model: unsloth/gemma-2b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
---
# Uploaded model
- **Developed by:** Pranja
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma 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)
|
fifala/20-fifa-06-28-02 | fifala | 2024-06-28T14:14:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2024-06-28T14:11:51Z | Entry not found |
huhuhuhus/Qwen-Qwen1.5-0.5B-1719583954 | huhuhuhus | 2024-06-28T14:12:35Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:12:35Z | Entry not found |
PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-4bit-smashed | PrunaAI | 2024-06-28T14:15:30Z | 0 | 0 | transformers | [
"transformers",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:victunes/TherapyLlama-8B-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| text-generation | 2024-06-28T14:12:41Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/CP4VSgck)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-4bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/victunes-TherapyLlama-8B-v1-HQQ-4bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
PrunaAI/victunes-TherapyLlama-8B-v1-QUANTO-int4bit-smashed | PrunaAI | 2024-07-01T08:00:00Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:victunes/TherapyLlama-8B-v1",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:12:46Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- 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 quanto.
- ***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 HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyLlama-8B-v1-QUANTO-int4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
PrunaAI/victunes-TherapyLlama-8B-v1-AWQ-4bit-smashed | PrunaAI | 2024-06-28T14:17:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"pruna-ai",
"conversational",
"base_model:victunes/TherapyLlama-8B-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
]
| text-generation | 2024-06-28T14:14:20Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- 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 HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 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/victunes-TherapyLlama-8B-v1-AWQ-4bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
SidXXD/pg_encoder_0078-caat_0500 | SidXXD | 2024-06-28T14:15:35Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:15:35Z | Entry not found |
PrunaAI/victunes-TherapyLlama-8B-v1-QUANTO-int8bit-smashed | PrunaAI | 2024-07-01T08:00:51Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:victunes/TherapyLlama-8B-v1",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:15:43Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- 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 quanto.
- ***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 HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyLlama-8B-v1-QUANTO-int8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
PrunaAI/victunes-TherapyLlama-8B-v1-QUANTO-float8bit-smashed | PrunaAI | 2024-07-01T07:59:53Z | 0 | 0 | transformers | [
"transformers",
"pruna-ai",
"base_model:victunes/TherapyLlama-8B-v1",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:15:54Z | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: victunes/TherapyLlama-8B-v1
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- 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 quanto.
- ***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 HARDWARE_NAME 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 victunes/TherapyLlama-8B-v1 installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install quanto
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
IMPORTS
model = AutoModelForCausalLM.from_pretrained("PrunaAI/victunes-TherapyLlama-8B-v1-QUANTO-float8bit-smashed", trust_remote_code=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained("victunes/TherapyLlama-8B-v1")
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 victunes/TherapyLlama-8B-v1 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). |
SidXXD/pg_encoder_0157-caat_0500 | SidXXD | 2024-06-28T14:16:00Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:16:00Z | Entry not found |
SidXXD/pg_encoder_0310-caat_0500 | SidXXD | 2024-06-28T14:16:28Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:16:28Z | Entry not found |
SidXXD/pg_encoder_0780-caat_0500 | SidXXD | 2024-06-28T14:17:20Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:17:20Z | Entry not found |
SidXXD/pg_encoder_1000-caat_0500 | SidXXD | 2024-06-28T14:18:02Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:18:02Z | Entry not found |
habulaj/181092277046 | habulaj | 2024-06-28T14:19:17Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:18:59Z | Entry not found |
jgaertner/bert-finetuned-ner4invoice9 | jgaertner | 2024-06-28T14:19:11Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:19:10Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
patruff/chucklesFimbFineTuneD | patruff | 2024-06-28T14:22:11Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:22:11Z | Entry not found |
jeslev/mpnet-with-hierarchy-properties | jeslev | 2024-06-28T14:22:55Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:22:55Z | Entry not found |
bekirbakar/whisper-small-tr | bekirbakar | 2024-06-28T14:23:01Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:23:01Z | Entry not found |
Grayx/john_paul_van_damme_51 | Grayx | 2024-06-28T14:23:27Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:23:16Z | Entry not found |
mttgermano/LunarLander-v2 | mttgermano | 2024-06-28T14:24:27Z | 0 | 0 | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
]
| reinforcement-learning | 2024-06-28T14:24:23Z | ---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -148.43 +/- 124.27
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'mttgermano/LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Hamed7immortal/shekan | Hamed7immortal | 2024-06-28T14:24:27Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T14:24:27Z | ---
license: openrail
---
|
Grayx/john_paul_van_damme_52 | Grayx | 2024-06-28T14:24:59Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:24:51Z | Entry not found |
dlantonia/instruct-pix2pix-model | dlantonia | 2024-06-30T17:43:01Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
]
| null | 2024-06-28T14:26:43Z | Entry not found |
mjjj7/MichaelJacksonThrillerNewModel | mjjj7 | 2024-06-28T14:30:24Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T14:28:35Z | ---
license: openrail
---
|
Aditya062003/question_generator | Aditya062003 | 2024-06-28T14:40:31Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| text2text-generation | 2024-06-28T14:29:05Z | Entry not found |
beratcmn/qwen2-0.5B-turkish-sentiment-analysis | beratcmn | 2024-06-28T15:33:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"tr",
"base_model:unsloth/Qwen2-0.5B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:29:27Z | ---
base_model: unsloth/Qwen2-0.5B
language:
- tr
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
library_name: transformers
---
# Uploaded model
- **Developed by:** beratcmn
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2-0.5B
This qwen2 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) |
500Bears/Nando | 500Bears | 2024-06-28T14:32:02Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T14:32:02Z | ---
license: apache-2.0
---
|
Beksultanbek/Love | Beksultanbek | 2024-06-28T14:33:11Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-28T14:33:11Z | ---
license: mit
---
|
hari02/florence_finetuned | hari02 | 2024-07-01T14:07:20Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T14:36:24Z | ---
license: apache-2.0
---
|
habulaj/5814044193 | habulaj | 2024-06-28T14:37:08Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:37:05Z | Entry not found |
rashid996958/pix2pix_exp37 | rashid996958 | 2024-06-28T14:37:53Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:37:48Z | Entry not found |
rubio21/modelo_bricks_dreambooth | rubio21 | 2024-06-28T14:40:45Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:40:45Z | Entry not found |
TitanRTX/YOLO_DETECTION | TitanRTX | 2024-06-28T14:40:50Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-28T14:40:50Z | ---
license: mit
---
|
Patricksa/EEEEE | Patricksa | 2024-06-28T14:41:39Z | 0 | 0 | null | [
"license:unknown",
"region:us"
]
| null | 2024-06-28T14:41:39Z | ---
license: unknown
---
|
clisther/PonyModelMerges | clisther | 2024-06-28T14:46:39Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:42:56Z | Entry not found |
leva4656/shatunov94 | leva4656 | 2024-06-28T14:44:12Z | 0 | 0 | null | [
"license:openrail",
"region:us"
]
| null | 2024-06-28T14:44:12Z | ---
license: openrail
---
|
GraceZhao/DynamiCrafter-CIL-512-no-watermark | GraceZhao | 2024-06-29T07:17:02Z | 0 | 1 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T14:44:53Z | ---
license: apache-2.0
---
|
OllmOne/gemma-2-9b-it-GGUF | OllmOne | 2024-06-28T14:47:43Z | 0 | 0 | null | [
"license:gemma",
"region:us"
]
| null | 2024-06-28T14:47:43Z | ---
license: gemma
---
|
Grayx/john_paul_van_damme_53 | Grayx | 2024-06-28T14:49:07Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:48:50Z | Entry not found |
rubio21/modelo_bricks_dreambooth80 | rubio21 | 2024-06-28T14:49:11Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T14:49:11Z | Entry not found |
PleIAs/Estienne | PleIAs | 2024-07-02T22:49:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2024-06-28T14:51:48Z | **Estienne** is a text-segmentation model trained on Deberta.
In contrast with most text-segmentation approach, Estienne is based on token classification. Editorial structure are identified similarly to named-entity recognition.
Estienne was trained on 2,000 example of manually annotated texts, excerpted at random from three very large dataset collected by Pleias: Common Corpus (cultural heritage texts in the public domain), Marianne-OpenData (French/English administrative documents) and OpenScientificPile (scientific publications in free licenses, indexed on OpenAlex).
Given the diversity of the corpus, Estienne should work out on diverse document formats in European languages.
The model is named in reference to the humanist Henri Estienne who introduced many practices of text segmentation still in use in scholarly edition today.
## Use
As Deberta remove newline by default and has no support for it in the tokenizer, they should be replaced by pilcrows (¶).
Estienne supports the following segmentations:
* **Text**
* **Separator** - actually a segmentation separator. They are generally based on newline (actually ¶) with some variations due to text segmentation understanding.
* **Title**
* **Table**
* **Dialog** - any kind of speaker attributed intervention.
* **Bibliography** - statement of a specific bibliographic reference, either in a bibliography section or a footnote.
* **Contact** - personal information, can be especially useful in the context of PII removal.
* **Paratext** - any non-meaningful text included in standard documents like header, page numbering, section recall, etc.
* **Author** - author names and signatures.
* **Date** - statement of date and time, common in letters and newspaper articles.
* **Keyword** - list of keywords, especially common in scientific publications.
## Example
|
etownsupport/sparse_custom_handler | etownsupport | 2024-06-28T16:01:10Z | 0 | 0 | null | [
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T14:59:33Z | ---
license: mit
---
|
Samreaver/Topographical_Features_Extraction | Samreaver | 2024-06-28T15:02:58Z | 0 | 0 | null | [
"license:mit",
"region:us"
]
| null | 2024-06-28T14:59:59Z | ---
license: mit
---
|
SicariusSicariiStuff/Blog_And_Updates | SicariusSicariiStuff | 2024-07-02T22:47:59Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-06-28T15:01:03Z | ---
license: apache-2.0
---
# July 3rd, 2024
Regarding **LLAMA-3_8B_Unaligned**: I'm happy to report that significant progress was made, more details in the [LLAMA-3_8B_Unaligned](https://huggingface.co/SicariusSicariiStuff/LLAMA-3_8B_Unaligned) ReadMe.
# July 2nd, 2024
Regarding **LLAMA-3_8B_Unaligned**- TL;DR The bad news: the training faild, model is schizo and unusable.
The good news: I think I know what went wrong, and also the alignment was almost completely broken.
Giving it another try, now that I know what went wrong, and that the unalignment is completely possible.
# July 1st, 2024, update 3
Oh, no support for EXL2 for QWEN2 with vision... Back to training! I hope to see more support for multi modaliti, and it will be especially cool too see something like Axolotl with support for multi modal training!
# July 1st, 2024, update 2
**Largest, most capable, UNCENSORED vision model to date released!**
CognitiveComputations has just released Dolphin-Vision-72b! This is very exciting, as there are many possibilities with this new model. The first thought that came to my mind is that it enables a single person to build an automatic pipeline to pretrain a stable diffusion model from scratch, including an uncensored version. I will be pausing my current model fine-tuning to quantize and upload Dolphin-Vision-72b in EXL2 quantization. Congratulations to the Dolphin team!
# July 1st, 2024
**LLAMA-3_8B_Unaligned**
Average Loss: 0.8.
The model might be released sooner than expected!
**ShareGPT JSON Editor**
Solved most of the issues with dynamic syntax highlighting, looking good! Token counting looks good too!
<div align="center">
<img src="https://i.imgur.com/S4d4bK0.png" alt="ShareGPT JSON Editor" style="width: 80%; min-width: 700px; display: block; margin: auto;">
</div>
# June 30, 2024, 2nd update
**The JSON editor was made and is usable!**
https://github.com/SicariusSicariiStuff/ShareGPT_Editor
I hope this helps our community, and that people will help to make it better, python is not my thing 🙃
Features so far:
-Markdown highlight (100% Customizable)
-Token counter (100% Customizable)
-Reads and writes ShareGPT JSON
-Minimal dependecies, ultra flexible through the use of YAML files
# June 30, 2024
**Making a JSON editor**
I know this might sound trivial and redundant, but I want to create a simple JSON editor for ShareGPT. Sometimes, we just want to add a personal touch to our data, you know?
I'm genuinely surprised there isn't a common tool like this already. I mean, I'm absolutely certain people have made similar tools for their own use. So, why not share it with the community?
My Python skills are pretty basic, but I can definitely prompt my way through this and build such a tool in a day or two. I'll also be kind enough to upload it to GitHub so it can benefit others. Let's save some effort and avoid reinventing the wheel each time, shall we?
# June 29, 2024
**LLAMA-3 Unaligned update**
I began a full fine-tuning of LLAMA-3 8B using a relatively small 40MB dataset for unalignment. My hardware is just sufficient, and I am using ZERO3 full offload.
This experiment aims to observe how the model's behavior changes with this dataset. Typically, I perform deep QLoRA training for unalignment (e.g., LoRA R 128 and similar settings). I chose not to include the massive RP dataset for this LLAMA-3 fine-tune partly because it doesn't exist yet. While I could use LimaRP and other publicly available datasets, that would defeat the purpose of creating something new and unique. I have started planning the creation of such a dataset, which requires considerable time and effort. However, once it's developed, it could be used for training all future models, making it a worthwhile investment. If you'd like to help, you can send me a dataset in TXT or JSON format (JSON preferred). Once this full fine-tuning experiment concludes, which should take about two weeks due to the slow RAM offload, I'll have a clearer idea of how to proceed. With 2x A6000 GPUs, it would likely be five times faster.
**Additional projects**
I am considering training a few LLMs to help me create pipelines for data generation and curation. NVIDIA's 340B Nemotron is impressive, but it's too large for most users. I'm contemplating training a 4B Mistral model for this purpose, which should be quick for both training and inference. However, 4B is a reduced version of 7B, so the question is whether it will be coherent and intelligent enough for the task. If not, I could train a 7B Mistral. There’s so much training to do and limited compute resources (and VRAM).
**This blog**
It's unconventional but fun, which is why I enjoy it.
**End notes**
The summer heat is intense! I'm far more productive in winter. I love snow and nature. Air conditioners are a necessary evil. I haven't shot a bow for two months, and after an hour of surfing, I'm exhausted. I need to get back in shape.
# What is this model?
It's not a **model** 🙃
I will be posting here some updates, ideas and document stuff. I guess we can call this some sort of a blog.
This is the first entry.
**June 28, 2024**. |
Grayx/john_paul_van_damme_54 | Grayx | 2024-06-28T15:04:01Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T15:03:48Z | Entry not found |
AdamKasumovic/llama3-8b-instruct-bactrian-x-en-100-percent-low-med-high-perplexity | AdamKasumovic | 2024-06-28T15:04:09Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-06-28T15:04:09Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** AdamKasumovic
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Zumak/Zuminha | Zumak | 2024-06-28T15:04:35Z | 0 | 0 | null | [
"region:us"
]
| null | 2024-06-28T15:04:29Z | Entry not found |
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