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QuantFactory/Mistral-7B-Instruct-v0.3-GGUF | QuantFactory | 2024-05-23T07:03:15Z | 198 | 3 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:quantized:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-05-23T04:58:44Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: transformers
pipeline_tag: text-generation
tags:
- mistral
---
# Mistral-7B-Instruct-v0.3-GGUF
- This is quantized version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) created using llama.cpp
# Model Description
The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
Mistral-7B-v0.3 has the following changes compared to [Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/edit/main/README.md)
- Extended vocabulary to 32768
- Supports v3 Tokenizer
- Supports function calling
### Chat
After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
```
mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256
```
### Instruct following
```py
from mistral_inference.model import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
```
### Function calling
```py
from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.model import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(
tools=[
Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
)
)
],
messages=[
UserMessage(content="What's the weather like today in Paris?"),
],
)
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
```
## Generate with `transformers`
If you want to use Hugging Face `transformers` to generate text, you can do something like this.
```py
from transformers import pipeline
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3")
chatbot(messages)
```
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall |
fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-994884 | fine-tuned | 2024-05-23T07:00:47Z | 6 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"Science",
"Research",
"Verification",
"Dataset",
"AI",
"custom_code",
"en",
"dataset:fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-994884",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-05-23T07:00:34Z | ---
license: apache-2.0
datasets:
- fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-994884
- allenai/c4
language:
- en
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- Science
- Research
- Verification
- Dataset
- AI
---
This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-en**](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case:
scientific claim verification search
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/SciFact-256-24-gpt-4o-2024-05-13-994884',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
fine-tuned/SCIDOCS-256-24-gpt-4o-2024-05-13-417900 | fine-tuned | 2024-05-23T07:00:36Z | 8 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"Translation",
"Editing",
"French",
"Scientific",
"Medical",
"custom_code",
"en",
"dataset:fine-tuned/SCIDOCS-256-24-gpt-4o-2024-05-13-417900",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-05-23T07:00:15Z | ---
license: apache-2.0
datasets:
- fine-tuned/SCIDOCS-256-24-gpt-4o-2024-05-13-417900
- allenai/c4
language:
- en
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- Translation
- Editing
- French
- Scientific
- Medical
---
This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-en**](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case:
service search for translation and editing
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/SCIDOCS-256-24-gpt-4o-2024-05-13-417900',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-497939 | fine-tuned | 2024-05-23T07:00:24Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"mteb",
"Finance",
"Sentiment",
"NLP",
"Analysis",
"QA",
"custom_code",
"en",
"dataset:fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-497939",
"dataset:allenai/c4",
"license:apache-2.0",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-05-23T07:00:10Z | ---
license: apache-2.0
datasets:
- fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-497939
- allenai/c4
language:
- en
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- Finance
- Sentiment
- NLP
- Analysis
- QA
---
This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-en**](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) designed for the following use case:
financial sentiment and QA analysis
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim
model = SentenceTransformer(
'fine-tuned/FiQA2018-256-24-gpt-4o-2024-05-13-497939',
trust_remote_code=True
)
embeddings = model.encode([
'first text to embed',
'second text to embed'
])
print(cos_sim(embeddings[0], embeddings[1]))
```
|
dlby/testModel34 | dlby | 2024-05-23T06:58:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T06:58:01Z | ---
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] |
duyntnet/Delexa-Instruct-V0.1-7b-imatrix-GGUF | duyntnet | 2024-05-23T06:57:14Z | 18 | 0 | transformers | [
"transformers",
"gguf",
"imatrix",
"Delexa-Instruct-V0.1-7b",
"text-generation",
"en",
"license:other",
"region:us",
"conversational"
] | text-generation | 2024-05-23T05:04:55Z | ---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- Delexa-Instruct-V0.1-7b
---
Quantizations of https://huggingface.co/lex-hue/Delexa-Instruct-V0.1-7b
# From original readme
## Delexa-V0.1-Instruct-7b: Our Newest and Best Model Yet!
We are excited to announce the release of Delexa-V0.1-Instruct-7b, our newest and best model yet! Delexa-V0.1-Instruct-7b has shown excellent performance on a variety of tasks, and we are confident that it will be a valuable asset to the research community.
### Eval Results
Delexa-V0.1-Instruct-7b was evaluated on a dataset of question-answer pairs. The model was given a single question and three different answer choices, and it was tasked with selecting the best answer. Delexa-V0.1-Instruct-7b achieved an average score of 8.27 on this task.
Here is a table showing the detailed eval results:
| Model | Turn 1 | Turn 2 | Average |
|---|---|---|---|
| gpt-4 | 8.95625 | 9.0250 | 8.990625 |
| Delexa-V0.1-Instruct-7b | 8.57500 | 7.9500 | 8.268750 |
| claude-v1 | 8.15000 | 7.6500 | 7.900000 |
| gpt-3.5-turbo | 8.07500 | 7.8125 | 7.943750 |
| vicuna-13b-v1.3 | 6.81250 | 5.9625 | 6.387500 |
| palm-2-chat-bison-001 | 6.71250 | 6.0875 | 6.400000 | |
gigio-br/output-prompt4 | gigio-br | 2024-05-23T06:56:52Z | 1 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-05-23T06:43:00Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- diffusers
- lora
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
base_model: runwayml/stable-diffusion-v1-5
inference: true
instance_prompt: 2D floor plan without colors, with 4 bedrooms
---
<!-- 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. -->
# LoRA DreamBooth - gigio-br/output-prompt4
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on 2D floor plan without colors, with 4 bedrooms using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
## 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] |
bandi2716/albert-base-v2-finetuned-emotion | bandi2716 | 2024-05-23T06:56:24Z | 108 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:albert/albert-base-v2",
"base_model:finetune:albert/albert-base-v2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-23T06:48:27Z | ---
license: apache-2.0
base_model: albert/albert-base-v2
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: albert-base-v2-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.912
- name: F1
type: f1
value: 0.911766000939379
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-emotion
This model is a fine-tuned version of [albert/albert-base-v2](https://huggingface.co/albert/albert-base-v2) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2451
- Accuracy: 0.912
- F1: 0.9118
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9011 | 1.0 | 250 | 0.4077 | 0.877 | 0.8776 |
| 0.2633 | 2.0 | 500 | 0.2451 | 0.912 | 0.9118 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
hgnoi/FpMpUr5ig3SByv8a | hgnoi | 2024-05-23T06:55:26Z | 131 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T06:53:48Z | ---
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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
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vubacktracking/roberta-base-finetuned-squadv2 | vubacktracking | 2024-05-23T06:49:44Z | 134 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-05-23T04:49:35Z | ---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
model-index:
- name: roberta-base-finetuned-squadv2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-squadv2
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on SQuAD-v2 dataset.
## Model description
Training Loss: 0.925
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Llimy1/llama2-micro-test-2 | Llimy1 | 2024-05-23T06:49:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T06:26:09Z | ---
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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **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]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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**BibTeX:**
[More Information Needed]
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[More Information Needed]
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navya608/random-forest-fraud-detection | navya608 | 2024-05-23T06:48:23Z | 2 | 1 | transformers | [
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T06:29:45Z | # Random Forest Fraud Detection Model
This is a Random Forest model trained to detect fraudulent transactions.
## Usage
```python
from transformers import pipeline
classifier = pipeline("fraud-detection", model="your-username/random-forest-fraud-detection")
data = {
"CustomerAge": 45,
"POSWEBLimit": 2000,
"CreditLimit": 5000,
"Amount": 1500,
"AverageIncomeExpendicture": 3000,
"BalanceChange": -500,
"AmountToCreditLimitRatio": 0.3,
"IsMale": 1,
"Marital Status": "Single",
"Cards": "Visa",
"CardColour": "Gold",
"CardType": "Credit",
"TransactionType": "Debit",
"Domain": "Retail",
"ATM": "No"
}
result = classifier(data)
print(result)
|
aangry-mouse/whisper-base-ml-ru-2 | aangry-mouse | 2024-05-23T06:38:52Z | 86 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ru",
"dataset:aangry-mouse/stepik_ml_ru_2",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-22T22:46:48Z | ---
language:
- ru
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- aangry-mouse/stepik_ml_ru_2
metrics:
- wer
model-index:
- name: Whisper Base Ml Ru
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: "ML \u0434\u0430\u0442\u0430\u0441\u0435\u0442"
type: aangry-mouse/stepik_ml_ru_2
args: 'config: ru, split: test'
metrics:
- name: Wer
type: wer
value: 33.821550154382074
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Base Ml Ru
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the ML датасет dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4592
- Wer: 33.8216
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.6497 | 0.6649 | 250 | 0.6474 | 40.6539 |
| 0.4388 | 1.3298 | 500 | 0.5218 | 37.7009 |
| 0.4485 | 1.9947 | 750 | 0.4651 | 37.5030 |
| 0.296 | 2.6596 | 1000 | 0.4592 | 33.8216 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.0.1+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
|
BecarIA/Longformer-SQuAD-becas-3 | BecarIA | 2024-05-23T06:36:30Z | 123 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-05-23T06:18:18Z | ---
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]
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### Downstream Use [optional]
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[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]
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<!-- 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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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hgnoi/olyNg6igQRxBQ7u1 | hgnoi | 2024-05-23T06:32:16Z | 131 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T06:30:46Z | ---
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
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[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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### 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] |
BothBosu/bert-scam-classifier-v1.3 | BothBosu | 2024-05-23T06:29:17Z | 109 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-23T06:27:50Z | ---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-scam-classifier-v1.3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-scam-classifier-v1.3
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:------------------:|:---------------:|:-----------:|
| No log | 1.0 | 160 | 0.0031 | {'accuracy': 1.0} | {'precision': 1.0} | {'recall': 1.0} | {'f1': 1.0} |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
zgbkdlm/fbs | zgbkdlm | 2024-05-23T06:27:08Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-22T12:21:51Z | ---
license: apache-2.0
---
# Checkpoints
This repository contains the trained models that we use in "Conditioning diffusions via explicit forward-backward bridging", see, https://github.com/zgbkdlm/fbs.
Specifically,
- `celeba-64_lin_2999.npz` is the checkpoint that we use for CelebA-HQ (64 x 64) inpainting and super-resolution.
- `mnist_lin_2999.npz` is the checkpoint that we use for MNIST (32 x 32) inpainting and super-resolution.
- `sb_mnist_lin_19.npz` is the checkpoint that we use for Shroödinger bridge MNIST (32 x 32) inpainting and super-resolution.
# Datasets
The MNIST dataset `mnist.npz` is licensed under Creative Commons Attribution-Share Alike 3.0 license (see, https://keras.io/api/datasets/mnist/).
|
hjskhan/llama-3-math-finetuned-10 | hjskhan | 2024-05-23T06:26:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T06:26:26Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** hjskhan
- **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)
|
SantiVillaRam7/haiku-generator | SantiVillaRam7 | 2024-05-23T06:26:02Z | 148 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T05:40:56Z | ---
license: apache-2.0
---
|
cadzchua/bert-base-qa-model-7up | cadzchua | 2024-05-23T06:26:00Z | 24 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-05-23T05:45:10Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-qa-model-7up
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-qa-model-7up
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1327
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7244 | 1.0 | 9450 | 0.7114 |
| 0.2855 | 2.0 | 18900 | 0.2326 |
| 0.1248 | 3.0 | 28350 | 0.1327 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
colesimmons/sux-transliteration | colesimmons | 2024-05-23T06:25:20Z | 161 | 0 | transformers | [
"transformers",
"safetensors",
"encoder-decoder",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-20T19:34:10Z | ---
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. -->
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<!-- 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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Examination [optional]
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[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]
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[More Information Needed]
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[More Information Needed]
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CMU-AIR2/math-phi-1-5-FULL-Arithmetic-10k | CMU-AIR2 | 2024-05-23T06:21:34Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T01:32:20Z | ---
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
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- 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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
**APA:**
[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
CMU-AIR2/math-phi-1-5-FULL-Arithmetic-8k | CMU-AIR2 | 2024-05-23T06:18:23Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T01:29:20Z | ---
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
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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eeeyounglee/EEVE-10.8B-Dense-Finetune-1 | eeeyounglee | 2024-05-23T06:12:47Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"llama",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-23T06:10:18Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# eeeyounglee/EEVE-10.8B-Dense-Finetune-1
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('eeeyounglee/EEVE-10.8B-Dense-Finetune-1')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=eeeyounglee/EEVE-10.8B-Dense-Finetune-1)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 900 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 1000,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 90,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: LlamaModel
(1): Pooling({'word_embedding_dimension': 4096, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Dense({'in_features': 4096, 'out_features': 256, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
MainakMaitra/mistral_7b_instruct_finetuned_multi_intent | MainakMaitra | 2024-05-23T06:09:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T06:09:39Z | ---
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]
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<!-- Provide the basic links for the model. -->
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- **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
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[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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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|
przybytniowska/roberta_base_QA_SQUAD_adafactor | przybytniowska | 2024-05-23T05:59:59Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:arrow",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-22T20:11:56Z | ---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
datasets:
- arrow
model-index:
- name: roberta_base_QA_SQUAD_adafactor
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_base_QA_SQUAD_adafactor
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the arrow dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.0
- Tokenizers 0.14.1
|
huudan12345/distilGPT2_en_xsum | huudan12345 | 2024-05-23T05:59:25Z | 145 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T05:59:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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[More Information Needed]
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<!-- 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
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- 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. -->
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[More Information Needed]
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[More Information Needed]
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<!-- 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]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- 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]
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## Model Card Contact
[More Information Needed] |
Keerthanah2002/rmj | Keerthanah2002 | 2024-05-23T05:58:17Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-23T05:51:30Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### rmj Dreambooth model trained by Keerthanah2002 with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
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|
ULRs/llama-3-8b-topic-classification-ur | ULRs | 2024-05-23T05:51:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | 2024-05-23T05:51:08Z | ---
library_name: peft
base_model: meta-llama/Meta-Llama-3-8B-Instruct
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- 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]
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#### Preprocessing [optional]
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[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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]
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- **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]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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### Framework versions
- PEFT 0.11.1 |
germanchura/modelo_entrenado_01 | germanchura | 2024-05-23T05:34:18Z | 24 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-05-23T03:12:58Z | ---
license: apache-2.0
base_model: distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: modelo_entrenado_01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# modelo_entrenado_01
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4536
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 267 | 0.6835 |
| 0.9199 | 2.0 | 534 | 0.6370 |
| 0.9199 | 3.0 | 801 | 0.5897 |
| 0.6357 | 4.0 | 1068 | 0.5777 |
| 0.6357 | 5.0 | 1335 | 0.5880 |
| 0.5711 | 6.0 | 1602 | 0.5634 |
| 0.5711 | 7.0 | 1869 | 0.5716 |
| 0.5481 | 8.0 | 2136 | 0.5407 |
| 0.5481 | 9.0 | 2403 | 0.5352 |
| 0.5204 | 10.0 | 2670 | 0.5153 |
| 0.5204 | 11.0 | 2937 | 0.5037 |
| 0.492 | 12.0 | 3204 | 0.4821 |
| 0.492 | 13.0 | 3471 | 0.4890 |
| 0.4854 | 14.0 | 3738 | 0.4826 |
| 0.48 | 15.0 | 4005 | 0.4718 |
| 0.48 | 16.0 | 4272 | 0.4758 |
| 0.464 | 17.0 | 4539 | 0.4655 |
| 0.464 | 18.0 | 4806 | 0.4870 |
| 0.4575 | 19.0 | 5073 | 0.4544 |
| 0.4575 | 20.0 | 5340 | 0.4559 |
| 0.4484 | 21.0 | 5607 | 0.5187 |
| 0.4484 | 22.0 | 5874 | 0.4987 |
| 0.4414 | 23.0 | 6141 | 0.4673 |
| 0.4414 | 24.0 | 6408 | 0.4795 |
| 0.4323 | 25.0 | 6675 | 0.4692 |
| 0.4323 | 26.0 | 6942 | 0.4749 |
| 0.4333 | 27.0 | 7209 | 0.4828 |
| 0.4333 | 28.0 | 7476 | 0.4351 |
| 0.4313 | 29.0 | 7743 | 0.4405 |
| 0.4292 | 30.0 | 8010 | 0.4614 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
juliuserictuliao/w2v-bert-2.0-tagalog-colab-CV16-4 | juliuserictuliao | 2024-05-23T05:31:16Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T05:31:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- 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 -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed] |
Pragadishwaran/sppech | Pragadishwaran | 2024-05-23T05:27:43Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T05:25:59Z | ---
license: apache-2.0
---
|
hgnoi/mSzMfGyRJQO8hBYC | hgnoi | 2024-05-23T05:23:25Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T05:21:48Z | ---
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]
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<!-- Provide the basic links for the model. -->
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### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
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<!-- 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
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[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]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed] |
SrikanthChellappa/Mistral-7B-Instruct-v0.3-AWQ-4Bit | SrikanthChellappa | 2024-05-23T05:23:14Z | 78 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | 2024-05-23T05:18:57Z | ---
license: apache-2.0
---
|
nitus-ac/nMer3b | nitus-ac | 2024-05-23T05:19:04Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Undi95/Llama-3-LewdPlay-8B",
"base_model:merge:Undi95/Llama-3-LewdPlay-8B",
"base_model:ajibawa-2023/General-Stories-Mistral-7B",
"base_model:merge:ajibawa-2023/General-Stories-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T05:05:03Z | ---
base_model:
- ajibawa-2023/General-Stories-Mistral-7B
- Undi95/Llama-3-LewdPlay-8B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [ajibawa-2023/General-Stories-Mistral-7B](https://huggingface.co/ajibawa-2023/General-Stories-Mistral-7B)
* [Undi95/Llama-3-LewdPlay-8B](https://huggingface.co/Undi95/Llama-3-LewdPlay-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: ajibawa-2023/General-Stories-Mistral-7B
layer_range: [0, 32]
- model: Undi95/Llama-3-LewdPlay-8B
layer_range: [0, 32]
merge_method: slerp
base_model: ajibawa-2023/General-Stories-Mistral-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
dendimaki/Mistral-7B-v0.1-push-demo | dendimaki | 2024-05-23T05:18:32Z | 75 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-23T04:55:26Z | ---
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. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### 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] |
baek26/all_4517_bart-all_rl | baek26 | 2024-05-23T05:06:03Z | 50 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | reinforcement-learning | 2024-05-23T05:05:20Z | ---
license: apache-2.0
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="baek26//tmp/tmpj_lma3ny/baek26/all_4517_bart-all_rl")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("baek26//tmp/tmpj_lma3ny/baek26/all_4517_bart-all_rl")
model = AutoModelForCausalLMWithValueHead.from_pretrained("baek26//tmp/tmpj_lma3ny/baek26/all_4517_bart-all_rl")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
KimByeongSu/gpt-neo-2.7B-cs-finetuning-filtered-7 | KimByeongSu | 2024-05-23T05:04:21Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"generated_from_trainer",
"base_model:EleutherAI/gpt-neo-2.7B",
"base_model:finetune:EleutherAI/gpt-neo-2.7B",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-22T06:34:13Z | ---
license: mit
tags:
- generated_from_trainer
base_model: EleutherAI/gpt-neo-2.7B
model-index:
- name: gpt-neo-2.7B-cs-finetuning-filtered-7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt-neo-2.7B-cs-finetuning-filtered-7
This model is a fine-tuned version of [EleutherAI/gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5821 | 1.0 | 948 | 2.3921 |
| 1.8935 | 2.0 | 1896 | 2.3477 |
| 1.4596 | 3.0 | 2844 | 2.4260 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1
- Datasets 2.12.0
- Tokenizers 0.15.1
|
zenai-org/lora-collections | zenai-org | 2024-05-23T04:47:54Z | 0 | 0 | null | [
"region:us"
] | null | 2024-05-23T04:45:57Z | ## Lora Collections
### MidJourney V6 - Portrait
**V1 - @toilaluan**
Write your description here |
zzunyang/law_dpo2 | zzunyang | 2024-05-23T04:47:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/open-llama-2-ko-7b",
"base_model:adapter:beomi/open-llama-2-ko-7b",
"region:us"
] | null | 2024-05-23T04:33:31Z | ---
library_name: peft
base_model: beomi/open-llama-2-ko-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
Marupudi/dharaneesh-tiiuae-falcon-7b-v2 | Marupudi | 2024-05-23T04:45:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T04:44:35Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
acsankar/InsuranceGPT | acsankar | 2024-05-23T04:43:54Z | 75 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-23T02:01:29Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# 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
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<!-- 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
Sorrowair/metal_35M_long_adap | Sorrowair | 2024-05-23T04:42:29Z | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:westlake-repl/SaProt_35M_AF2",
"base_model:adapter:westlake-repl/SaProt_35M_AF2",
"region:us"
] | null | 2024-05-23T04:42:26Z | ---
library_name: peft
base_model: westlake-repl/SaProt_35M_AF2
---
# Model Card for Model ID
This model is used for a demo task<br><br> The digital label means: <br>0: <br>
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.11.1 |
xchgeaxeax/WhiteRabbitNeo-7B-v1.5a-GGUF | xchgeaxeax | 2024-05-23T04:40:13Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2024-05-23T04:40:13Z | ---
license: other
license_name: deepseek
license_link: https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/raw/main/LICENSE
---
|
votepurchase/Yaki-Dofu-Mix | votepurchase | 2024-05-23T04:39:54Z | 230 | 3 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"ja",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-23T04:39:54Z | ---
license: creativeml-openrail-m
language:
- ja
- en
tags:
- stable-diffusion
- text-to-image
---
# Yaki-Dofu-Mix
<img src="https://huggingface.co/Vsukiyaki/Yaki-Dofu-Mix/resolve/main/imgs/Yaki-Dofu-Mix.png" style="width: 768px;">
## 概要 / Overview
- **Yaki-Dofu-Mix**は、アニメ風の画風に特化したマージモデルです。 / **Yaki-Dofu-Mix** is a merge model that specializes in an anime-like painting style.
- VAEなしでも鮮やかな色合いで出力されます。 / The output will be vividly tinted without VAE.
<hr>
## ライセンス / License
<div class="px-2">
<table class="table-fixed border mt-0 text-xs">
<tbody>
<tr>
<td class="px-4 text-base text-bold" colspan="2">
<a href="https://huggingface.co/spaces/CompVis/stable-diffusion-license">
修正 CreativeML OpenRAIL-M ライセンス / Modified CreativeML OpenRAIL-M license
</a>
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span style="font-size: 18px;">
✅
</span>
</td>
<td>
このモデルのクレジットを入れずに使用する<br>
Use the model without crediting the creator
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span style="font-size: 18px;">
🚫
</span>
</td>
<td>
このモデルで生成した画像を商用利用する<br>
Sell images they generate
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span style="font-size: 18px;">
🚫
</span>
</td>
<td>
このモデルを商用の画像生成サービスで利用する</br>
Run on services that generate images for money
</td>
</tr>
<tr>
<td class="align-middle px-2 w-8">
<span style="font-size: 18px;">
✅
</span>
</td>
<td>
このモデルを使用したマージモデルを共有する<br>
Share merges using this model
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span style="font-size: 18px;">
🚫
</span>
</td>
<td>
このモデル、またはこのモデルをマージしたモデルを販売する</br>
Sell this model or merges using this model
</td>
</tr>
<tr class="bg-danger-100">
<td class="align-middle px-2 w-8">
<span style="font-size: 18px;">
🚫
</span>
</td>
<td>
このモデルをマージしたモデルに異なる権限を設定する</br>
Have different permissions when sharing merges
</td>
</tr>
</tbody>
</table>
</div>
<hr>
## 推奨設定 / Recommended Settings
<pre style="margin: 1em 0; padding: 1em; border-radius: 5px; white-space: pre-line;">
Steps: 20 ~ 60
Sampler: DPM++ 3M SDE Exponential
CFG scale: 7.5
Denoising strength: 0.55
Hires steps: 20
Hires upscaler: R-ESRGAN 4x+ Anime6B
Clip skip: 2
</pre>
Negative:
<pre style="margin: 1em 0; padding: 1em; border-radius: 5px; white-space: pre-line;">
(easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad, (inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95),verybadimagenegative_v1.3,
</pre>
<hr>
## 例 / Examples
<div class="flex justify-center">
<div class="container mx-auto px-2">
<div class="flex flex-wrap min-w-min items-baseline">
<div class="p-1 flex-1" style="width: 50%; min-width: 320px; flex-basis: 50%;">
<div class="flex-1">
<img
alt="gallery"
class="block h-full w-full rounded-t-lg object-contain object-center"
src="https://huggingface.co/Vsukiyaki/Yaki-Dofu-Mix/resolve/main/imgs/sample01.png"
loading="lazy"
/>
</div>
<div class="w-full">
<pre class="w-full" style="white-space: pre-line;">
(solo:1.2),cute girl,(pink short hair),(casual wavy hair:1.3), blunt bangs,blush,head tilt,upper body,black cap,oversized black t-shirt,simple background,white background,cowboy shot,shadow,choker,
Negative prompt: (easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad, (inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95),verybadimagenegative_v1.3,
Steps: 60,
Sampler: DPM++ 3M SDE Exponential,
CFG scale: 7.5,
Seed: 1452497008,
Size: 768x768,
Denoising strength: 0.55,
Clip skip: 2,
Hires upscale: 2.5,
Hires steps: 20,
Hires upscaler: R-ESRGAN 4x+ Anime6B,
</pre>
</div>
</div>
<div class="p-1 flex-1" style="width: 50%; min-width: 320px; flex-basis: 50%;">
<div class="w-full">
<img
alt="gallery"
class="block h-full w-full rounded-t-lg object-contain object-center"
src="https://huggingface.co/Vsukiyaki/Yaki-Dofu-Mix/resolve/main/imgs/sample02.png"
loading="lazy"
/>
</div>
<div class="w-full">
<pre class="w-full" style="white-space: pre-line;">
night,cute girl against wall in the downtown,solo,from side,pink hair,(casual wavy hair:1.3),blunt bangs,duffel coat,plaid skirt,scarf,blush,(depth of field:1.3),(night view),dynamic angle,outdoor,cowboy shot,
Negative prompt: (easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad, (inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95),verybadimagenegative_v1.3,
Steps: 60,
Sampler: DPM++ 3M SDE Exponential,
CFG scale: 7.5,
Seed: 3362678745,
Size: 760x768,
Denoising strength: 0.55,
Clip skip: 2,
Hires upscale: 2.5,
Hires steps: 20,
Hires upscaler: R-ESRGAN 4x+ Anime6B,
</pre>
</div>
</div>
<div class="p-1 flex-1" style="width: 50%; min-width: 320px; flex-basis: 50%;">
<div class="flex-1">
<img
alt="gallery"
class="block h-full w-full rounded-t-lg object-contain object-center"
src="https://huggingface.co/Vsukiyaki/Yaki-Dofu-Mix/resolve/main/imgs/sample03.png"
loading="lazy"
/>
</div>
<div class="w-full">
<pre class="w-full" style="white-space: pre-line;">
((solo:1.2)),cute girl sitting on bench in garden,frilled dirndl,from above,looking up,cobblestone pavement,aqua hair,fine bob cut,(hair over one eye),(dappled sunlight:1.2),blurry,(depth of field:1.1),head tilt,:o,(petals),tree,butterfly
Negative prompt: (easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad, (inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95),verybadimagenegative_v1.3,
Steps: 60,
Sampler: DPM++ 3M SDE Exponential,
CFG scale: 7.5,
Seed: 617162279,
Size: 760x768,
Denoising strength: 0.55,
Clip skip: 2,
Hires upscale: 2.5,
Hires steps: 20,
Hires upscaler: R-ESRGAN 4x+ Anime6B,
</pre>
</div>
</div>
<div class="p-1 flex-1" style="width: 50%; min-width: 320px; flex-basis: 50%;">
<div class="w-full">
<img
alt="gallery"
class="block h-full w-full rounded-t-lg object-contain object-center"
src="https://huggingface.co/Vsukiyaki/Yaki-Dofu-Mix/resolve/main/imgs/sample04.png"
loading="lazy"
/>
</div>
<div class="w-full">
<pre class="w-full" style="white-space: pre-line;">
cute girl standing on a beautiful beach,white t-shirt,(brown hair:1.3,brown eyes),(casual wavy long hair:1.3),splash,looking at viewer,upper body,sunset view,chromatic aberration,(depth of field:1.3),cinematic lighting,serenity,wind
Negative prompt: (easynegative:1.0),(worst quality,low quality:1.2),(bad anatomy:1.4),(realistic:1.1),nose,lips,adult,fat,sad, (inaccurate limb:1.2),extra digit,fewer digits,six fingers,(monochrome:0.95),verybadimagenegative_v1.3,
Steps: 60,
Sampler: DPM++ 3M SDE Exponential,
CFG scale: 7.5,
Seed: 1118141335,
Size: 768x768,
Denoising strength: 0.55,
Clip skip: 2,
Hires upscale: 2.5,
Hires steps: 20,
Hires upscaler: R-ESRGAN 4x+ Anime6B,
</pre>
</div>
</div>
</div>
</div>
</div>
<hr>
Twiter: [@Vsukiyaki_AIArt](https://twitter.com/Vsukiyaki_AIArt)
<a
href="https://twitter.com/Vsukiyaki_AIArt"
class="mb-2 inline-block rounded px-6 py-2.5 text-white shadow-md"
style="background-color: #1da1f2">
<svg xmlns="http://www.w3.org/2000/svg" class="h-3.5 w-3.5" fill="currentColor" viewBox="0 0 24 24">
<path d="M24 4.557c-.883.392-1.832.656-2.828.775 1.017-.609 1.798-1.574 2.165-2.724-.951.564-2.005.974-3.127 1.195-.897-.957-2.178-1.555-3.594-1.555-3.179 0-5.515 2.966-4.797 6.045-4.091-.205-7.719-2.165-10.148-5.144-1.29 2.213-.669 5.108 1.523 6.574-.806-.026-1.566-.247-2.229-.616-.054 2.281 1.581 4.415 3.949 4.89-.693.188-1.452.232-2.224.084.626 1.956 2.444 3.379 4.6 3.419-2.07 1.623-4.678 2.348-7.29 2.04 2.179 1.397 4.768 2.212 7.548 2.212 9.142 0 14.307-7.721 13.995-14.646.962-.695 1.797-1.562 2.457-2.549z" />
</svg>
</a>
|
votepurchase/artiwaifu-diffusion-1.0 | votepurchase | 2024-05-23T04:39:28Z | 294 | 2 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-05-23T04:39:28Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
tags:
- text-to-image
- stable-diffusion
- safetensors
- stable-diffusion-xl
- diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
pipeline_tag: text-to-image
---
<h1 align="center"><strong style="font-size: 48px;">ArtiWaifu Diffusion 1.0</strong></h1>
<p align="center">
<img src="https://i.postimg.cc/RFN05PW0/1.png" alt="alt text" title="Cover" width="450"/>
</p>
We have released the **A**rti**Wa**ifu Diffusion V1.0 model, designed to generate aesthetically pleasing and faithfully restored anime-style illustrations.
The AWA Diffusion is an iteration of the Stable Diffusion XL model, mastering over 6000 artistic styles and more than 4000 anime characters, generating images through [trigger words](#trigger-words).
As a specialized image generation model for anime, it excels in producing high-quality anime images, especially in generating images with highly recognizable styles and characters while maintaining a consistently high-quality aesthetic expression.
## Model Details
The AWA Diffusion model is fine-tuned from Stable Diffusion XL, with a selected dataset of 1.5M high-quality anime images, covering a wide range of both popular and niche anime concepts up to April 15, 2024.
AWA Diffusion employs our most advanced training strategies, enabling users to easily induce the model to generate images of specific characters or styles while maintaining high image quality and aesthetic expression.
**Model Information**
- Developed by: [Euge](https://civitai.com/user/Euge_)
- Funded by: [Neta.art](https://nieta.art/)
- Model type: Generative text-to-image model
- Finetuned from model: [SDXL 1.0 Base](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
- License: [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/)
## Usage Guide
This guide will (i) introduce the model's recommended usage methods and prompt writing strategies, aiming to provide suggestions for generation, and (ii) serve as a reference document for model usage, detailing the writing patterns and strategies for trigger words, quality tags, rating tags, style tags, and character tags.
### Basic Usage
- **CFG scale**: <span style="color:cyan">5-11</span>
- **Resolution**: Area (= width x height) around 1024x1024. Not lower than 256x256, and resolutions where both length and width are multiples of 32.
- **Sampling method**: Euler A (<span style="color:cyan">50+</span> steps) or DPM++ 2M Karras (<span style="color:cyan">~35</span> steps)
Due to the special training method, AWA's optimal inference step count is higher than regular values. As the inference steps increase, the quality of the generated images can continue to improve...
❓ **Question:** Why not use the standard SDXL resolution?
💡 **Answer:** Because the bucketing algorithm used in training does not adhere to a fixed set of buckets. Although this does not conform to positional encoding, we have not observed any adverse effects.
### Prompting Strategies
All text-to-image diffusion models have a notoriously high sensitivity to prompt, and AWA Diffusion is no exception. Even a misspelling in the prompt, or even replacing spaces with underscores, can affect the generated results.
AWA Diffusion encourages users to write prompt in **tags** separated by **comma + space (`, `)**. Although the model also supports natural language descriptions as prompt, or an intermix of both, the tag-by-tag format is more stable and user-friendly.
When describing a specific ACG concept, such as a character, style, or scene, we recommend users choose tags from the [Danbooru tags](https://danbooru.donmai.us/tags) and replace underscores in the Danbooru tags with spaces to ensure the model accurately understands your needs. For example, `bishop_(chess)` should be written as `bishop (chess)`, and in inference tools like AUTOMATIC1111 WebUI that use parentheses to weight prompt, all parentheses within the tags should be escaped, i.e., `bishop \(chess\)`.
#### Tag Ordering
Including AWA Diffusion, most diffusion models better understand logically ordered tags. While tag ordering is not mandatory, it can help the model better understand your needs. Generally, the earlier the tag in the order, the greater its impact on generation.
Here's an example of tag ordering. The example organizes the order of tags, prepends [art style tags](#style-tags) and [character tags](#character-tags) because style and subject are the most important to the image. Subsequently, other tags are added in order of importance. Lastly, [aesthetic tags](#aesthetic-tags) and [quality tags](#quality-tags) are positioned at the end to further emphasize the aesthetics of the image.
art style (<span style="color:red">_by xxx_</span>) -> character (<span style="color:orange">_1 frieren (sousou no frieren)_</span>) -> race (elf) -> composition (cowboy shot) -> painting style (<span style="color:green">_impasto_</span>) -> theme (fantasy theme) -> main environment (in the forest, at day) -> background (gradient background) -> action (sitting on ground) -> expression (expressionless) -> main characteristics (white hair) -> other characteristics (twintails, green eyes, parted lip) -> clothing (wearing a white dress) -> clothing accessories (frills) -> other items (holding a magic wand) -> secondary environment (grass, sunshine) -> aesthetics (<span style="color:blue">_beautiful color_</span>, <span style="color:cyan">_detailed_</span>) -> quality (<span style="color:purple">_best_</span> quality) -> secondary description (birds, cloud, butterfly)
Tag order is not set in stone. Flexibility in writing prompt can yield better results. For example, if the effect of a concept (such as style) is too strong and detracts from the aesthetic appeal of the image, you can move it to a later position to reduce its impact.
#### Negative Prompt
Negative prompt are not necessary for AWA Diffusion. If you use negative prompt, it is not the case that the more negative prompt, the better. They should be **as concise as possible and easily recognizable by the model**. Too many negative words may lead to poorer generation results.
Here are some recommended scenarios for using negative prompt:
1. Watermark: `signature`, `logo`, `artist name`;
2. Quality: `worst quality`, `lowres`, `ugly`, `abstract`;
3. Style: `real life`, `3d`, `celluloid`, `sketch`, `draft`;
4. Human anatomy: `deformed hand`, `fused fingers`, `extra limbs`, `extra arms`, `missing arm`, `extra legs`, `missing leg`, `extra digits`, `fewer digits`.
### Trigger Words
Add trigger words to your prompts to inform the model about the concept you want to generate. Trigger words can include character names, artistic styles, scenes, actions, quality, etc.
**Tips for Trigger Word**
1. **Typos**: The model is very sensitive to the spelling of trigger words. Even a single letter difference can cause a trigger to fail or lead to unexpected results.
2. **Bracket Escaping**: Pay attention when using inference tools that rely on parentheses for weighting prompt, such as AUTOMATIC1111 WebUI, to escape parentheses in trigger words, e.g., `1 lucy (cyberpunk)` -> `1 lucy \(cyberpunk\)`.
3. **Triggering Effect Preview**：Through searching tags on [Danbooru](https://danbooru.donmai.us/tags) to preview the tag and better understand the tag's meaning and usage.
#### Style Tags
Style tags are divided into two types: <span style="color:red">Painting Style Tags</span> and <span style="color:blue">Artistic Style Tags</span>. <span style="color:red">Painting Style Tags</span> describe the painting techniques or media used in the image, such as oil painting, watercolor, flat color, and impasto. <span style="color:blue">Artistic Style Tags</span> represent the artistic style of the artist behind the image.
AWA Diffusion supports the following <span style="color:red">Painting Style Tags</span>:
- Painting style tags available in the Danbooru tags, such as `oil painting`, `watercolor`, `flat color`, etc.;
- All painting style tags supported by [AID XL 0.8](https://civitai.com/models/124189/anime-illust-diffusion-xl), such as `flat-pasto`, etc.;
- All style tags supported by [Neta Art XL 1.0](https://civitai.com/models/410737/neta-art-xl), such as `gufeng`, etc.;
See the [Painting Style Tags List](https://huggingface.co/Eugeoter/artiwaifu-diffusion-1.0/blob/main/references/style.csv) for full lists of painting style tags.
AWA Diffusion supports the following <span style="color:blue">Artistic Style Tags</span>:
- Artistic style tags available in the Danbooru tags, such as `by yoneyama mai`, `by wlop`, etc.;
- All artistic style tags supported by [AID XL 0.8](https://civitai.com/models/124189/anime-illust-diffusion-xl), such as `by antifreeze3`, `by 7thknights`, etc.;
See the [Artistic Style Tags List](https://huggingface.co/Eugeoter/artiwaifu-diffusion-1.0/blob/main/references/artist.csv) for full lists of artistic style tags.
The higher the tag count in the tag repository, the more thoroughly the artistic style has been trained, and the higher the fidelity in generation. Typically, artistic style tags with a count higher than **50** yield better generation results.
**Tips for Style Tag**
1. **Intensity Adjustment**: You can adjust the intensity of a style by altering the order or weighting of style tags in your prompt. Frontloading a style tag enhances its effect, while placing it later reduces its effect.
❓ **Question:** Why include the prefix `by` in artistic style tags?
💡 **Answer:** To clearly inform the model that you want to generate a specific artistic style rather than something else, we recommend including the prefix `by` in artistic style tags. This differentiates `by xxx` from `xxx`, especially when `xxx` itself carries other meanings, such as `dino` which could represent either a dinosaur or an artist's identifier.
Similarly, when triggering characters, add a `1` as a prefix to the character trigger word.
#### Character Tags
Character tags describe the character IP in the generated image. Using character tags will guide the model to generate the **appearance features** of the character.
Character tags also need to be sourced from the [Character Tag List](https://huggingface.co/Eugeoter/artiwaifu-diffusion-1.0/blob/main/references/character.csv). To generate a specific character, first find the corresponding trigger word in the tag repository, replace all underscores `_` in the trigger word with spaces ` `, and prepend `1 ` to the character name.
For example, `1 ayanami rei` triggers the model to generate the character Rei Ayanami from the anime "EVA," corresponding to the Danbooru tag `ayanami_rei`; `1 asuna (sao)` triggers the model to generate the character Asuna from "Sword Art Online," corresponding to the Danbooru tag `asuna_(sao)`.
[More examples](#examples)
The higher the tag count in the tag repository, the more thoroughly the character has been trained, and the higher the fidelity in generation. Typically, character tags with a count higher than **100** yield better generation results.
**Tips for Character Tag**
1. **Character Costuming**: To achieve more flexible character costuming, character tags do not deliberately guide the model to draw the official attire of the character. To generate a character in a specific official outfit, besides the trigger word, you should also include a description of the attire in the prompt, e.g., "1 lucy (cyberpunk), <span style="color:cyan">wearing a white cropped jacket, underneath bodysuit, shorts, thighhighs, hip vent</span>".
2. **Series Annotations**: Some character tags include additional parentheses annotations after the character name. The parentheses and the annotations within cannot be omitted, e.g., `1 lucy (cyberpunk)` cannot be written as `1 lucy`. Other than that, you don't need to add any additional annotations, for example, you DON'T need to add the series tag to which the character belongs after the character tag.
3. **Known Issue 1**: When generating certain characters, mysterious feature deformations may occur, e.g., `1 asui tsuyu` triggering the character Tsuyu Asui from "My Hero Academia" may result in an extra black line between the eyes. This is because the model incorrectly interprets the large round eyes as glasses, thus `glasses` should be included in the negative prompt to avoid this issue.
4. **Known Issue 2**: When generating less popular characters, AWA Diffusion might produce images with incomplete feature restoration due to insufficient data/training. In such cases, we recommend that you extend the character description in your prompt beyond just the character name, detailing the character's origin, race, hair color, attire, etc.
**Character Tag Trigger Examples**
| Trigger Word | Note |
| ------------------------------- | -------------------------------------------------------------- |
| 1 lucy (cyberpunk) | ✅ Correct character tag |
| 1 lucy | ❌ Missing bracket annotation |
| 1 lucy (cyber) | ❌ Incorrect bracket annotation |
| lucy (cyberpunk) | ❌ Missing prefix `1 ` |
| 1 lucy cyberpunk | ❌ Missing brackets |
| 1 lucy (cyberpunk | ❌ Bracket not closed |
| 1 lucky (cyberpunk) | ❌ Spelling error |
| 1 lucy (cyberpunk: edgerunners) | ❌ Bracket annotation not following the required character tag |
❓ **Question:** Why do some character tags contain bracket annotations, e.g., `lucy (cyberpunk)`, while others do not, e.g., `frieren`?
💡 **Answer:** In different works, there may be characters with the same name, such as Asuna from "Sword Art Online" and "Blue Archive". To distinguish these characters with the same name, it is necessary to annotate the character's name with the work's name, abbreviated if the name is too long. For characters with unique names that currently have no duplicates, like `frieren`, no special annotations are required. Here is an example:
#### Quality Tags and Aesthetic Tags
For AWA Diffusion, including quality descriptors in your positive prompt is **very important**. Quality descriptions relate to quality tags and aesthetic tags.
Quality tags directly describe the aesthetic quality of the generated image, impacting the detail, texture, human anatomy, lighting, color, etc. Adding quality tags helps the model generate higher quality images. Quality tags are ranked from highest to lowest as follows:
<span style="color:orange">amazing quality</span> -> <span style="color:purple">best quality</span> -> <span style="color:blue">high quality</span> -> <span style="color:green">normal quality</span> -> low quality -> <span style="color:grey">worst quality</span>
Aesthetic tags describe the aesthetic features of the generated image, aiding the model in producing artistically appealing images. In addition to typical aesthetic words like `perspective`, `lighting and shadow`, AWA Diffusion has been specially trained to respond effectively to aesthetic trigger words such as `beautiful color`, `detailed`, and `aesthetic`, which respectively express appealing colors, details, and overall beauty.
The recommended generic way to describe quality is: _(Your Prompt), <span style="color:orange">beautiful color, detailed, amazing quality</span>_
**Tips for Quality and Aesthetic Tags**
1. **Tag Quantity**: Only one quality tag is needed; multiple aesthetic tags can be added.
2. **Tag Position**: The position of quality and aesthetic tags is not fixed, but they are typically placed at the end of the prompt.
3. **Relative Quality**: There is no absolute hierarchy of quality; the implied quality aligns with general aesthetic standards, and different users may have different perceptions of quality.
#### Rating Tags
Rating tags describe the level of exposure in the content of the generated image. Rating tags are ranked from highest to lowest as follows:
<span style="color:green">rating: general</span> (or <span style="color:green">safe</span>) -> <span style="color:yellow">rating: suggestive</span> -> <span style="color:orange">rating: questionable</span> -> <span style="color:red">rating: explicit</span> (or <span style="color:red">nsfw</span>)
### Prompt Word Examples
#### Example 1
**A**
_<span style="color:green">by yoneyama mai</span>, <span style="color:blue">1 frieren</span>, 1girl, solo, fantasy theme, smile, holding a magic wand, <span style="color:yellow">beautiful color</span>, <span style="color:red">amazing quality</span>_
1. <span style="color:green">by yoneyama mai</span> triggers the artistic style of Yoneyama Mai, placed at the front to enhance the effect.
2. <span style="color:blue">1 frieren</span> triggers the character Frieren from the series "Frieren at the Funeral."
3. <span style="color:yellow">beautiful color</span> describes the beautiful colors in the generated image.
4. <span style="color:red">amazing quality</span> describes the stunning quality of the generated image.
**B**
_<span style="color:green">by nixeu</span>, <span style="color:blue">1 lucy (cyberpunk)</span>, 1girl, solo, cowboy shot, gradient background, white cropped jacket, underneath bodysuit, shorts, thighhighs, hip vent, <span style="color:yellow">detailed</span>, <span style="color:red">best quality</span>_
#### Example 2: Style Mixing
By layering multiple different style tags, you can generate images with features of multiple styles.
**A** Simple Mixing
_**<span style="color:green">by ningen mame</span>, <span style="color:cyan">by ciloranko</span>, <span style="color:blue">by sho (sho lwlw)</span>**, 1girl, 1 hatsune miku, sitting, arm support, smile, detailed, amazing quality_
**B** Weighted Mixing
Using AUTOMATIC1111 WebUI prompt weighting syntax (parentheses weighting), weight different style tags to better control the generated image's style.
_**<span style="color:green">(by ningen mame:0.8)</span>, <span style="color:cyan">(by ciloranko:1.1)</span>, <span style="color:blue">(by sho \(sho lwlw\):1.2)</span>**, 1girl, 1 hatsune miku, sitting, arm support, smile, detailed, amazing quality_
#### Example 3: Multi-Character Scenes
By adding multiple character tags to your prompts, you can generate images with multiple characters in the same frame. Compared to other similar models, AWA performs better in multi-character scenes but remains unstable.
**A** Mixed Gender Scene
_**1girl and 1boy, <span style="color:blue">1 ganyu</span> girl, <span style="color:cyan">1 gojou satoru</span> boy**, beautiful color, amazing quality_
**B** Same Gender Scene
_**2girls, <span style="color:blue">1 ganyu</span> girl, <span style="color:orange">1 yoimiya</span> girl**, beautiful color, amazing quality_
## Future Work
AWA Diffusion is expected to combine high-level <span style="color:purple">aesthetics</span> with comprehensive <span style="color:cyan">knowledge</span>. It should neither have the traditional AI's greasy feel nor become a knowledge-deficient vase.
We will continue to explore more advanced training techniques and strategies, consistently improving the model's quality.
## Support Us
Training AWA Diffusion incurs substantial costs. If you appreciate our work, please consider supporting us through [Ko-fi](https://ko-fi.com/eugeai), to aid our research and development efforts. Thank you for your like and support! |
votepurchase/PerfectDeliberate-Anime_v2 | votepurchase | 2024-05-23T04:38:57Z | 310 | 2 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-23T04:38:57Z | ---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/111274?modelVersionId=307086
|
votepurchase/NovelAIRemix | votepurchase | 2024-05-23T04:38:26Z | 270 | 3 | diffusers | [
"diffusers",
"safetensors",
"Anime",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-23T04:38:26Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Anime
---
# NovelAIRemix
NovelAI mixed with SD1.5.
Sample and prompt:
sitting elementary girl, Pretty CUTE, gorgeous hair, Magazine ad, iconic, 1943, Cartoon, sharp focus, 4k. beautiful art on canvas by kyoani and ROSSDRAWS and ross tran. DETAILED CHIBI
Check out:
https://huggingface.co/Yntec/NovelAI
# Recipe
SD1.4Full + fp16 - no-ema = SD1.4 (https://huggingface.co/Yntec/NovelAIRemix/resolve/main/sd-v1-4-fp16-no-ema.safetensors)
SD1.5Full + fp16 - no-ema = SD1.5 (https://huggingface.co/Yntec/DreamLikeRemix/resolve/main/v1-5-pruned-fp16-no-ema.safetensors)
Add Difference (SD1.4 + (SD1.4 - SD1.5)*1)=SD1.5Essence (https://huggingface.co/Yntec/NovelAIRemix/resolve/main/SD1.5Essence.safetensors)
Weighted Sum (SD1.5Essence * (1 - 0.7) + NovelAIFull * 0.7) = NovelAISD1.5
Weighted Sum (NovelAISD1.5 * (1 - 0.7) + NovelAISFW * 0.7) = NovelAIRemix |
votepurchase/NSFW-GEN-ANIME-v2 | votepurchase | 2024-05-23T04:38:14Z | 881 | 5 | diffusers | [
"diffusers",
"safetensors",
"NSFW",
"UnfilteredAI",
"Anime",
"Text-to-Image",
"text-to-image",
"en",
"base_model:OEvortex/PixelGen",
"base_model:finetune:OEvortex/PixelGen",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-05-23T04:38:13Z | ---
base_model:
- OEvortex/PixelGen
- UnfilteredAI/NSFW-gen
license: other
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- NSFW
- UnfilteredAI
- Anime
- Text-to-Image
---
**Model Name:** NSFW-GEN-ANIME
**Type:** Anime Text-to-Image Generator
**Description:** NSFW-GEN-ANIME is a text-to-anime image generator developed by UnfilteredAI. This model is designed to generate various kinds of images, including explicit and NSFW (Not Safe For Work) content, from textual inputs.
**Features:**
- **Anime Output:** The model produces uncensored and potentially explicit anime-style images based on textual inputs.
- **Tensor Type:** Operates with FP16 tensor type for optimized performance and efficiency.
- **Large Model Size:** With 3.47 billion parameters, the model offers a vast capacity for learning and generating diverse anime imagery.
- **Community Engagement:** As part of UnfilteredAI's open-source initiatives, the model encourages collaboration and contributions from the AI community.
**Usage Guidelines:**
- **Responsible Use:** Users are advised to exercise discretion and responsibility when generating content with this model.
- **Age Restriction:** Due to the explicit nature of the generated content, usage is restricted to individuals over the legal age in their jurisdiction.
- **Ethical Considerations:** Avoid using the model to create harmful or offensive anime imagery.
**Get Involved:**
- **Contribute:** Help enhance the capabilities and ethical considerations of the model by contributing to its development on UnfilteredAI's open-source platform.
- **Explore:** Dive into the anime imagery produced by the model to explore its creative potential and applications.
- **Connect:** Engage with the UnfilteredAI community to share insights, feedback, and ideas related to NSFW anime content generation and AI ethics.
|
Vk357/distilbert-base-uncased-finetuned-cola | Vk357 | 2024-05-23T04:38:10Z | 62 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-21T04:58:52Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Vk357/distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Vk357/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5146
- Validation Loss: 0.4586
- Train Matthews Correlation: 0.4722
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5146 | 0.4586 | 0.4722 | 0 |
### Framework versions
- Transformers 4.41.0
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
AlignmentResearch/robust_llm_pythia-70m-pm-gen-ian-nd | AlignmentResearch | 2024-05-23T04:37:58Z | 142 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T04:37:42Z | ---
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.
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### Downstream Use [optional]
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[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
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[More Information Needed]
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### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
AlignmentResearch/robust_llm_pythia-14m-pm-gen-ian-nd | AlignmentResearch | 2024-05-23T04:37:48Z | 5,909 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T04:37:41Z | ---
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
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[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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
votepurchase/Realistic_Vision_V1.4 | votepurchase | 2024-05-23T04:37:09Z | 266 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-23T04:37:08Z | ---
license: creativeml-openrail-m
tags:
- stable-diffusion
- text-to-image
---
<b>Please read this!</b><br>
My model has always been free and always will be free. There are no restrictions on the use of the model. The rights to this model still belong to me.
<b>You can support me directly on Boosty - https://boosty.to/sg_161222</b><br>
<hr/>
<b>Important note: "RAW photo" in the prompt may degrade the result.</b>
<b>I use this template to get good generation results:
Prompt:</b>
*subject*, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Example:</b> a close up portrait photo of 26 y.o woman in wastelander clothes, long haircut, pale skin, slim body, background is city ruins, (high detailed skin:1.2), 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3
<b>Negative Prompt:</b>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck<br>
<b>OR</b><br>
(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation
<b>Euler A or DPM++ 2M Karras with 25 steps<br>
CFG Scale 3,5 - 7<br>
Hires. fix with Latent upscaler<br>
0 Hires steps and Denoising strength 0.25-0.45<br>
Upscale by 1.1-2.0</b> |
krispychicken/whisper-medium-asr-v2 | krispychicken | 2024-05-23T04:33:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T04:33:55Z | ---
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
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[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
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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JoPmt/CodeLlemur-3.5B-Instruct-line | JoPmt | 2024-05-23T04:32:22Z | 87 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"codellama/CodeLlama-7b-Instruct-hf",
"conversational",
"base_model:codellama/CodeLlama-7b-Instruct-hf",
"base_model:finetune:codellama/CodeLlama-7b-Instruct-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T03:44:48Z | ---
tags:
- merge
- mergekit
- lazymergekit
- codellama/CodeLlama-7b-Instruct-hf
base_model:
- codellama/CodeLlama-7b-Instruct-hf
---
# CodeLlemur-2B-Instruct-line
CodeLlemur-2B-Instruct-line is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf)
## 🧩 Configuration
```yaml
dtype: bfloat16
merge_method: linear
slices:
- sources:
- layer_range: [0, 16]
model: codellama/CodeLlama-7b-Instruct-hf
parameters:
weight: 0.25
- layer_range: [16, 32]
model: codellama/CodeLlama-7b-Instruct-hf
parameters:
weight: 0.25
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "JoPmt/CodeLlemur-2B-Instruct-line"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
khaingsmon/xho-2 | khaingsmon | 2024-05-23T04:31:08Z | 78 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:ml-superb-subset",
"base_model:Akashpb13/Swahili_xlsr",
"base_model:finetune:Akashpb13/Swahili_xlsr",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-23T03:14:20Z | ---
license: apache-2.0
base_model: Akashpb13/Swahili_xlsr
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
metrics:
- wer
model-index:
- name: xho-2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: ml-superb-subset
type: ml-superb-subset
config: xho
split: test
args: xho
metrics:
- name: Wer
type: wer
value: 0.6780487804878049
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xho-2
This model is a fine-tuned version of [Akashpb13/Swahili_xlsr](https://huggingface.co/Akashpb13/Swahili_xlsr) on the ml-superb-subset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7671
- Wer: 0.6780
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.033 | 15.3846 | 400 | 0.7671 | 0.6780 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
leo009/mistral-7b-v3 | leo009 | 2024-05-23T04:29:22Z | 10 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-v0.3-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-v0.3-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T04:22:47Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
base_model: unsloth/mistral-7b-v0.3-bnb-4bit
---
# Uploaded model
- **Developed by:** leo009
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.3-bnb-4bit
This mistral 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)
|
choihyunsoo/distilbert-base-uncased-finetuned-emotion | choihyunsoo | 2024-05-23T04:22:33Z | 110 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-05-14T19:59:19Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9255
- name: F1
type: f1
value: 0.9255503643924508
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2150
- Accuracy: 0.9255
- F1: 0.9256
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8042 | 1.0 | 250 | 0.3079 | 0.9075 | 0.9068 |
| 0.2448 | 2.0 | 500 | 0.2150 | 0.9255 | 0.9256 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
NakanoMiku0-0/llama3-patent-finetune-text | NakanoMiku0-0 | 2024-05-23T04:20:49Z | 10 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T00:25:38Z | ---
license: apache-2.0
---
|
johaanm/lora_model | johaanm | 2024-05-23T04:17:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T04:17:22Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** johaanm
- **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)
|
nitus-ac/nMer3a | nitus-ac | 2024-05-23T04:12:31Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Undi95/Llama-3-LewdPlay-8B",
"base_model:merge:Undi95/Llama-3-LewdPlay-8B",
"base_model:ajibawa-2023/General-Stories-Mistral-7B",
"base_model:merge:ajibawa-2023/General-Stories-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T03:54:25Z | ---
base_model:
- ajibawa-2023/General-Stories-Mistral-7B
- Undi95/Llama-3-LewdPlay-8B
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [ajibawa-2023/General-Stories-Mistral-7B](https://huggingface.co/ajibawa-2023/General-Stories-Mistral-7B)
* [Undi95/Llama-3-LewdPlay-8B](https://huggingface.co/Undi95/Llama-3-LewdPlay-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: ajibawa-2023/General-Stories-Mistral-7B
- model: Undi95/Llama-3-LewdPlay-8B
merge_method: slerp
base_model: ajibawa-2023/General-Stories-Mistral-7B
dtype: bfloat16
parameters:
t: [0, 0.5, 0.3, 0.7, 1]
```
|
acsankar/InsuranceGPT-16bit_ver1.1 | acsankar | 2024-05-23T04:11:22Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T04:05:06Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# 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]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
datek/gemma-2b-flock-1716437255 | datek | 2024-05-23T04:10:12Z | 144 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T04:07:36Z | ---
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] |
Sirawipa/tian-ft8bit | Sirawipa | 2024-05-23T04:07:36Z | 1 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"generated_from_trainer",
"base_model:openthaigpt/openthaigpt-1.0.0-7b-chat",
"base_model:adapter:openthaigpt/openthaigpt-1.0.0-7b-chat",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2024-05-23T04:01:01Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: openthaigpt/openthaigpt-1.0.0-7b-chat
model-index:
- name: tian-ft8bit
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tian-ft8bit
This model is a fine-tuned version of [openthaigpt/openthaigpt-1.0.0-7b-chat](https://huggingface.co/openthaigpt/openthaigpt-1.0.0-7b-chat) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3650
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6818 | 0.9362 | 11 | 1.2394 |
| 0.7417 | 1.9574 | 23 | 0.4204 |
| 0.3016 | 2.9787 | 35 | 0.3303 |
| 0.1401 | 4.0 | 47 | 0.3590 |
| 0.0825 | 4.9362 | 58 | 0.3573 |
| 0.0601 | 5.9574 | 70 | 0.3517 |
| 0.0515 | 6.9787 | 82 | 0.3590 |
| 0.0483 | 8.0 | 94 | 0.3617 |
| 0.05 | 8.9362 | 105 | 0.3620 |
| 0.0393 | 9.3617 | 110 | 0.3650 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 |
pritiOli/llama3-fine-tuned-student-assessment | pritiOli | 2024-05-23T04:06:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-22T19:42:31Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** pritiOli
- **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)
|
xkiwilabs/lora_opComms_LLama3_v5 | xkiwilabs | 2024-05-23T04:02:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T04:02:37Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** xkiwilabs
- **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)
|
GENIAC-Team-Ozaki/lora-dpo-finetuned-stage4-sft-multitest_0.1_5e-7_ep-15 | GENIAC-Team-Ozaki | 2024-05-23T03:58:19Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T03:48:27Z | ---
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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[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. -->
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<!-- 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]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
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BotCuddles/llama3_try2 | BotCuddles | 2024-05-23T03:55:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T03:54:46Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** BotCuddles
- **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)
|
reinel215/whisper-small-panita-v1 | reinel215 | 2024-05-23T03:52:41Z | 79 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-22T17:30:27Z | ---
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]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
MikeGreen2710/street_alley_classification | MikeGreen2710 | 2024-05-23T03:44:19Z | 33 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"generated_from_trainer",
"base_model:MikeGreen2710/mlm_listing_1.79",
"base_model:finetune:MikeGreen2710/mlm_listing_1.79",
"endpoints_compatible",
"region:us"
] | null | 2024-05-21T08:28:34Z | ---
base_model: MikeGreen2710/mlm_listing_1.79
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: training_with_callbacks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# training_with_callbacks
This model is a fine-tuned version of [MikeGreen2710/mlm_listing_1.79](https://huggingface.co/MikeGreen2710/mlm_listing_1.79) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3694
- F1: 0.9412
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 24
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.3883 | 1.0 | 140 | 0.2135 | 0.9032 |
| 0.196 | 2.0 | 280 | 0.2403 | 0.9028 |
| 0.1268 | 3.0 | 420 | 0.2044 | 0.9251 |
| 0.0797 | 4.0 | 560 | 0.2866 | 0.9245 |
| 0.0506 | 5.0 | 700 | 0.3358 | 0.9232 |
| 0.0283 | 6.0 | 840 | 0.3247 | 0.9349 |
| 0.0219 | 7.0 | 980 | 0.3160 | 0.9355 |
| 0.0173 | 8.0 | 1120 | 0.3366 | 0.9384 |
| 0.0112 | 9.0 | 1260 | 0.3360 | 0.9409 |
| 0.0147 | 10.0 | 1400 | 0.3523 | 0.9384 |
| 0.0087 | 11.0 | 1540 | 0.3694 | 0.9412 |
| 0.0075 | 12.0 | 1680 | 0.3761 | 0.9381 |
| 0.0041 | 13.0 | 1820 | 0.4205 | 0.9323 |
| 0.0059 | 14.0 | 1960 | 0.4012 | 0.9409 |
| 0.0058 | 15.0 | 2100 | 0.4717 | 0.9269 |
| 0.0027 | 16.0 | 2240 | 0.4644 | 0.9325 |
| 0.0033 | 17.0 | 2380 | 0.4491 | 0.9353 |
| 0.0011 | 18.0 | 2520 | 0.4494 | 0.9353 |
| 0.0025 | 19.0 | 2660 | 0.4440 | 0.9381 |
| 0.0012 | 20.0 | 2800 | 0.4482 | 0.9381 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
JL42/NewMes-v7-GGUF | JL42 | 2024-05-23T03:40:38Z | 4 | 1 | transformers | [
"transformers",
"gguf",
"Text Generation",
"medical",
"Inference Endpoints",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-23T03:35:36Z | ---
license: llama3
library_name: transformers
tags:
- Text Generation
- medical
- Inference Endpoints
---
Base model: Llama-3-8B
## Model Description
- **Developed by:** bongbongs
- **Model type:** LLM
- **Language(s) (NLP):** English
- **Finetuned from model:** llama-3-8b
Fine-tuned on medical training datsets |
ronenh24/distilbert-base-uncased-finetuned-imdb | ronenh24 | 2024-05-23T03:35:26Z | 107 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:stanfordnlp/imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-05-20T19:39:30Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
datasets:
- stanfordnlp/imdb
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4894
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6819 | 1.0 | 157 | 2.4978 |
| 2.5872 | 2.0 | 314 | 2.4488 |
| 2.527 | 3.0 | 471 | 2.4823 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
toanduc/vit5-base-vietnews-summarization-sport | toanduc | 2024-05-23T03:34:22Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-base-vietnews-summarization",
"base_model:finetune:VietAI/vit5-base-vietnews-summarization",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-03-11T19:44:13Z | ---
license: mit
base_model: VietAI/vit5-base-vietnews-summarization
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: vit5-base-vietnews-summarization-sport
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit5-base-vietnews-summarization-sport
This model is a fine-tuned version of [VietAI/vit5-base-vietnews-summarization](https://huggingface.co/VietAI/vit5-base-vietnews-summarization) on an toanduc/vietnamese-sport-newspapers-summarization dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7541
- Rouge1: 24.4342
- Rouge2: 12.2779
- Rougel: 18.7382
- Rougelsum: 21.4735
- Gen Len: 19.0
## Model description
Summary model for Vietnamese sports articles under the transfer tag
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.7931 | 1.0 | 2897 | 1.6246 | 25.1902 | 11.9842 | 18.7125 | 21.8765 | 19.0 |
| 1.5869 | 2.0 | 5794 | 1.5557 | 24.8104 | 12.918 | 19.1867 | 21.9091 | 19.0 |
| 1.4568 | 3.0 | 8691 | 1.5264 | 24.3498 | 12.6452 | 18.7426 | 21.4748 | 19.0 |
| 1.3375 | 4.0 | 11588 | 1.5259 | 24.3584 | 12.2843 | 18.7177 | 21.4296 | 19.0 |
| 1.2417 | 5.0 | 14485 | 1.5299 | 24.3633 | 11.9901 | 18.5573 | 21.4541 | 19.0 |
| 1.1705 | 6.0 | 17382 | 1.5533 | 24.4314 | 12.3337 | 18.7517 | 21.5669 | 19.0 |
| 1.0965 | 7.0 | 20279 | 1.5716 | 24.7562 | 12.4883 | 18.8926 | 21.7281 | 19.0 |
| 1.0479 | 8.0 | 23176 | 1.5918 | 24.3464 | 12.4018 | 18.6725 | 21.3891 | 19.0 |
| 0.9993 | 9.0 | 26073 | 1.6116 | 24.6254 | 12.1213 | 18.7091 | 21.5747 | 19.0 |
| 0.9512 | 10.0 | 28970 | 1.6427 | 24.6133 | 12.4088 | 18.8244 | 21.6162 | 19.0 |
| 0.8903 | 11.0 | 31867 | 1.6595 | 24.4154 | 12.4455 | 18.721 | 21.4605 | 19.0 |
| 0.8463 | 12.0 | 34764 | 1.6953 | 24.6333 | 12.2631 | 18.8074 | 21.6077 | 19.0 |
| 0.8224 | 13.0 | 37661 | 1.7127 | 24.2227 | 12.141 | 18.5775 | 21.321 | 19.0 |
| 0.8003 | 14.0 | 40558 | 1.7348 | 24.3755 | 12.1357 | 18.6279 | 21.383 | 19.0 |
| 0.7771 | 15.0 | 43455 | 1.7455 | 24.4422 | 12.1864 | 18.7155 | 21.4499 | 19.0 |
| 0.7666 | 16.0 | 46352 | 1.7541 | 24.4342 | 12.2779 | 18.7382 | 21.4735 | 19.0 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Sorrowair/binding_site_35M_adap | Sorrowair | 2024-05-23T03:30:59Z | 3 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:westlake-repl/SaProt_35M_AF2",
"base_model:adapter:westlake-repl/SaProt_35M_AF2",
"region:us"
] | null | 2024-05-23T03:30:57Z | ---
library_name: peft
base_model: westlake-repl/SaProt_35M_AF2
---
# Model Card for Model ID
This model is used for a demo task<br><br> The digital label means: <br>0: <br>
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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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.
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## Training Details
### Training Data
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<!-- 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).
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### Framework versions
- PEFT 0.11.1 |
hbminsi/segformer-b0-finetuned-segments-sidewalk-2 | hbminsi | 2024-05-23T03:30:57Z | 194 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | 2024-05-23T03:16:55Z | ---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-segments-sidewalk-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# segformer-b0-finetuned-segments-sidewalk-2
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the segments/sidewalk-semantic dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7084
- Mean Iou: 0.0871
- Mean Accuracy: 0.1451
- Overall Accuracy: 0.6167
- Accuracy Unlabeled: nan
- Accuracy Flat-road: 0.5180
- Accuracy Flat-sidewalk: 0.9088
- Accuracy Flat-crosswalk: 0.0001
- Accuracy Flat-cyclinglane: 0.0259
- Accuracy Flat-parkingdriveway: 0.0
- Accuracy Flat-railtrack: nan
- Accuracy Flat-curb: 0.0012
- Accuracy Human-person: 0.0017
- Accuracy Human-rider: 0.0
- Accuracy Vehicle-car: 0.9553
- Accuracy Vehicle-truck: 0.0
- Accuracy Vehicle-bus: 0.0
- Accuracy Vehicle-tramtrain: nan
- Accuracy Vehicle-motorcycle: 0.0
- Accuracy Vehicle-bicycle: 0.0000
- Accuracy Vehicle-caravan: 0.0
- Accuracy Vehicle-cartrailer: 0.0
- Accuracy Construction-building: 0.4663
- Accuracy Construction-door: 0.0
- Accuracy Construction-wall: 0.0670
- Accuracy Construction-fenceguardrail: 0.0
- Accuracy Construction-bridge: 0.0
- Accuracy Construction-tunnel: nan
- Accuracy Construction-stairs: 0.0
- Accuracy Object-pole: 0.0064
- Accuracy Object-trafficsign: 0.0
- Accuracy Object-trafficlight: 0.0
- Accuracy Nature-vegetation: 0.9708
- Accuracy Nature-terrain: 0.0024
- Accuracy Sky: 0.5740
- Accuracy Void-ground: 0.0
- Accuracy Void-dynamic: 0.0
- Accuracy Void-static: 0.0
- Accuracy Void-unclear: 0.0
- Iou Unlabeled: nan
- Iou Flat-road: 0.3925
- Iou Flat-sidewalk: 0.6649
- Iou Flat-crosswalk: 0.0001
- Iou Flat-cyclinglane: 0.0249
- Iou Flat-parkingdriveway: 0.0
- Iou Flat-railtrack: 0.0
- Iou Flat-curb: 0.0012
- Iou Human-person: 0.0017
- Iou Human-rider: 0.0
- Iou Vehicle-car: 0.2851
- Iou Vehicle-truck: 0.0
- Iou Vehicle-bus: 0.0
- Iou Vehicle-tramtrain: 0.0
- Iou Vehicle-motorcycle: 0.0
- Iou Vehicle-bicycle: 0.0000
- Iou Vehicle-caravan: 0.0
- Iou Vehicle-cartrailer: 0.0
- Iou Construction-building: 0.3825
- Iou Construction-door: 0.0
- Iou Construction-wall: 0.0540
- Iou Construction-fenceguardrail: 0.0
- Iou Construction-bridge: 0.0
- Iou Construction-tunnel: 0.0
- Iou Construction-stairs: 0.0
- Iou Object-pole: 0.0048
- Iou Object-trafficsign: 0.0
- Iou Object-trafficlight: 0.0
- Iou Nature-vegetation: 0.6011
- Iou Nature-terrain: 0.0024
- Iou Sky: 0.5451
- Iou Void-ground: 0.0
- Iou Void-dynamic: 0.0
- Iou Void-static: 0.0
- Iou Void-unclear: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Flat-road | Accuracy Flat-sidewalk | Accuracy Flat-crosswalk | Accuracy Flat-cyclinglane | Accuracy Flat-parkingdriveway | Accuracy Flat-railtrack | Accuracy Flat-curb | Accuracy Human-person | Accuracy Human-rider | Accuracy Vehicle-car | Accuracy Vehicle-truck | Accuracy Vehicle-bus | Accuracy Vehicle-tramtrain | Accuracy Vehicle-motorcycle | Accuracy Vehicle-bicycle | Accuracy Vehicle-caravan | Accuracy Vehicle-cartrailer | Accuracy Construction-building | Accuracy Construction-door | Accuracy Construction-wall | Accuracy Construction-fenceguardrail | Accuracy Construction-bridge | Accuracy Construction-tunnel | Accuracy Construction-stairs | Accuracy Object-pole | Accuracy Object-trafficsign | Accuracy Object-trafficlight | Accuracy Nature-vegetation | Accuracy Nature-terrain | Accuracy Sky | Accuracy Void-ground | Accuracy Void-dynamic | Accuracy Void-static | Accuracy Void-unclear | Iou Unlabeled | Iou Flat-road | Iou Flat-sidewalk | Iou Flat-crosswalk | Iou Flat-cyclinglane | Iou Flat-parkingdriveway | Iou Flat-railtrack | Iou Flat-curb | Iou Human-person | Iou Human-rider | Iou Vehicle-car | Iou Vehicle-truck | Iou Vehicle-bus | Iou Vehicle-tramtrain | Iou Vehicle-motorcycle | Iou Vehicle-bicycle | Iou Vehicle-caravan | Iou Vehicle-cartrailer | Iou Construction-building | Iou Construction-door | Iou Construction-wall | Iou Construction-fenceguardrail | Iou Construction-bridge | Iou Construction-tunnel | Iou Construction-stairs | Iou Object-pole | Iou Object-trafficsign | Iou Object-trafficlight | Iou Nature-vegetation | Iou Nature-terrain | Iou Sky | Iou Void-ground | Iou Void-dynamic | Iou Void-static | Iou Void-unclear |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:------------------:|:----------------------:|:-----------------------:|:-------------------------:|:-----------------------------:|:-----------------------:|:------------------:|:---------------------:|:--------------------:|:--------------------:|:----------------------:|:--------------------:|:--------------------------:|:---------------------------:|:------------------------:|:------------------------:|:---------------------------:|:------------------------------:|:--------------------------:|:--------------------------:|:------------------------------------:|:----------------------------:|:----------------------------:|:----------------------------:|:--------------------:|:---------------------------:|:----------------------------:|:--------------------------:|:-----------------------:|:------------:|:--------------------:|:---------------------:|:--------------------:|:---------------------:|:-------------:|:-------------:|:-----------------:|:------------------:|:--------------------:|:------------------------:|:------------------:|:-------------:|:----------------:|:---------------:|:---------------:|:-----------------:|:---------------:|:---------------------:|:----------------------:|:-------------------:|:-------------------:|:----------------------:|:-------------------------:|:---------------------:|:---------------------:|:-------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:---------------:|:----------------------:|:-----------------------:|:---------------------:|:------------------:|:-------:|:---------------:|:----------------:|:---------------:|:----------------:|
| 2.9796 | 0.4 | 20 | 3.2289 | 0.0664 | 0.1236 | 0.5591 | nan | 0.2423 | 0.9160 | 0.0000 | 0.0110 | 0.0000 | nan | 0.0006 | 0.0021 | 0.0 | 0.9292 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.2970 | 0.0 | 0.1131 | 0.0 | 0.0 | nan | 0.0 | 0.0502 | 0.0 | 0.0 | 0.9793 | 0.0015 | 0.2752 | 0.0 | 0.0148 | 0.0000 | 0.0 | 0.0 | 0.2077 | 0.6059 | 0.0000 | 0.0107 | 0.0000 | 0.0 | 0.0006 | 0.0020 | 0.0 | 0.2985 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.2620 | 0.0 | 0.0654 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0249 | 0.0 | 0.0 | 0.5724 | 0.0015 | 0.2711 | 0.0 | 0.0027 | 0.0000 | 0.0 |
| 2.6315 | 0.8 | 40 | 2.7084 | 0.0871 | 0.1451 | 0.6167 | nan | 0.5180 | 0.9088 | 0.0001 | 0.0259 | 0.0 | nan | 0.0012 | 0.0017 | 0.0 | 0.9553 | 0.0 | 0.0 | nan | 0.0 | 0.0000 | 0.0 | 0.0 | 0.4663 | 0.0 | 0.0670 | 0.0 | 0.0 | nan | 0.0 | 0.0064 | 0.0 | 0.0 | 0.9708 | 0.0024 | 0.5740 | 0.0 | 0.0 | 0.0 | 0.0 | nan | 0.3925 | 0.6649 | 0.0001 | 0.0249 | 0.0 | 0.0 | 0.0012 | 0.0017 | 0.0 | 0.2851 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0000 | 0.0 | 0.0 | 0.3825 | 0.0 | 0.0540 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0048 | 0.0 | 0.0 | 0.6011 | 0.0024 | 0.5451 | 0.0 | 0.0 | 0.0 | 0.0 |
### Framework versions
- Transformers 4.41.1
- Pytorch 1.13.1
- Datasets 2.19.1
- Tokenizers 0.19.1
|
mmacros/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF | mmacros | 2024-05-23T03:24:45Z | 0 | 0 | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:llama3",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-05-23T03:24:31Z | ---
language:
- en
license: llama3
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
pipeline_tag: text-generation
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messages:
- role: user
content: Hey my name is Julien! How are you?
- example_title: Winter holidays
messages:
- role: system
content: You are a helpful and honest assistant. Please, respond concisely and
truthfully.
- role: user
content: Can you recommend a good destination for Winter holidays?
- example_title: Programming assistant
messages:
- role: system
content: You are a helpful and honest code and programming assistant. Please,
respond concisely and truthfully.
- role: user
content: Write a function that computes the nth fibonacci number.
inference:
parameters:
max_new_tokens: 300
stop:
- <|end_of_text|>
- <|eot_id|>
---
# mmacros/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo mmacros/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --model meta-llama-3-8b-instruct.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo mmacros/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --model meta-llama-3-8b-instruct.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m meta-llama-3-8b-instruct.Q4_K_M.gguf -n 128
```
|
nms05/Dinov2-SigLIP-Phi3-LoRA | nms05 | 2024-05-23T03:24:12Z | 0 | 0 | null | [
"visual-question-answering",
"en",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"dataset:liuhaotian/LLaVA-CC3M-Pretrain-595K",
"arxiv:2401.06209",
"region:us"
] | visual-question-answering | 2024-05-21T06:12:58Z | ---
datasets:
- liuhaotian/LLaVA-Instruct-150K
- liuhaotian/LLaVA-CC3M-Pretrain-595K
language:
- en
metrics:
- accuracy
pipeline_tag: visual-question-answering
---
# DinoV2-SigLIP-Phi3(LoRA) VLM
* **Vision Encoder** - DinoV2 + SigLIP @384px resolution. [Why 2 vision encoders?](https://arxiv.org/abs/2401.06209)
* **Connector** - MLP (Dino and SigLIP features are concatenated and then projected to Phi3 representation space)
* **Language Model** - Phi3 + LoRA
* **Pre-train (Align) Dataset** - LLaVA-CC3M-Pretrain-595K
* **Fine-tune (Instruction) Dataset** - LLAVA-v1.5-Instruct + LRV-Instruct
Scripts to build and train the models are available at [NMS05/DinoV2-SigLIP-Phi3-LoRA-VLM](https://github.com/NMS05/DinoV2-SigLIP-Phi3-LoRA-VLM). |
RomBor/poca-SoccerTwos | RomBor | 2024-05-23T03:18:16Z | 14 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2024-05-23T03:18:10Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: RomBor/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
benchang1110/Taiwan-tinyllama-v1.0-chat | benchang1110 | 2024-05-23T03:14:14Z | 152 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"zh",
"dataset:benchang1110/ChatTaiwan",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-21T11:49:42Z | ---
language:
- zh
license: apache-2.0
datasets:
- benchang1110/ChatTaiwan
pipeline_tag: text-generation
widget:
- example_title: 範例一
messages:
- role: user
content: 你好
---
## Model Card for Model ID
This model is the instruction finetuning version of [benchang1110/Taiwan-tinyllama-v1.0-base](https://huggingface.co/benchang1110/Taiwan-tinyllama-v1.0-base).
## Usage
```python
import torch, transformers
def generate_response():
model = transformers.AutoModelForCausalLM.from_pretrained("benchang1110/Taiwan-tinyllama-v1.0-chat", torch_dtype=torch.bfloat16, device_map=device,attn_implementation="flash_attention_2")
tokenizer = transformers.AutoTokenizer.from_pretrained("benchang1110/Taiwan-tinyllama-v1.0-chat")
streamer = transformers.TextStreamer(tokenizer,skip_prompt=True)
while(1):
prompt = input('USER:')
if prompt == "exit":
break
print("Assistant: ")
message = [
{'content': prompt, 'role': 'user'},
]
untokenized_chat = tokenizer.apply_chat_template(message,tokenize=False,add_generation_prompt=False)
inputs = tokenizer.encode_plus(untokenized_chat, add_special_tokens=True, return_tensors="pt",return_attention_mask=True).to(device)
outputs = model.generate(inputs["input_ids"],attention_mask=inputs['attention_mask'],streamer=streamer,use_cache=True,max_new_tokens=512,do_sample=True,temperature=0.1,repetition_penalty=1.2)
if __name__ == '__main__':
device = 'cuda' if torch.cuda.is_available() else 'cpu'
generate_response()
``` |
khaingsmon/xho-1 | khaingsmon | 2024-05-23T03:11:41Z | 78 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:ml-superb-subset",
"base_model:Akashpb13/Swahili_xlsr",
"base_model:finetune:Akashpb13/Swahili_xlsr",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-23T01:24:45Z | ---
license: apache-2.0
base_model: Akashpb13/Swahili_xlsr
tags:
- generated_from_trainer
datasets:
- ml-superb-subset
model-index:
- name: xho-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xho-1
This model is a fine-tuned version of [Akashpb13/Swahili_xlsr](https://huggingface.co/Akashpb13/Swahili_xlsr) on the ml-superb-subset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Sorrowair/metal_35M_adap | Sorrowair | 2024-05-23T03:09:33Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:westlake-repl/SaProt_35M_AF2",
"base_model:adapter:westlake-repl/SaProt_35M_AF2",
"region:us"
] | null | 2024-05-23T03:09:30Z | ---
library_name: peft
base_model: westlake-repl/SaProt_35M_AF2
---
# Model Card for Model ID
This model is used for a demo task<br><br> The digital label means: <br>0: <br>
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
FabioSantos/curso_llama3Finetune_unsloth | FabioSantos | 2024-05-23T03:04:31Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T02:37:19Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** FabioSantos
- **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)
|
seongs/maymust_mistral_v1.1 | seongs | 2024-05-23T03:02:35Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T02:15:06Z | ---
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] |
huqian/dummy-model | huqian | 2024-05-23T03:00:21Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2024-05-23T02:58:51Z | ---
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] |
thesven/Hermes-2-Theta-Llama-3-8B-GPTQ | thesven | 2024-05-23T02:52:23Z | 80 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2024-05-23T02:36:58Z | ---
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.
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h104/dippy_1049 | h104 | 2024-05-23T02:50:44Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T02:49:25Z | ---
library_name: transformers
tags: []
---
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thorirhrafn/GPT1B_domar_RLHF_rouge_e10 | thorirhrafn | 2024-05-23T02:50:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-23T02:50:13Z | ---
library_name: transformers
tags: []
---
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|
cocoirun/longforemr-kobart-summary-v1 | cocoirun | 2024-05-23T02:47:53Z | 152 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-23T02:17:35Z | ---
license: cc-by-nc-nd-4.0
---
Longformer인코더 KoBART로 AIHUB 금융 및 콜 상담 대화 데이터를 CHATGPT를 통해 요약한 학습 데이터를 학습한 모델
```
input = """고객: 안녕하세요, 제가 여기서 사용하는 신용카드에 대해 궁금한 게 있어요.
상담원: 안녕하세요! 네, 어떤 문의가 있으신가요?
고객: 제가 이번 달에 카드를 사용하면서 리워드 포인트를 얼마나 쌓았는지 확인하고 싶어요.
상담원: 네, 당신의 리워드 포인트 잔액을 확인해 드릴 수 있습니다. 제가 당신의 카드 번호를 입력하고 확인해볼게요. 번호를 알려주실 수 있을까요?
고객: 네, 제 카드 번호는 1234-5678-9012-3456입니다.
상담원: 감사합니다. 잠시만 기다려주세요. 확인 중이에요... 네, 현재 당신의 리워드 포인트 잔액은 3,250 포인트입니다.
고객: 알겠어요, 감사합니다! 그럼 추가적인 이용 혜택이나 할인에 관한 정보도 얻을 수 있을까요?
상담원: 물론이죠! 저희 카드사는 다양한 이용 혜택을 제공하고 있습니다. 예를 들어, 여행, 쇼핑, 식사 등 다양한 분야에서 할인 혜택을 받을 수 있거나, 리워드 포인트를 사용하여 상품이나 기프트 카드로 교환할 수 있습니다. 어떤 혜택에 관심이 있으신가요?
고객: 저는 여행 할인이나 마일리지 적립에 관심이 있어요.
상담원: 그런 경우에는 당신에게 적합한 여행 카드 혜택을 제공하는 카드를 추천해 드릴 수 있습니다. 여행 카드는 항공사 마일리지를 쌓을 수 있고, 호텔 할인 혜택을 받을 수도 있습니다. 제가 몇 가지 옵션을 제안해 볼까요?
고객: 네, 그러면 좋을 것 같아요. 감사합니다!
상담원: 말씀해 주셔서 감사합니다. 이제 제가 몇 가지 추천을 드리도록 하겠습니다. 어떤 항공사를 주로 이용하시나요?"""
```
```
output ="""
- 고객이 신용카드에 대해 궁금한 사항 상담
- 리워드 포인트 확인 요청
- 상담원이 카드 번호와 잔액 확인 후 추가 이용 혜택 안내
- 고객이 여행 할인, 마일리지, 호텔 할인 등 다양한 혜택에 관심 표현
"""
```
해당 모델을 활용하기 위해서 다음과 같은 class 필요
```
class LongformerSelfAttentionForBart(nn.Module):
def __init__(self, config, layer_id):
super().__init__()
self.embed_dim = config.d_model
self.longformer_self_attn = LongformerSelfAttention(config, layer_id=layer_id)
self.output = nn.Linear(self.embed_dim, self.embed_dim)
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
is_cross_attention = key_value_states is not None
bsz, tgt_len, embed_dim = hidden_states.size()
# bs x seq_len x seq_len -> bs x seq_len 으로 변경
attention_mask = attention_mask.squeeze(dim=1)
attention_mask = attention_mask[:,0]
is_index_masked = attention_mask < 0
is_index_global_attn = attention_mask > 0
is_global_attn = is_index_global_attn.flatten().any().item()
outputs = self.longformer_self_attn(
hidden_states,
attention_mask=attention_mask,
layer_head_mask=None,
is_index_masked=is_index_masked,
is_index_global_attn=is_index_global_attn,
is_global_attn=is_global_attn,
output_attentions=output_attentions,
)
attn_output = self.output(outputs[0])
return (attn_output,) + outputs[1:] if len(outputs) == 2 else (attn_output, None, None)
```
```
class LongformerEncoderDecoderForConditionalGeneration(BartForConditionalGeneration):
def __init__(self, config):
super().__init__(config)
if config.attention_mode == 'n2':
pass # do nothing, use BertSelfAttention instead
else:
self.model.encoder.embed_positions = BartLearnedPositionalEmbedding(
config.max_encoder_position_embeddings,
config.d_model)
self.model.decoder.embed_positions = BartLearnedPositionalEmbedding(
config.max_decoder_position_embeddings,
config.d_model)
for i, layer in enumerate(self.model.encoder.layers):
layer.self_attn = LongformerSelfAttentionForBart(config, layer_id=i)
```
```
class LongformerEncoderDecoderConfig(BartConfig):
def __init__(self, attention_window: List[int] = None, attention_dilation: List[int] = None,
autoregressive: bool = False, attention_mode: str = 'sliding_chunks',
gradient_checkpointing: bool = False, **kwargs):
"""
Args:
attention_window: list of attention window sizes of length = number of layers.
window size = number of attention locations on each side.
For an affective window size of 512, use `attention_window=[256]*num_layers`
which is 256 on each side.
attention_dilation: list of attention dilation of length = number of layers.
attention dilation of `1` means no dilation.
autoregressive: do autoregressive attention or have attention of both sides
attention_mode: 'n2' for regular n^2 self-attention, 'tvm' for TVM implemenation of Longformer
selfattention, 'sliding_chunks' for another implementation of Longformer selfattention
"""
super().__init__(**kwargs)
self.attention_window = attention_window
self.attention_dilation = attention_dilation
self.autoregressive = autoregressive
self.attention_mode = attention_mode
self.gradient_checkpointing = gradient_checkpointing
assert self.attention_mode in ['tvm', 'sliding_chunks', 'n2']
```
모델 오브젝트 로드 후
weight파일을 별도로 다운받아서 load_state_dict로 웨이트를 불러야 합니다.
```
tokenizer = AutoTokenizer.from_pretrained("cocoirun/longforemr-kobart-summary-v1")
model = LongformerEncoderDecoderForConditionalGeneration.from_pretrained("cocoirun/longforemr-kobart-summary-v1")
device = torch.device('cuda')
model.load_state_dict(torch.load("summary weight.ckpt"))
model.to(device)
```
모델 요약 함수
```
def summarize(text, max_len):
max_seq_len = 4096
context_tokens = ['<s>'] + tokenizer.tokenize(text) + ['</s>']
input_ids = tokenizer.convert_tokens_to_ids(context_tokens)
if len(input_ids) < max_seq_len:
while len(input_ids) < max_seq_len:
input_ids += [tokenizer.pad_token_id]
else:
input_ids = input_ids[:max_seq_len - 1] + [
tokenizer.eos_token_id]
res_ids = model.generate(torch.tensor([input_ids]).to(device),
max_length=max_len,
num_beams=5,
no_repeat_ngram_size = 3,
eos_token_id=tokenizer.eos_token_id,
bad_words_ids=[[tokenizer.unk_token_id]])
res = tokenizer.batch_decode(res_ids.tolist(), skip_special_tokens=True)[0]
res = res.replace("\n\n","\n")
return res
``` |
giantdev/dippy-qOFcv-sn11m4 | giantdev | 2024-05-23T02:41:39Z | 128 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T02:39:47Z | ---
library_name: transformers
tags: []
---
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<!-- 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. -->
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rabbitcat/DataSmith-6b | rabbitcat | 2024-05-23T02:39:39Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-21T09:44:09Z | ---
license: apache-2.0
---
# DataSmith
## Introduction
DataSmith is a large model designed to generate JSON-format data from textual content. The DataSmith-6B version, equipped with 6 billion parameters, is fine-tuned using a comprehensive selection of data sources, including news, encyclopedias, legal documents, medical records, advertising, academic papers, books, novels, and various public announcements. This model serves as the foundation for a series of task-specific adaptations.
## Models Available
- DataSmith-6B
- Hugging Face Model Hub: [DataSmith-6B](https://huggingface.co/rabbitcat/DataSmith-6b)
- github：[DataSmith](https://github.com/element-factory/DataSmith)
## Usage
You can use the model directly or load it with device and dtype settings. The following is an example of generating questions and answers based on text content. You also can use `quick_start_demo.py` to generate question and answer pairs based on text content.
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rabbitcat/DataSmith-6b")
# Load model with device and dtype settings
model = AutoModelForCausalLM.from_pretrained(
"rabbitcat/DataSmith-6b",
device_map="auto",
torch_dtype='auto'
).eval()
# Generate prompt and text
prompt = "读取以下文本材料，并根据材料生成问题，以及问题的答案。问题不应该是开放式的，应该能够通过材料回答。问题的答案应该在材料中表述或暗示。问题应该与材料相关，不应该太具体或太普遍。输出应为json格式。\n"
text = "文本材料：\n北京市地处中国北部、华北平原北部，东与天津市毗连，其余均与河北省相邻，中心位于东经116°20′、北纬39°56′，北京市地势西北高、东南低。西部、北部和东北部三面环山，东南部是一片缓缓向渤海倾斜的平原。境内流经的主要河流有：永定河、潮白河、北运河、拒马河等，北京市的气候为暖温带半湿润半干旱季风气候，夏季高温多雨，冬季寒冷干燥，春、秋短促。"
# Generate messages for the model
messages = [
{
"role": "user",
"content": prompt + text
}
]
# Tokenize and generate response
input_ids = tokenizer.apply_chat_template(
conversation=messages,
tokenize=True,
add_generation_prompt=True,
return_tensors='pt'
)
output_ids = model.generate(
input_ids.to('cuda'),
max_new_tokens=512,
)
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
print(response)
# Model response:
# [
# {"question":"北京市位于中国的哪个方位？", "answer":"北京市地处中国北部、华北平原北部。"},
# {"question":"北京市东临哪个城市？", "answer":"北京市东与天津市毗连。"},
# {"question":"北京市周边与哪个省份相邻？", "answer":"北京市其余均与河北省相邻。"},
# {"question":"北京市的中心位置是怎样的？", "answer":"北京市中心位于东经116°20′、北纬39°56′。"},
# {"question":"北京市地势的总体特征是什么？", "answer":"北京市地势西北高、东南低。"},
# {"question":"北京市西部、北部和东北部三面被什么环绕？", "answer":"北京市西部、北部和东北部三面环山。"},
# {"question":"北京市东南部是什么地形？", "answer":"北京市东南部是一片缓缓向渤海倾斜的平原。"},
# {"question":"北京市境内主要流经哪些河流？", "answer":"北京市境内流经的主要河流有永定河、潮白河、北运河、拒马河等。"},
# {"question":"北京市的气候类型是什么？", "answer":"北京市的气候为暖温带半湿润半干旱季风气候。"},
# {"question":"北京市哪个季节的气温最高？", "answer":"北京市夏季的气温最高。"},
# {"question":"北京市哪个季节降雨量最多？", "answer":"北京市夏季降雨量最多。"},
# {"question":"北京市哪个季节气候最寒冷？", "answer":"北京市冬季气候最寒冷。"},
# {"question":"北京市哪个季节秋高气爽？", "answer":"北京市秋季气候秋高气爽。"},
# {"question":"北京市哪个季节春暖花开？", "answer":"北京市春季气候春暖花开。"},
# {"question":"北京市春季和秋季分别持续多长时间？", "answer":"北京市春、秋短促。"}
# ]
```
## Datasets
We use gpt-4 to generate training corpus by constructing prompt. If you need it, please contact us by email.
## Contributing
Our team has two contributors, and we are looking for more contributors to join us.
You can contribute in several ways:
1. Open an issue
2. Contact us by email
## Contact Us
- Email:
- [email protected]
- [email protected] |
pkarypis/phi_15_cpd_rank500_headdim | pkarypis | 2024-05-23T02:38:56Z | 176 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T02:36:42Z | ---
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]
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### Recommendations
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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
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[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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#### Metrics
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[More Information Needed]
#### Summary
## Model Examination [optional]
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[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]
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nannnzk/llama-loki-002 | nannnzk | 2024-05-23T02:32:59Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T02:26:31Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[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
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#### Speeds, Sizes, Times [optional]
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### 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]
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## Technical Specifications [optional]
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[More Information Needed]
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EthanRhys/Red-WarioWare | EthanRhys | 2024-05-23T02:29:35Z | 0 | 0 | null | [
"license:openrail++",
"region:us"
] | null | 2024-05-23T02:28:46Z | ---
license: openrail++
---
|
MILVLG/Imp-v1.5-3B-196-q4f16_1-MLC | MILVLG | 2024-05-23T02:23:50Z | 0 | 0 | null | [
"text-generation",
"dataset:liuhaotian/LLaVA-Pretrain",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"arxiv:2405.12107",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-05-20T04:20:38Z | ---
license: apache-2.0
pipeline_tag: text-generation
datasets:
- liuhaotian/LLaVA-Pretrain
- liuhaotian/LLaVA-Instruct-150K
---
# 😈 Imp
## Introduction
To fit the [MLC](https://github.com/mlc-ai/mlc-llm) framework for mobile devices, we further perform the 4-bit quantization to [`Imp-v1.5-3B-196`](https://huggingface.co/MILVLG/Imp-v1.5-3B-196-q4f16_1-MLC) to obtain `Imp-v1.5-3B-196-q4f16_1-MLC`.
To use this model on moblie devices, please refer to the [mlc-imp](https://github.com/MILVLG/mlc-imp) project.
## License
This project is licensed under the Apache License 2.0 - see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.
## Citation
If you use our model or refer our work in your studies, please cite:
```bibtex
@article{imp2024,
title={Imp: Highly Capable Large Multimodal Models for Mobile Devices},
author={Shao, Zhenwei and Yu, Zhou and Yu, Jun and Ouyang, Xuecheng and Zheng, Lihao and Gai, Zhenbiao and Wang, Mingyang and Ding, Jiajun},
journal={arXiv preprint arXiv:2405.12107},
year={2024}
}
``` |
sanxialiuzhan/llama3-lora-openIE | sanxialiuzhan | 2024-05-23T02:23:13Z | 1 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | 2024-05-22T15:18:20Z | ---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model-index:
- name: sft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sft
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the duie dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0501
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 24
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 96
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.074 | 0.16 | 500 | 0.0621 |
| 0.0625 | 0.31 | 1000 | 0.0562 |
| 0.0581 | 0.47 | 1500 | 0.0543 |
| 0.0626 | 0.62 | 2000 | 0.0530 |
| 0.0597 | 0.78 | 2500 | 0.0524 |
| 0.0619 | 0.93 | 3000 | 0.0500 |
| 0.0445 | 1.09 | 3500 | 0.0499 |
| 0.0501 | 1.25 | 4000 | 0.0492 |
| 0.0487 | 1.4 | 4500 | 0.0490 |
| 0.0501 | 1.56 | 5000 | 0.0485 |
| 0.0516 | 1.71 | 5500 | 0.0472 |
| 0.0458 | 1.87 | 6000 | 0.0468 |
| 0.0381 | 2.03 | 6500 | 0.0482 |
| 0.037 | 2.18 | 7000 | 0.0506 |
| 0.0387 | 2.34 | 7500 | 0.0501 |
| 0.0363 | 2.49 | 8000 | 0.0498 |
| 0.0321 | 2.65 | 8500 | 0.0500 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
saad17g/finetuned_T5_amzn_v3 | saad17g | 2024-05-23T02:17:18Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-22T18:20:56Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: google-t5/t5-small
model-index:
- name: finetuned_T5_amzn_v3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_T5_amzn_v3
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.41.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.19.1
|
OwOpeepeepoopoo/LittleJerry9 | OwOpeepeepoopoo | 2024-05-23T02:04:57Z | 127 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T02:03:39Z | ---
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]
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- **Shared by [optional]:** [More Information Needed]
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frankmorales2020/Meta-Llama-3-8B-text-to-sql-flash-attention-2 | frankmorales2020 | 2024-05-23T02:01:50Z | 3 | 1 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:adapter:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"region:us"
] | null | 2024-05-22T22:47:55Z | ---
license: llama3
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
datasets:
- generator
model-index:
- name: Meta-Llama-3-8B-text-to-sql-flash-attention-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Meta-Llama-3-8B-text-to-sql-flash-attention-2
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the generator dataset.
## Model description
Innovative Adaptation of Meta-Llama-3–8B for Text-to-SQL: https://medium.com/@frankmorales_91352/innovative-adaptation-of-meta-llama-3-8b-for-text-to-sql-d53544725154
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
hello2mao/Qwen-VL-Chat-4bit-AWQ | hello2mao | 2024-05-23T01:59:49Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"qwen",
"text-generation",
"custom_code",
"autotrain_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | 2024-05-22T09:23:00Z | Qwen/Qwen-VL-Chat 的 AWE算法 的 4bit量化 |
wahid028/llama3-8B-chat-FT-legal-qa | wahid028 | 2024-05-23T01:57:30Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-23T00:19:42Z | ---
library_name: transformers
tags:
- llama-factory
---
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